Array Signal Processing

Blind space-time RAKE receivers for classical DS-CDMA that cancel strong multiuser access interference while optimally combining the desired user's multipath are presented. The delay spread is assumed to be fraction of a symbol interval as in the IS-95 CDMA standard. The post-correlation symbol interval is segmented into that which encompasses the RAKE fingers and that away from the RAKE fingers. The signal-plus-interference and interference-alone space-time correlation matrices are estimated during the former and latter, respectively. Reduced-dimension space-time RAKE receivers are investigated for alleviation of the computational burden of computing the largest generalized eigenvector of the resulting matrix pencil. Theoretical analysis and supporting simulations reveal that if the space and time compressing transformations are designed judiciously, reduced-dimension two-dimensional (2-D) RAKE receivers offer faster convergence at the expense of only a slight loss in asymptotic signal-to-interference-plus-noise ratio (SINR) relative to the full-dimension space-time RAKE receiver. Applications to the IS-95 uplink are also presented along with supporting simulations.

This paper introduces a new closed-form ESPRITbased algorithm for multisource direction finding and polarization estimation with arbitrarily spaced electromagnetic vector-sensors whose three-dimensional (3-D) locations need not be known. The vector-sensor, already commercially available, consists of six colocated but diversely polarized antennas separately measuring all six electromagnetic-field components of an incident wavefield. ESPRIT exploits the nonspatial interrelations among the six unknown electromagnetic-field components of each source and produces from the measured data a set of eigenvalues, from which the source's electromagnetic-field vector may be estimated to within a complex scalar. Application of a vector cross-product operation to this ambiguous electromagnetic-field vector estimate produces an unambiguous estimate of that source's normalized Poynting vector, which contains as its components the source's Cartesian direction cosines. Monte Carlo simulation results verify the efficacy and versatility of this innovative scheme. This novel method maybe considered as a simplification and a refinement over Li's earlier work in 1993 [8].

This paper introduces a novel ESPRIT-based closedform source localization algorithm applicable to arbitrarily spaced three-dimensional arrays of vector hydrophones, whose locations need not be known. Each vector hydrophone consists of two or three identical but orthogonally oriented velocity hydrophones plus one pressure hydrophone, all spatially co-located in a point-like geometry. A velocity hydrophone measures one Cartesian component of the incident sonar wavefield's velocity vector, whereas a pressure hydrophone measures the acoustic wavefield's pressure. Velocity-hydrophone technology is well established in underwater acoustics and a great variety of commercial models have long been available. ESPRIT is realized herein by exploiting the nonspatial interrelations among each vector hydrophone's constituent hydrophones, such that ESPRIT's eigenvalues become independent of array geometry. Simulation results verify the efficacy and versatility of this innovative scheme. Aspects of this underwater acoustic algorithm are analogous to Li's earlier work with diversely polarized antennas.

This letter advances a new polynomial-rooting-based algorithm for multiple-source azimuth-elevation direction-finding and polarization estimation, using dipoles, loops, and/or circularly polarized antennas, which may arbitrarily collocate or orient on an L-shaped uniformly spaced array grid. The number, orientations or types of these antennas may vary from array grid point to array grid point.

This paper introduces a novel eigenstructure-based algorithm uni-vector-sensor ESPRIT that yields closed-form direction-of-arrival (DOA) estimates and polarization estimates using one electromagnetic vector sensor. A vector sensor is composed of six spatially co-located nonisotropic polarizationsensitive antennas, measuring all six electromagnetic field components of the incident wave field. Uni-vector-sensor ESPRIT is based on a matrix-pencil pair of temporally displaced data sets collected from a single electromagnetic vector sensor. The closed-form parameter estimates are obtained through a vector cross-product operation on each decoupled signalsubspace eigenvector of the data correlation matrix. This method exploits the electromagnetic sources' polarization diversity in addition to their spatial diversity, requires no a priori knowledge of signal frequencies, suffers no frequency-DOA ambiguity, pairs automatically the x-axis direction cosines with the y-axis direction cosines, eliminates array interelement calibration, can resolve up to five completely polarized uncorrelated monochromatic sources from near field or far field. It impressively out-performs an array of spatially displaced identically polarized antennas of comparable array-manifold size and computational load.

Blind channel equalization (BCE) has been widely used in underwater communications due to its strong robustness against multipath propagation and its suitability for rapidly varying environments. However, there has been little research on the application of BCE for underwater source localization. On the other hand, conventional matched field processing (MFP), and particularly Riemannian MFP (RMFP), have been regarded as highly effective for this problem. In this paper, based on the statistical characterization of the signal-to-noise ratio (SNR) in underwater acoustic channels, we propose a method for estimating the channel transfer function, which is then used to construct a blind channel equalizer. A source localization approach using the proposed BCE is also presented. The localization performance using BCE is comparable to that of RMFP, achieving a depth error of 10 meters and a range error of 100 meters, while requiring significantly lower computational complexity.

We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. For this we apply the Alternating Conditional Expectation (ACE) algorithm -a powerful technique from nonparametric statistics -to approximately invert the (post-)nonlinear functions. Interestingly, in the framework of the ACE method convergence can be proven and in the PNL BSS scenario the optimal transformation found by ACE will coincide with the desired inverse functions. After the nonlinearities have been removed by ACE, temporal decorrelation (TD) allows us to recover the source signals. An excellent performance underlines the validity of our approach and demonstrates the ACE-TD method on realistic examples. where 4 1 and 4 are independent source signals, that are temporally correlated, © 1 and © are the observed signals, 7 # H §¦ h ) R is the mixing matrix and ¤ 1 and ¤ are the com-

Signal processing in satellite applications is usually performed either on-ground or on-board, i.e. at the gateway station or in the payload. Within the framework of the European Space Agency (ESA) SatNEx III study, a hybrid approach has been considered by splitting the processing between the satellite and the gateway, aiming to strike a better balance between performance and payload complexity. The design of a highcapacity multi-beam system has been carried out, to assess the potential applicability of a hybrid space-ground processing architecture (DIGISAT) for satellite broadband systems; this is achieved via hybrid space-ground beamforming, MIMO and MIMO-MUD, Precoding, as well as Digital Feeder link techniques.

Capon's method is widely used in the literature in array signal processing and spectral analysis. Recently, this method has been extended to the time-frequency analysis. The topic of this paper is to compare the standard spectrogram and its Capon's counterpart, with respect to the several basic parameters: instantaneous frequency estimation, distribution concentration, and resolution of two close components. The analysis is generalized to the distributions from the Cohen class and distributions that take into account phase nonlinearity.

A Matrix Pencil method using a single snapshot is used to compute the Direction of arrival by measuring the incident angles down from the perpendicular direction along an antenna array. The Multiple Signal Classification (MUSIC) algorithm is used as a benchmark. The correlation coefficient of the signals is computed by varying the phase of the source signals at which they impinge on the Uniform Linear Array and from which a measure is obtained to distinguish between weakly and highly correlated signals. Results from computer simulations show that the Root Mean Square Error (RMSE) of Matrix Pencil (MP) and MUSIC method decreases with increasing Signal to Noise Ratio (SNR) and angle separation of source signals. The MP method provides more advantages over MUSIC algorithm at low Signal to Noise Ratio (SNR).The variance of the Matrix Pencil method estimate approaches the Cramér–Rao bound (CRB) indicating that MP method outperforms MUSIC algorithm during low SNR. In the investigations th...

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city. Index Terms-Multibaseline (MB) polarimetric synthetic aperture radar (SAR) interferometry (POL-InSAR), multichannel SAR, polarimetric array signal processing techniques, 3-D polarimetric SAR tomography, 3-D SAR urban remote sensing. I. INTRODUCTION T HE ANALYSIS of synthetic aperture radar (SAR) data of urban areas (see [1]-[11] and the references therein) is a very difficult task due to three main reasons. 1) Radar images are geometrically distorted by layover and shadow effects. The layover yields a superposition of different reflection components. Manuscript

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city. Index Terms-Multibaseline (MB) polarimetric synthetic aperture radar (SAR) interferometry (POL-InSAR), multichannel SAR, polarimetric array signal processing techniques, 3-D polarimetric SAR tomography, 3-D SAR urban remote sensing. I. INTRODUCTION T HE ANALYSIS of synthetic aperture radar (SAR) data of urban areas (see [1]-[11] and the references therein) is a very difficult task due to three main reasons. 1) Radar images are geometrically distorted by layover and shadow effects. The layover yields a superposition of different reflection components. Manuscript

3D MIMO introduces the vertical dimension of the antenna downtilt angle to make the direction of signal transmission more accurate to improve system capacity. In this paper, we verify the effect of antenna downtilt angle on channel capacity through simulations of four fixed antenna downtilt angles, 90, 96, 99, and 102 degrees under the conditions that the distance between mobile station (MS) and base station (BS) is 250 m, and the heights of antenna in BS and MS are 25 m and 1.5 m, respectively. The simulation results show that the antenna downtilt angle of 96 degrees has a larger channel capacity than the others. In addition, we proposed an adaptive optimization method by applying the Q-learning algorithm to adaptively optimize the antenna downtilt angles to maximize system capacity. The performance of the proposed method is to investigate the Q-learning algorithm with three different discount rates at 0.9, 0.5, and 0.1, and four different propagation distances on 20 × 1 and 60 × 4 MIMO. We demonstrate that there is only a 1% difference between the adaptively optimized antenna downtilt angle and the ideal optimal antenna downtilt angle when the discount rate of Q-learning algorithm is 0.9, and its channel capacity performance can reach more than 99.72% of the ideal optimal one.

Direction of Arrival (DOA) estimation is one of the major tasks in array signal processing. In this paper, a new DOA estimation method is proposed using a rotational uniform linear array (RULA) consisting of omnidirectional sensors. The main contribution of the proposed method is that the number of distinguishable signals is larger than the methods in the literature with a uniform linear array consisting of the same number of omnidirectional sensors. Moreover, the new method can effectively reduce unknown spatial noises using a generalized complement projection matrix under the RULA framework. Simulations are presented to illustrate the effectiveness of the proposed method and comparison with some existing DOA estimation methods is also made.

The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector. The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a differential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the efficacy of the proposed algorithm.

This paper reviews recent progress toward understanding the dynamics of the middle atmosphere in the framework of the Atmospheric Dynamics Research InfraStructure in Europe (ARISE) initiative. The middle atmosphere, integrating the stratosphere and mesosphere, is a crucial region which influences tropospheric weather and climate.

ARISE proposes to design a new infrastructure that integrates different station networks in order to provide a new "3D" image of the atmospheric dynamics from the ground up to the mesosphere with unprecedented spatio-temporal resolution. The implied networks are: - the International infrasound network developed for the verification of the Comprehensive nuclear Test Ban Treaty (CTBT). This system is unique by its quality for infrasound and atmospheric wave observations, - the Network for the Detection of Atmospheric Composition Changes (NDACC) which uses Lidar to measure stratospheric dynamics, - the Network for the Detection of Mesopause Changes (NDMC), dedicated to airglow layer measurements in the mesosphere, and additional complementary stations and satellite data. The infrastructure extends across Europe and outlying regions, including polar and equatorial regions. The measurements will be used to improve the parameterization of gravity waves in the stratosphere to bet...

The search for life in the universe is a major theme of astronomy and astrophysics for the next decade. Searches for technosignatures are complementary to searches for biosignatures, in that they offer an alternative path to discovery, and address the question of whether complex (i.e. technological) life exists elsewhere in the Galaxy. This approach has been endorsed in prior Decadal Reviews and National Academies reports, and yet the field still receives almost no federal support in the US. Because of this lack of support, searches for technosignatures, precisely the part of the search of greatest public interest, suffers from a very small pool of trained practitioners. A major source of this issue is institutional inertia at NASA, which avoids the topic as a result of decades-past political grandstanding, conflation of the effort with non-scientific topics such as UFOs, and confusion regarding the scope of the term "SETI." The Astro2020 Decadal should address this issue ...

We propose a new broadband beamformer design technique which produces an optimal receiver beam pattern for any set of field measurements in space and time. The modal subspace decomposition (MSD) technique is based on projecting a desired pattern into the subspace of patterns achievable by a particular set of space-time sampling positions. This projection is the optimal achievable pattern in the sense that it minimizes the mean-squared error (MSE) between the desired and actual patterns. The main advantage of the technique is versatility as it can be applied to both sparse and dense arrays, nonuniform and asynchronous time sampling, and dynamic arrays where sensors can move throughout space. It can also be applied to any beam pattern type, including frequency-invariant and spot pattern designs. A simple extension to the technique is presented for oversampled arrays, which allows high-resolution beamforming whilst carefully controlling input energy and error sensitivity.

Independent Component Analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie set of random variables, measurements, or signals. ICA is a general purpose technique which is used to linearly transform the observed random data into components. The ICA can be estimated by using the concept of maximum nonGaussianity, maximum likelihood estimation, or minimisation of mutual information. This paper applies ICA to seismic acceleration time histories in order to locate any hidden components of ground rotational motion or tilts. Normally the three components of seismically induced rotations are not recorded in most of the available seismic instruments, primarily because previous devices did not provide the required sensitivity to observe rotations in a wide frequency band and distance range (the two horizontal components, equal to tilt at the free surface, are generally recorded at low frequencies) Igel et al 2003. From the x, y and z components usually recorded the Extended Generalised Lambda Distributions (EGLD) -ICA model was used to examine whether rotational or tilt trends were embedded within the 3 components. The algorithm tries to fit a matrix from the data which will separate any other trends within the available components. The results show that the EGLDICA separates trends within the 3 components; however these are not yet identified as tilts or rotations.

Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate, nonvanishing constant minimum determinant, uniform average transmitted energy per antenna, and good shaping. However, the high decoding complexity is a critical issue for practice. When the Channel State Information (CSI) is available at both the transmitter and the receiver, Singular Value Decomposition (SVD) is commonly applied for a Multiple-Input Multiple-Output (MIMO) system to enhance the throughput or the performance. In this paper, two novel techniques, Perfect Coded Multiple Beamforming (PCMB) and Bit-Interleaved Coded Multiple Beamforming with Perfect Coding (BICMB-PC), are proposed, employing both PSTBCs and SVD with and without channel coding, respectively. With CSI at the transmitter (CSIT), the decoding complexity of PCMB is substantially reduced compared to a MIMO system employing PSTBC, providing a new prospect of CSIT. Especially, because of the special property of the generation matrices, PCMB provides much lower decoding complexity than the state-of-the-art SVD-based uncoded technique in dimensions 2 and 4. Similarly, the decoding complexity of BICMB-PC is much lower than the state-of-theart SVD-based coded technique in these two dimensions, and the complexity gain is greater than the uncoded case. Moreover, these aforementioned complexity reductions are achieved with only negligible or modest loss in performance.

Non-emotional learning is introduced for machine learning. This is achieved by using a non-emotional objective function in the place of quadratic (emotional) objective function that is commonly used in practice. The Adaline which is widely used in neural nets is employed in the derivation of the non-emotional learning. The non-emotional learning is then applied to multilayer neural nets. This is achieved in two steps. First, the regular backpropagation that minimizes the overall quadratic innovation function is used to obtain the backpropagation innovation at the output of the Adaline of each neuron in the net. Second, based on the backpropagation innovation of the individual Adalines, the non-emotional learning is applied to update the weight vector of each neuron in the net. Then, the non-emotional learning is applied to train a multiplayer neural net of an exclusive-OR gate. The learning curves of the non-emotional learning is compared with the emotional learning used in practice. It is shown that the non-emotional learning is extremely faster than the emotional learning as applied to multilayer neural nets.

We have developed a signal-processing method for array data recorded at densely and widely distributed tiltmeter stations in Japan and analyzed the data recorded during the last 5 years. Filtered seismograms were analyzed to detect coherent seismic waves and estimate the epicenters. In order to identify very-low frequency earthquakes (VLFEs) with only low-frequency energies only, we selected only events not listed in the earthquake catalogue provided by the Japan Meteorological Agency from all of the detected events. The epicenters of the obtained VLFEs were estimated to be distributed not only along the Nankai Trough, where the events occurred on the reverse faults in the well-developed accretionary prism, but also in Tokachi-oki without accretion. This result suggests that even in such a region, VLFEs can occur on a shallower portion of the plate boundary than the seismogenic region of ordinary earthquakes and/or in a deformed area of the overriding plate along the subduction zone...

This paper shows how digital predistortion of a phased array can benefit from the parametric variations over parallel power amplifiers (PAs). Different antenna configurations are simulated by varying the PA input drive levels by the Monte-Carlo method. The error vector magnitude (EVM) at the steering angle and total radiated adjacent channel power ratio (TRACPR) are used as performance metrics. The simulation results indicate that array predistortion can benefit from the variations between the PAs to improve the EVM significantly. However, at the same time, the TRACPR performance is reduced. This gives a new trade-off to balance between in-band and outoff-band distortion in the fifth generation beamforming systems.

As GPS continues to become a more widely used tool everyday , many are looking for unique ways to improve the technology. However, GPS positioning in many conditions is not reliable due to the non-visibility of sufficient number of satellites. The GPS like signal transmitted by Pseudolites (PLs) can be employed to augment and provide continuity, accuracy and availability in such environment. One of the major problems associated with such navigation system is the " Near-Far " problem, which occurs due to the interference of strong PL signal and weak navigation satellite signals over certain range. Among various methods of mitigating the near-far problem, pulsing of pseudolite signal for fractions of a full duty cycle has been observed to be more effective. However, the pulsed signals affect GPS receivers depending on its characteristics such as power and duty cycle. This paper is focused on the impact of pulsed interference on GPS receiver equipment. A simple pulse blanking...

This paper proposes a statistical-eigenmode space-division multiple-access (SE-SDMA) transmission approach for a two-user downlink system where two transmit antennas are deployed at the base station and each mobile user has only one receive antenna, assuming that only statistical channel state information (SCSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio (SLNR), the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and then transmits to each user along the corresponding eigenmode. For this approach, we derive an exact expression of the achievable ergodic rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. We show that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gains. For comparison with ZFBF, we show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter (CSIT) tends to zero in highly correlated channels. Further, when ZFBF is implemented with inaccurate CSIT, the operating regions of SE-SDMA and ZFBF are investigated for different ranges of signal-to-noise ratio (SNR), instantaneous CSIT accuracy levels, and spatial correlation levels. Numerical results confirm the analytically derived conclusions, and physical insights are provided.

This article studies the transmission of a single cell-edge user's signal using statistical channel state information at cooperative base stations (BSs) with a general jointly correlated multiple-input multiple-output (MIMO) channel model. We first present an optimal scheme to maximize the ergodic sum capacity with per-BS power constraints, revealing that the transmitted signals of all BSs are mutually independent and the optimum transmit directions for each BS align with the eigenvectors of the BS's own transmit correlation matrix of the channel. Then, we employ matrix permanents to derive a closed-form tight upper bound for the ergodic sum capacity. Based on these results, we develop a low-complexity power allocation solution using convex optimization techniques and a simple iterative water-filling algorithm (IWFA) for power allocation. Finally, we derive a necessary and sufficient condition for which a beamforming approach achieves capacity for all BSs. Simulation results...

Significant advances in the development of millimeter-wave and terahertz (30-10 000 GHz) technologies have been made to cope with the increasing interest in this still not fully explored electromagnetic spectrum. The nature of electromagnetic waves over this frequency range is well suited for the development of high-resolution imaging applications, molecular-sensitive spectroscopic devices, and ultrabroadband wireless communications. In this paper, millimeter-wave and terahertz antenna technologies are overviewed including the conventional and nonconventional planar/nonplanar antenna structures based on different platforms. As a promising technological platform, substrate-integrated circuits (SICs) attract more and more attention. Various substrate-integrated waveguide (SIW) schemes and other synthesized guide techniques have been widely employed in the design of antennas and arrays. Different types of substrate-integrated antennas and beamforming networks are discussed with respect to theoretical and experimental results in connection with electrical and mechanical performances.

Among the mobile ad hoc networks appealing characteristics there are network reconfigurability and flexibility. In this context a smart antenna capable of self-configuring multiple high-directivity beams provides a major advantage in terms of power saving, increased range, and spatial reuse of channels. In this paper a smart antenna made of a cylindrical array of patches suitable for MANETs is presented.

To increase reliability and safety, industrial plant equipment such as compressors, electric motors, gear trains, and so forth are regularly monitored for damage. A traditional approach for monitoring is for a trained technician to make vibration measurements of the equipment. Inspection of the vibration measurements and their comparison to known healthy/damaged data sets allow for assessing the health status of the machines. This process is repeated at regular intervals. However, it is time-consuming and labor-intensive. It would be greatly convenient both in terms of time and cost to develop a remote acoustic based system to detect the health status of industrial equipment. A microphone phased array machine health monitoring system is proposed to remotely identify and classify machine faults. Acoustic spectral signature analysis has existed for many years, and it is similar to vibration analysis using accelerometers with the advantage that nothing must be mounted on the machine. In addition, acoustic imaging systems using microphone phased arrays have also been in existence for many years. The current state-of-the-art, however, requires an expert human operator to interpret the data from these devices, and therefore it is not suitable for automated, online monitoring. The implementation of a microphone phased array approach to monitor equipment faults in a typical industrial environment presents some unique challenges. A typical industrial plant is a highly acoustic reverberant environment that will result in significant reflections and background noise levels. To be effective, the array will need to be able to monitor multiple machines through the plant, simultaneously. To investigate the potential of the proposed approach, a numerical model of the microphone phased array in a large highly reverberant room was developed. The model was then used to investigate several array designs, study the effect of reflections and reverberation, determine the capability of the system to monitor multiple machines and so forth. These numerical studies revealed that the critical concern is that the array signal to noise ratio must be larger than the noise difference between the loudest and quietest machines being monitored. It was also found that the reverberation of typical industrial plants is not important if a sufficient number of microphones is used in the array, e.g. for a plant with an average absorption coefficient of 10% a minimum of 100 microphones is required in the array.

The application of deep learning for adaptive beamforming is a necessary and disruptive advancement in wireless communication technology. It has the potential to satisfy the continuous need for data traffic in a highly dense network created by the vehicle to infrastructure (V2I) scenario. Due to excessive usage of data in V2I scenario interference is a pertinent problem. It is essential to adaptively predict and nullify the interference for V2I scenarios. Practical V2I network implementation is limited because of the inevitability of interference due to the random nature of the wireless channel. This paper proposes an adaptive beamforming (ABF) technique for mitigation of interference in V2I networks, especially in a multiuser environment. In this work, LSTM based deep learning and Non-Linear Auto Regressive (NAR) based regressor have been employed to predict the angles between the RSU's and UE. Simulation results have confirmed that the proposed LSTM model achieves comparable performance in terms of system throughput when compared with the non-linear auto regressive (NAR) method implemented as an artificial neural network (ANN).

In this paper the algorithm of signal detection by means passive acoustics and digital data processing is proposed. This algorithm is original and contains the processing of signals, which are received by several acoustic aerials. For signal detection the calculation of wide-band ambiguity function together with its projection on time axis are used. All the calculations are executed with the real signals. As the result the location of the aircraft on Cartesian coordinate plane is observed. The suggested algorithm is also proposed to use for measuring of aircraft noise with the aim of environmental protection.

A closed-form multi-dimensional multi-invariance generalization of the ESPRIT algorithm is introduced to exploit the entire invariance structure underlying a (possibly) multiparametric data model, thereby greatly improving estimation performance. The multiple-invariance data structure that this proposed method can handle includes: (1) multiple occurrence of one size of invariance along one or multiple parametric dimensions, (2) multiple sizes of invariances along one or multiple parametric dimensions, and (3) invariances that cross over two or more parametric dimensions. The basic (uni-dimensional uni-invariance) ESPRIT algorithm is applied in parallel to each m ultiple pair of matrixpencils characterizing the multiple invariance relationships in the data model, producing multiple sets of cyclically ambiguous estimates over the multi-dimensional parameter space. A weighted least-squares hyper-plane is then tted to these set of estimates to yield very accurate and unambiguous estimates of the signal parameters.

In this paper, an investigation of the potential of rapid refractivity retrieval is presented. The retrieval technique utilizes radar phase measurements of ground clutter to derive near-surface refractivity, which has been commonly used as a proxy for humidity, given its close relation to vapor pressure. Surface humidity is an important meteorological parameter and has been known to play an important role in convective initiation. In this paper, the refractivity retrieval technique is exploited by using smaller numbers of samples for phase calculation, which is a fundamental process in refractivity retrieval. The impetus for this paper is to explore the possibility of rapid refractivity retrieval by exploiting the rapid beam-steering capability of a phased-array radar. Using the National Weather Radar Testbed in Norman, OK, a 64-pulse per radial raw-data set was collected for conventional refractivity processing. Then, subsets of the 64 samples were extracted to emulate shorter dwell periods and the corresponding more rapid experiments. The test cases that were considered are 2, 4, 8, 16, and 32 samples. Refractivity fields retrieved using smaller numbers of samples are compared against the reference field, which was obtained using the entire 64-sample data set. It will be shown that, statistically, significant refractivity fields can be obtained from as short as a two-sample dwell.

In this paper, a new algorithm for robust adaptive beamforming is developed. The basic idea of the proposed algorithm is to estimate the difference between the actual and presumed steering vectors and to use this difference to correct the erroneous presumed steering vector. The estimation process is performed iteratively where a quadratic convex optimization problem is solved at each iteration. Unlike other robust beamforming techniques, our algorithm does not assume that the norm of the mismatch vector is upper bounded, and hence it does not suffer from the negative effects of over/under estimation of the upper bound. Simulation results show the effectiveness of the proposed algorithm.

This article presents a low-complexity parametric algorithm for the estimation of the carrier frequency offset (CFO) in orthogonal frequency division multiplexing systems. The proposed algorithm is equivalent to the Unitary ESPRIT algorithm, originally devised for estimating the directionof-arrival of a wavefront, now applied to the CFO scenario. It is verified that the proposed algorithm reduces the overall computational complexity in around 40%, while sustaining the error-rate performance achieved by the standard ESPRIT algorithm.

Proposed herein is a pre-processing technique applicable to maximum-SINR beamformers for "blind" spacetime rake receivers in "single-user"-type DS-CDMA detection. This smart-antennas signal-processing technique enhances the constructive summation of multipaths and the rejection of cochannel interference, both of which decrease the bit-error-rate and lower the error-floor of the cellular uplink's near-far problem at high SNR. This technique is "blind" in that it needs neither prior knowledge nor explicit estimation of (1) the channelfaded multipaths' arrival angles, relative arrival delays or power profiles, (2) the receiving antenna array's nominal or actual gain-phase-polarization response, and (3) the co-channel multiaccess users' signature-spreading codes. Monte Carlo simulations show that the proposed scheme can enhance the output-SINR by 1 -10 dB. A mathematical analysis shows this proposed pre-processing scheme to always improve the asymptotic output-SINR. A formula is analytically derived to predict this asymptotic output-SINR. This formula lies within 1dB of limited simulation results at an input-SIR above -7dB and a chip-rate space timesampling, but the formula is less accurate otherwise.

This novel underwater acoustic azimuth-elevation source localization scheme realizes the eigenstructure-based polynomial rooting procedure for an L-shaped uniformly spaced array of diversely oriented and possibly spatially co-located velocity hydrophones and an optional pressure hydrophone. A velocity hydrophone measures a Cartesian component of the acoustic particle velocity vector of the incident wavefield. At each uniformly spaced array grid, one or more co-located and diversely oriented velocity hydrophones and/or a pressure hydrophone are placed, with the number and orientations of velocity hydrophones possibly varying from grid position to grid position in some known prearranged manner. The diverse orientation of the velocity hydrophones, however, disrupts the Vandermonde array manifold structure in each of the two uniform-linear-array legs of the L-shaped array. Nonetheless, ingenuous mathematical manipulations proposed in this paper restore the disrupted Vandermonde algebraic structure, thereby permitting once again the use of polynomial rooting to estimate the directions of arrival. A proposed pairing procedure matches each source's x x x-axis direction cosine estimate with its corresponding y y y-axis direction cosine estimate. Simulation results verify the efficacy of the proposed scheme.

This paper introduces a new closed-form ESPRITbased algorithm for multisource direction finding and polarization estimation with arbitrarily spaced electromagnetic vector-sensors whose three-dimensional (3-D) locations need not be known. The vector-sensor, already commercially available, consists of six colocated but diversely polarized antennas separately measuring all six electromagnetic-field components of an incident wavefield. ESPRIT exploits the nonspatial interrelations among the six unknown electromagnetic-field components of each source and produces from the measured data a set of eigenvalues, from which the source's electromagnetic-field vector may be estimated to within a complex scalar. Application of a vector cross-product operation to this ambiguous electromagnetic-field vector estimate produces an unambiguous estimate of that source's normalized Poynting vector, which contains as its components the source's Cartesian direction cosines. Monte Carlo simulation results verify the efficacy and versatility of this innovative scheme. This novel method maybe considered as a simplification and a refinement over Li's earlier work in 1993 [8].

This paper introduces a novel ESPRIT-based closedform source localization algorithm applicable to arbitrarily spaced three-dimensional arrays of vector hydrophones, whose locations need not be known. Each vector hydrophone consists of two or three identical but orthogonally oriented velocity hydrophones plus one pressure hydrophone, all spatially co-located in a point-like geometry. A velocity hydrophone measures one Cartesian component of the incident sonar wavefield's velocity vector, whereas a pressure hydrophone measures the acoustic wavefield's pressure. Velocity-hydrophone technology is well established in underwater acoustics and a great variety of commercial models have long been available. ESPRIT is realized herein by exploiting the nonspatial interrelations among each vector hydrophone's constituent hydrophones, such that ESPRIT's eigenvalues become independent of array geometry. Simulation results verify the efficacy and versatility of this innovative scheme. Aspects of this underwater acoustic algorithm are analogous to Li's earlier work with diversely polarized antennas.

This work derives new non-asymptotic and asymptotic CramCr-Rao lower bounds (CRB) for the estimation of multiple puretone incident signals' amplitudes, azimuthelevation arrival-angles, polarization parameters, and frequencies, based on data observed from one dipole triad (composed of three spatially collocated but orthogonally oriented electrically short dipoles) or one looptriad (composed of three spatially collocated but orthogonally oriented magnetically small loops). The incident wurces are puretones at distinct frequencies, in contrast to the existing electromagnetic antenna-array signalestimation CRB literature's modeling of all sources to be at the same carrier-frequency.

This work derives new non-asymptotic and asymptotic CramCr-Rao lower bounds (CRB) for the estimation of multiple puretone incident signals' amplitudes, azimuthelevation arrival-angles, polarization parameters, and frequencies, based on data observed from one dipole triad (composed of three spatially collocated but orthogonally oriented electrically short dipoles) or one looptriad (composed of three spatially collocated but orthogonally oriented magnetically small loops). The incident wurces are puretones at distinct frequencies, in contrast to the existing electromagnetic antenna-array signalestimation CRB literature's modeling of all sources to be at the same carrier-frequency.

Proposed herein is a pre-processing technique applicable to maximum-SINR beamformers for "blind" spacetime rake receivers in "single-user"-type DS-CDMA detection. This smart-antennas signal-processing technique enhances the constructive summation of multipaths and the rejection of cochannel interference, both of which decrease the bit-error-rate and lower the error-floor of the cellular uplink's near-far problem at high SNR. This technique is "blind" in that it needs neither prior knowledge nor explicit estimation of (1) the channelfaded multipaths' arrival angles, relative arrival delays or power profiles, (2) the receiving antenna array's nominal or actual gain-phase-polarization response, and (3) the co-channel multiaccess users' signature-spreading codes. Monte Carlo simulations show that the proposed scheme can enhance the output-SINR by 1 -10 dB. A mathematical analysis shows this proposed pre-processing scheme to always improve the asymptotic output-SINR. A formula is analytically derived to predict this asymptotic output-SINR. This formula lies within 1dB of limited simulation results at an input-SIR above -7dB and a chip-rate space timesampling, but the formula is less accurate otherwise.

This novel underwater acoustic azimuth-elevation source localization scheme realizes the eigenstructure-based polynomial rooting procedure for an L-shaped uniformly spaced array of diversely oriented and possibly spatially co-located velocity hydrophones and an optional pressure hydrophone. A velocity hydrophone measures a Cartesian component of the acoustic particle velocity vector of the incident wavefield. At each uniformly spaced array grid, one or more co-located and diversely oriented velocity hydrophones and/or a pressure hydrophone are placed, with the number and orientations of velocity hydrophones possibly varying from grid position to grid position in some known prearranged manner. The diverse orientation of the velocity hydrophones, however, disrupts the Vandermonde array manifold structure in each of the two uniform-linear-array legs of the L-shaped array. Nonetheless, ingenuous mathematical manipulations proposed in this paper restore the disrupted Vandermonde algebraic structure, thereby permitting once again the use of polynomial rooting to estimate the directions of arrival. A proposed pairing procedure matches each source's x x x-axis direction cosine estimate with its corresponding y y y-axis direction cosine estimate. Simulation results verify the efficacy of the proposed scheme.

This paper introduces a new closed-form ESPRITbased algorithm for multisource direction finding and polarization estimation with arbitrarily spaced electromagnetic vector-sensors whose three-dimensional (3-D) locations need not be known. The vector-sensor, already commercially available, consists of six colocated but diversely polarized antennas separately measuring all six electromagnetic-field components of an incident wavefield. ESPRIT exploits the nonspatial interrelations among the six unknown electromagnetic-field components of each source and produces from the measured data a set of eigenvalues, from which the source's electromagnetic-field vector may be estimated to within a complex scalar. Application of a vector cross-product operation to this ambiguous electromagnetic-field vector estimate produces an unambiguous estimate of that source's normalized Poynting vector, which contains as its components the source's Cartesian direction cosines. Monte Carlo simulation results verify the efficacy and versatility of this innovative scheme. This novel method maybe considered as a simplification and a refinement over Li's earlier work in 1993 [8].

This paper introduces a novel eigenstructure-based algorithm uni-vector-sensor ESPRIT that yields closed-form direction-of-arrival (DOA) estimates and polarization estimates using one electromagnetic vector sensor. A vector sensor is composed of six spatially co-located nonisotropic polarizationsensitive antennas, measuring all six electromagnetic field components of the incident wave field. Uni-vector-sensor ESPRIT is based on a matrix-pencil pair of temporally displaced data sets collected from a single electromagnetic vector sensor. The closed-form parameter estimates are obtained through a vector cross-product operation on each decoupled signalsubspace eigenvector of the data correlation matrix. This method exploits the electromagnetic sources' polarization diversity in addition to their spatial diversity, requires no a priori knowledge of signal frequencies, suffers no frequency-DOA ambiguity, pairs automatically the x-axis direction cosines with the y-axis direction cosines, eliminates array interelement calibration, can resolve up to five completely polarized uncorrelated monochromatic sources from near field or far field. It impressively out-performs an array of spatially displaced identically polarized antennas of comparable array-manifold size and computational load.

This paper introduces a novel ESPRIT-based closedform source localization algorithm applicable to arbitrarily spaced three-dimensional arrays of vector hydrophones, whose locations need not be known. Each vector hydrophone consists of two or three identical but orthogonally oriented velocity hydrophones plus one pressure hydrophone, all spatially co-located in a point-like geometry. A velocity hydrophone measures one Cartesian component of the incident sonar wavefield's velocity vector, whereas a pressure hydrophone measures the acoustic wavefield's pressure. Velocity-hydrophone technology is well established in underwater acoustics and a great variety of commercial models have long been available. ESPRIT is realized herein by exploiting the nonspatial interrelations among each vector hydrophone's constituent hydrophones, such that ESPRIT's eigenvalues become independent of array geometry. Simulation results verify the efficacy and versatility of this innovative scheme. Aspects of this underwater acoustic algorithm are analogous to Li's earlier work with diversely polarized antennas.

Aperture extension is achieved in this novel ESPRITbased two-dimensional angle estimation scheme using a uniform rectangular array of vector hydrophones spaced much farther apart than a half-wavelength. A vector hydrophone comprises two or three spatially co-located, orthogonally oriented identical velocity hydrophones (each of which measures one Cartesian component of the underwater acoustical particle velocity vector-field) plus an optional pressure hydrophone. Each incident source's directions-of-arrival are determined from the source's acoustical particle velocity components, which are extracted by decoupling the data covariance matrix's signal-subspace eigenvectors using the lower dimensional eigenvectors obtainable by ESPRIT. These direction-cosine estimates are unambiguous but have high variance; they are used as coarse references to disambiguate the cyclic phase ambiguities in ESPRIT's eigenvalues when the intervector-hydrophone spacing exceeds a halfwavelength. In one simulation scenario, the estimation standard deviation decreases with increasing intervector-hydrophone spacing up to 12 wavelengths, effecting a 97% reduction in the estimation standard deviation relative to the half-wavelength case. This proposed scheme and the attendant vector-hydrophone array outperform a uniform half-wavelength spaced pressurehydrophone array with the same aperture and slightly greater number of component hydrophones by an order of magnitude in estimation standard deviation. Other simulations demonstrate how this proposed method improves underwater acoustic communications link performance. The virtual array interpolation technique would allow this proposed algorithm to be used with irregular array geometries.