Array Signal Processing Algorithms Research Articles

Design of Novel Photodetector Array in Optical Communication Systems and Optimization of Signal Processing Algorithm

This study aims to enhance the high-speed and high-power performance of photodetectors, focusing on the epitaxial layer structure of photodetector arrays. The epitaxial structure of the photodetector involves a P–I–N structure with InAlAs/InGaAs heterostructures. InAlAs is used as the P-region material, and a dual-waveguide photodetector array is designed with a core diameter of 20 μm. The optical coupling is achieved through normal incident vertical illumination. Additionally, a novel structure of a dual-core symmetrically connected photodetector array is proposed, altering the electrode structure and core arrangement to maintain consistent distances for the output signals from the cores to the device’s output terminal. In order to address the issue of minimum bit error rate in optical communication systems under conditions of optical noise and limited energy efficiency of received optical signals, a photodetector array signal processing algorithm is proposed. The experimentally designed dual-core symmetrically connected P–I–N photodetector array achieves a 3 dB bandwidth of 15.37 GHz at an average photocurrent of 5 mA. Furthermore, it maintains a 3 dB bandwidth of 11.67 GHz at a high-power operating state of 50 mA, with an alternating current (AC) saturation current of 56 mA and a saturated RF output power of 1.11 dBm at 11.67 GHz. In comparison, the improved dual-core traveling-wave photodetector array at 11.67 GHz only achieves an AC saturation current of 38 mA and a saturated RF output power of 2.66 dBm. The signal processing algorithm for the selected dual-core symmetrically connected P–I–N photodetector array demonstrates that employing a method of merging array elements reduces the system’s computational complexity with minimal impact on the system’s power consumption.

Research on a Non-Contact Multi-Electrode Voltage Sensor and Signal Processing Algorithm.

Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object or the ground, which can avoid the above problems. However, most of the existing flat-plate structure voltage measurement sensors are not only expensive to manufacture, but also bulky, and when the relative position between the wire under test and the sensor changes, it will bring great measurement errors, making it difficult to meet actual needs. Aiming to address the above problems, this paper proposes a multi-electrode array structure non-contact voltage sensor and signal processing algorithm. The sensor is manufactured by the PCB process, which effectively reduces the manufacturing cost and process difficulty. The experimental and simulation results show that, when the relative position of the wire and the sensor is offset by 10 mm in the 45° direction, the relative error of the traditional single-electrode voltage sensor is 17.62%, while the relative error of the multi-electrode voltage sensor designed in this paper is only 0.38%. In addition, the ratio error of the sensor under the condition of power frequency of 50 Hz is less than ±1% and the phase difference is less than 4°. The experimental results show that the sensor has good accuracy and linearity.

Frequency-difference autoproduct cross-term analysis and cancellation for improved ambiguity surface robustness.

Frequency-differencing, or autoproduct processing, techniques are one area of research that has been found to increase the robustness of acoustic array signal processing algorithms to environmental uncertainty. Previous studies have shown that frequency differencing techniques are able to mitigate problems associated with environmental mismatch in source localization techniques. While this method has demonstrated increased robustness compared to conventional methods, many of the metrics, such as ambiguity surface peak values and dynamic range, are lower than would typically be expected for the observed level of robustness. These previous studies have suggested that such metrics are reduced by the inherent nonlinearity of the frequency-differencing method. In this study, simulations of simple multi-path environments are used to analyze this nonlinearity and signal processing techniques are proposed to mitigate the effects of this problem. These methods are used to improve source localization metrics, particularly ambiguity surface peak value and dynamic range, in two experimental environments: a small laboratory water tank and in a deep ocean (Philippine Sea) environment. The performance of these techniques demonstrates that many source localization metrics can be improved for frequency-differencing methods, which suggests that frequency-differencing methods may be as robust as previous studies have shown.

An intelligent algorithm for coherent sound source localization based on a strong tracking filter

Background noise is inevitable when sensor arrays are used for aeroacoustic measurements in wind tunnels. The direct removal of background noise, however, would affect the measurement accuracy. In particular, the existing array signal processing algorithms are either invalid or inefficient for removing the noise that is coherent with the signal of interest. In this paper, an intelligent algorithm is developed to localize the coherent sound sources and background noise in real time by iteratively checking the collected data from the array. The proposed method can automatically adjust the suboptimal fading factor to extract useful information as much as possible in residual sequence. This algorithm is tested in simulations and then demonstrated in an experiment. Compared to the two existing methods, the results indicate that the new method has a good phase-shift tracking ability and rapid estimation-error convergence speed, and can achieve an acceptable performance even for low-cost acoustic sensors. Overall, the proposed method should assist array beamforming and hence benefit aeroacoustic measurement.

White noise reduction for wideband linear array signal processing

The performance of wideband array signal processing algorithms is dependant on the noise level in the system. In this thesis, a method is proposed for reducing the level of white noise in wideband arrays via a judiciously designed spatial transformation followed by a bank of high-pass filters. The method is initially introduced for uniform linear arrays (ULAs) and analysed in detail. The spectrum of the signal and noise after being processed by the proposed noise reduction method is analysed, and the correlation matrix of the processed noise is derived. The reduced noise level leads to a higher signal-to-noise ratio (SNR) for the system, which can have a significant effect on the performance improvement of various beamforming methods and other array signal processing applications such as direction of arrival (DOA) estimation. The performance of two well-known beamformers, the reference signal based (RSB) beamformer and the linearly constrained minimum variance (LCMV) beamformer is reviewed. Then, the theoretical effect of applying the proposed noise reduction method as a pre-processing step on the performance enhancement of RSB and LCMV beamformers is studied. The theoretical results are then confirmed by simulation. As a representative example of wideband DOA estimation application, a compressive sensing-based DOA estimation method is employed to demonstrate the improved estimation by applying the pre-processing noise reduction method, which is confirmed by simulation. Next, the idea is extended to wideband non-uniform linear arrays (NLAs). Since, NLA does not have a uniform spacing, the beam response of the row vectors of the transformation is distorted. Therefore, the transformation is re-designed using the least squares method to satisfy the band-pass requirements of the transformation. Simulation results show a satisfactory improvement in the the performance of RSB and LCMV beamformers for the NLA structure. The idea is further extended to uniform rectangular arrays (URAs) and uniform circular arrays (UCAs), as two major types of the planar arrays. Two methods are proposed for reducing the effect of white noise in wideband URAs and for each one, a different transformation is designed. The first one is based on a two-dimensional (2D) transformation and the second one is an adaptation of the method developed for the ULA case. The developed method for the UCA structure is based on a one-dimensional (1D) transformation, with modified modulation for the transformation to satisfy the required band-pass characteristics of the transformation. Same as linear array structures, the RSB and LCMV beamformers are used to demonstrate the performance enhancement of the method for planar arrays.

Complex Factor Analysis and Extensions

Many subspace-based array signal processing algorithms assume that the noise is spatially white. In this case, the noise covariance matrix is a multiple of the identity and the eigenvectors of the data covariance matrix are not affected by it. If the noise covariance is an unknown arbitrary diagonal (e.g., for an uncalibrated array), the eigenvalue decomposition leads to incorrect subspace estimates and it has to be replaced by a more general “factor analysis” decomposition (FA), which then reveals all relevant information. We consider this data model and several extensions where the noise covariance matrix has a more general structure, such as banded, sparse, block diagonal, and cases, where we have multiple data covariance matrices that share the same noise covariance matrix. Starting from a nonlinear weighted least squares formulation, we propose new estimation algorithms for both classical FA and its extensions. The optimization is based on Gauss–Newton gradient descent. Generally, this leads to an iteration involving the inversion of a very large matrix. Using the structure of the problem, we show how this can be reduced to the inversion of a matrix with dimension equal to the number of unknown noise covariance parameters. This results in new algorithms that have faster numerical convergence and lower complexity compared to several maximum-likelihood based algorithms that could be considered state of the art. The new algorithms scale well to large dimensions and can replace eigenvalue decompositions in many applications even if the noise can be assumed to be white.

Electrically “Long” Dipoles in a Collocated/Orthogonal Triad—for Direction Finding and Polarization Estimation

In dipole-antenna array signal-processing algorithm development, research has focused on “short dipoles” whose physical length ( $L$ ) is under (1/10) of a wavelength $\lambda $ . Such electrically “short” dipoles have very small input impedances, rendering such “short” dipoles to be inefficient radiators. Practical dipoles, with an electrical length of $({L}/{\lambda }) \in [{0.1, 1}]$ , have notably larger input impedance, hence making them better radiators. Of such practical dipoles, this paper investigates their use for azimuth-polar direction finding and for polarization estimation. This paper will first present the measurement model (i.e., array manifold) of a triad of such practical dipoles, collocated in space and orthogonally oriented. This paper will then develop the corresponding closed-form algorithms to estimate the bivariate azimuth–elevation direction-of-arrival or the bivariate polarization. Such closed-form algorithms previously have been unavailable in the existing literature for such a triad of electrically “long” dipoles of pragmatic radiation efficiency.

Analysis of the Dynamic Sensitivity of Hemisphere-Shaped Electrostatic Sensors' Circular Array for Charged Particle Monitoring.

Electrostatic sensor arrays (ESAs) are promising in industrial applications related to charged particle monitoring. Sensitivity is a fundamental and commonly-used sensing characteristic of an ESA. However, the usually used spatial sensitivity, which is called static sensitivity here, is not proper for moving particles or capable of reflecting array signal processing algorithms integrated in an ESA. Besides, reports on ESAs for intermittent particles are scarce yet, especially lacking suitable array signal processing algorithms. To solve the problems, the dynamic sensitivity of ESA is proposed, and a hemisphere-shaped electrostatic sensors’ circular array (HSESCA) along with its application in intermittent particle monitoring are taken as an example. In detail, a sensing model of the HSESCA is built. On this basis, its array signals are analyzed; the dynamic sensitivity is thereupon defined by analyzing the processing of the array signals. Besides, a component extraction-based array signal processing algorithm for intermittent particles is proposed, and the corresponding dynamic sensitivity is analyzed quantitatively. Moreover, simulated and experimental results are discussed, which validate the accuracy of the models and the effectiveness of the relevant approaches. The proposed dynamic sensitivity of ESA, as well as the array signal processing algorithm are expected to provide references in modeling, designing and using ESAs.

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0–60m/s wind speed with resolution of 0.1m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.

High-Sensitivity Phased Array Receivers for Radio Astronomy

Phased arrays have a long history in radio astronomy. Large, sparse synthesis arrays have been in use for decades to capture high-resolution images of deep space objects. More recent work has extended the range of applications to other types of arrays, including aperture arrays (AAs) and phased array feeds (PAFs) for multibeam reflector antennas. The extreme sensitivity required for astronomical instrumentation is driving advances in numerical electromagnetic modeling, design optimization of large arrays, low noise amplifiers, minimization of receiver noise, cryogenic PAFs, array calibration, optimal beamforming, interferometric imaging, and array signal processing algorithms for radio-frequency interference mitigation. We give an overview of research progress, current and planned array-based instruments, and open challenges in these areas related to the new generation of sparse arrays, PAFs, and AAs that are in development for astronomical observatories around the world.

Foreword to the Special Issue on Multichannel Space-Based SAR

The papers in this special section focus on space-based synthetic aperture radar (SAR)technology. With the advent of active electronically steerable phased-array SAR antennas equipped with more than one receive channel (i.e., analog-/digital converter) on the most recent generation of commercial/civilian spacecraft, such as RADARSAT-2, TerraSAR-X & TanDEM-X, PAZ, and ALOS-2, relatively mature singlechannel SAR imaging is now rapidly evolving into advanced, multiaperture SAR concepts. The spatial diversity of multiple parallel receive channels can, for instance, be used to discriminate moving objects from the stationary background, to determine the underlying topography of the backscattering terrain, or to measure the large-scale surface motion using single-pass interferometry. The flexible programmability of this newest generation of SAR satellites offers quasi-seamless antenna beamsteering at a rate equivalent to the pulse repetition frequency and, in conjunction with sophisticated array signal processing algorithms, opens up novel radar concepts and capabilities that were not previously possible.

Parallel Jacobi EVD Methods on Integrated Circuits

Design strategies for parallel iterative algorithms are presented. In order to further study different tradeoff strategies in design criteria for integrated circuits, A 10 × 10 Jacobi Brent-Luk-EVD array with the simplified μ-CORDIC processor is used as an example. The experimental results show that using the μ-CORDIC processor is beneficial for the design criteria as it yields a smaller area, faster overall computation time, and less energy consumption than the regular CORDIC processor. It is worth to notice that the proposed parallel EVD method can be applied to real-time and low-power array signal processing algorithms performing beamforming or DOA estimation.

MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression

Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and presented in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches, namely, the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their ever-growing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a two-step algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, which is similar to the multiple signal classification array signal processing algorithm, identifies a subset of the library elements, which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more well conditioned than the initial one using the complete library. The second step applies collaborative sparse regression, which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets.

Direction-of-arrival estimation for a uniform circular acoustic vector-sensor array mounted around a cylindrical baffle

This work investigates the direction-of-arrival (DOA) estimation for a uniform circular acoustic Vector-Sensor Array (UCAVSA) mounted around a cylindrical baffle. The total pressure field and the total particle velocity field near the surface of the cylindrical baffle are analyzed theoretically by applying the method of spatial Fourier transform. Then the so-called modal vector-sensor array signal processing algorithm, which is based on the decomposed wavefield representations, for the UCAVSA mounted around the cylindrical baffle is proposed. Simulation and experimental results show that the UCAVSA mounted around the cylindrical baffle has distinct advantages over the same manifold of traditional uniform circular pressure-sensor array (UCPSA). It is pointed out that the acoustic Vector-Sensor (AVS) could be used under the condition of the cylindrical baffle and that the UCAVSA mounted around the cylindrical baffle could also combine the anti-noise performance of the AVS with spatial resolution performance of array system by means of modal vector-sensor array signal processing algorithms.

A Note on the Mutual-Coupling Problems in Transmitting and Receiving Antenna Arrays

In antenna arrays, mutual coupling between antenna elements is well known as an undesired effect, which degrades the performance of array signal-processing algorithms. The compensation of such an undesired effect has been a popular research topic throughout the years. Various approaches for mutual-coupling compensation have been developed, and they can easily be found in the open literature. In general, the mutual-coupling problems for a transmitting and receiving array are different, even if the physical geometry of the array remains unchanged. However, it seems that antenna engineers are not well aware of such differences in the analysis of receiving antenna arrays. In this note, the mutual-coupling problems in transmitting and receiving antenna arrays are revisited. The differences between the mutual coupling and mutual impedances for transmitting and receiving antenna arrays are explained. It is concluded that the mutual-coupling problems of transmitting and receiving arrays are in general different, and hence different mutual impedances should be used for mutual-coupling analysis and compensation.

A Novel RSMI based on Regression and Natural Power Method

In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We call this new algorithm as ‘R-NP'. The basic idea of the algorithm is to utilize an unstructured least squares linear regression approach at the updating observation vector step and the close relationship between RSMI with NP. This algorithm has simpler procedures than other RSMI algorithms. A numerical example illustrates that R-NP method is efficient and have a better performance in terms of transient behavior with respect to EIVPAST. In this paper, we consider the case where the order of system to be identified is a priori known.

A Fast Multiple-Source Detection and Localization Array Signal Processing Algorithm Using the Spatial Filtering and ML Approach

We propose a computationally efficient algorithm for detection of multiple signals that also gives a rough estimation of their direction of arrivals (DOAs). The narrowband received signals from a uniform linear array are first filtered by a set of orthogonal filters, e.g., by a fast Fourier transformation, in order to separate the sources into multiple spatial intervals. This transformation converts the complicated multihypothesis problem of source detection and localization into multiple binary hypothesis testing problems. For an additive white Gaussian noise (AWGN) environment, the maximum-likelihood (ML) solution of these interrelated tests requires substantially less computational complexity than that of the multihypothesis problem. For each spatial interval, a binary test detects the presence of a single source and thus gives a rough localization. We employ generalized-likelihood ratio (GLR) tests as the detection criterion, assuming that the number of sources, their power, and the noise variance are all unknown. We also show that the optimal uniformly most powerful invariant (UMPI) detector does not exist. However, we derive a UMPI detector that uses some extra information and as a result provides an upper bound performance for evaluation of any invariant detector. Simulations illustrate that the proposed noniterative GLR test performs efficiently for various number of observed data snapshots and signal-to-noise-ratios (SNR)s, and its performance is comparable to the upper bound performance. We used the proposed algorithm for the initialization of the iterative implementation of the standard ML localization. This combination is a high-resolution localization algorithm with a low computational complexity

Modal array signal processing using circular microphone arrays applied to acoustic source detection and localization problems

Many applications of acoustic signal processing rely on estimates of several parameters present in the observed acoustic scene such as the number and location of acoustic sources. These parameters have been traditionally estimated by means of classical array signal processing (CASP) algorithms using microphone arrays. Algorithms for parameter estimation solely based on the paradigm of CASP often suffer from the narrowband assumption underlying the signal model. This restriction limits their usability when wideband signals, such as speech, are present in the wave field under observation. We investigate the parameter estimation problem by applying the notion of wave field decomposition using baffled circular microphone arrays. The obtained wave field representation is used as the basis for ‘‘modal array signal processing algorithms.’’ It is shown that by applying the notion of modal array signal processing, novel algorithms can be derived that have the potential to unambiguously detect and localize multiple simultaneously active wideband sources in the array’s full field-of-view. Performance evaluations by means of simulations, measurements, and real-time case studies are presented.

Acoustic source detection and localization based on wavefield decomposition using circular microphone arrays

This paper is concerned with the problem of detecting and localizing multiple wideband acoustic sources by applying the notion of wavefield decomposition using circular microphone arrays optionally mounted into cylindrical baffles. The decomposed wavefield representation is used to serve as a basis for so-called modal array signal processing algorithms, which have the significant advantage over classical array signal processing algorithms that they inherently support multiple wideband acoustic sources. A rigorous derivation of modal array signal processing algorithms for source detection and localization, as well as performance evaluations, by means of measurements using an actual real-time capable implementation are presented.

Temporal diversity on DS-CDMA communication systems for blind array signal processing

Many array signal processing algorithms have been developed to reject interfering signals and, therefore, increase the quality of the communication link. Some of them are reference-based and some others are blind, i.e., without any side information. A specific type of deterministic blind beamforming algorithms, named self-reference, is treated in the paper. Self-reference beamforming is suitable for signals presenting a diversity or a redundancy structure. This type of signals exhibit the property of exact self-prediction that allows the definition of an error signal to be minimized. In the present paper, the time diversity present in the well-known DS-CDMA communication system is analyzed. Blind approaches are derived from the property of exact self-prediction arising from the implicit time redundancy encountered in the spreading codes, and from the explicit time redundancy that can also be induced by the transmitter. The proposed methods do not require, unlike most blind subspace-based techniques, any kind of spectral matrix decomposition, with the consequent reduction of complexity and computational burden. The presented methods were tested via simulations and compared to the classical time reference beamformer (TRB) in terms of SINR and BER, showing a performance slightly better than a TRB scheme with 10% of the bits as a training sequence.