Array Signal Processing Research Articles

Peculiarities of adaptive signal processing in circular antenna arrays

Formulation of the problem. Today, antenna arrays are often used as directional antennas of different radio systems. The main advantages of the such antennas over the mechanically steered directional antennas are the ability to steer the main lobe (beam) of the Radiation Pattern (RP) without the rotation of the array, and also the ability to control the shape of the RP. However, the antenna arrays also have some drawbacks. For example, a drawback of the linear and the planar antenna arrays is the varying beamwidth. This width increases as the orientation of the beam moves from the normal to the aperture to the plane of the aperture. This feature is due to the fact that when the beam is deflected, the effective aperture of the antenna array decreases, and this is the reason of the beam increase. If it is required to steer the beam in a wide range of angles while maintaining its width, then this restricts the usage of a linear and flat antenna arrays as the directional antennas. This disadvantage is practically absent in the circular antenna arrays. Therefore, such arrays are attractive for the usage, for example, in the radio communication systems, because they allow the beam to move within 360° in azimuth plane while maintaining its width practically fixed. If the antenna array is built using a digital beamforming architecture (digital array), then such an array can also be easily made as a multi-beam one, that will provide a parallel view of space. In multibeam digital antenna arrays, the same analog portion of each channel from the antenna to the digital output is used to form all the beams. And only in digital form, the separate devices for the forming of these beams are required, which multiply the channel output signal samples by the weights and add the weighted signals together to form the output signals of the antenna array. The access to the signals from the outputs also allows such an antenna array to easily become adaptive, because these signals are used to calculate the weights in all the adaptive algorithms. The adaptive signal processing allows to suppress the interferences. The suppression of the interference signals in the output signal of the antenna array is achieved due to the creation of the dips (low levels) in the RP towards the sources of these interferences. Such a change in the shape of the RP is provided by the changing weights calculated using an adaptive algorithm. Target. The paper deals with the circular adaptive antenna arrays with digital beamforming. The features of the such adaptive antenna arrays and their performance are considered. Results. The architecture of a single beam and a multibeam adaptive antenna array is considered, as well as a procedure (algorithm) for the calculation of its weights. It is also demonstrated the efficiency of a circular adaptive antenna array in terms of maintaining its beamwidth regardless of its direction and simultaneously suppressing interferences, that practically does not affect this beamwidth. Practical significance. The considered adaptive antenna array can be used as a directional antenna in radio systems where it is required to form a several beams simultaneously, to maintain their width regardless of steering directions, and also to suppress the interference signals in the output signal.

DOA Estimation With Co-Prime Array by a Combined BNC-PFLOM Method in Presence of Impulsive Noise

Direction of arrival (DOA) estimation with co-prime array is well studied in the field of array signal processing, but most research assumes noise to be Gaussian white noise. In practical scenarios, there might be impulsive noise, which has neither second-order statistics nor high-order cumulant, therefore cannot be processed directly in co-prime array configurations. In addition, the signals received by the co-prime virtual array are of single snapshot, which can be considered as coherent virtual signals coming from the same source and therefore performance degradation in DOA estimation. To solve the above mentioned problems, a combined bounded non-linear covariance (BNC) and phase fractional low-order moments (PFLOM), called BNCPFLOM, is proposed in this paper. The proposed BNC-PFLOM combines the merits of both BNC and PFLOM by constructing equivalent data covariance matrices of the received signal to lower the impact of impulsive noise. Moreover, the proposed BNC-PFLOM can build multiple pseudo snapshots of virtual signals from the co-prime array by using different orders of the proposed method. With the increase of pseudo snapshots, the proposed method is more robust in DOA estimation. Besides, the pseudo snapshots are developed from different methods, which implies different virtual signals coming from different sources. Therefore, the correlation between the virtual co-array signals can be reduced; and the proposed method direct estimates the DOAs of signals without spatial smoothing when the number of incoming signals is small. The proposed method generates multiple pseudo snapshots, which improves the resolution and accuracy in DOA estimation with co-prime array, and can be used for radar, sonar, communication systems, etc. Simulation results demonstrate the effectiveness of the proposed method, which outperforms the conventional BNC, FLOM, and PFLOM with smaller root means square error (RMSE) in terms of the generalized signal to noise ratio (GSNR) and factor of impulsive noise.

An Improved Toeplitz Approximation Method for Coherent DOA Estimation in Impulsive Noise Environments.

Direction of arrival (DOA) estimation is an important research topic in array signal processing and widely applied in practical engineering. However, when signal sources are highly correlated or coherent, conventional subspace-based DOA estimation algorithms will perform poorly due to the rank deficiency in the received data covariance matrix. Moreover, conventional DOA estimation algorithms are usually developed under Gaussian-distributed background noise, which will deteriorate significantly in impulsive noise environments. In this paper, a novel method is presented to estimate the DOA of coherent signals in impulsive noise environments. A novel correntropy-based generalized covariance (CEGC) operator is defined and proof of boundedness is given to ensure the effectiveness of the proposed method in impulsive noise environments. Furthermore, an improved Toeplitz approximation method combined CEGC operator is proposed to estimate the DOA of coherent sources. Compared to other existing algorithms, the proposed method can avoid array aperture loss and perform more effectively, even in cases of intense impulsive noise and low snapshot numbers. Finally, comprehensive Monte-Carlo simulations are performed to verify the superiority of the proposed method under various impulsive noise conditions.

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

Twenty-Five Years of Sensor Array and Multichannel Signal Processing: A review of progress to date and potential research directions

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.

Key Technologies for High-Speed Si-Substrate LED Based Visible Light Communication

Nowadays, the demand for high-speed information transmission is increasing rapidly. Visible light communication (VLC) based on light-emitting diodes (LEDs) is supposed as a potential candidate for future 6G communication networks. However, as an emerging field of optical communications, the data rate of VLC systems is restricted to the extremely limited modulation bandwidth and nonlinear characteristics of optoelectronic devices. In this paper, we outline the state of art and challenges of VLC, and introduce the key technologies for high-speed Silicon (Si) -substrate LED-based VLC systems. Based on an optimized integrated 4×4 8-wavelength LED array and advanced digital signal processing (DSP) methods, we demonstrate an ultrahigh-speed VLC system. Digital Zobel network (DZN) pre-equalization and transfer learning bidirectional gate recurrent unit (TL-Bi-GRU) based post-equalization are proposed to mitigate linear and nonlinear impairment. The yellow and green LEDs are especially enhanced in high-efficacy, and achieve the impressive data rates of 3.45Gbit/s, and 3.83Gbit/s respectively, which is a remarkable improvement. The achievable overall data rate of the device is 28.93 Gbit/s. As far as we know, it's the highest data rate achieved by a single-chip Si-substrate LED array.

Gridless Multisnapshot Variational Line Spectral Estimation From Coarsely Quantized Samples

Due to the increasing demand for low power and higher sampling rates, low resolution quantization for data acquisition has drawn great attention recently. Consequently, line spectral estimation (LSE) with multiple measurement vectors (MMVs) from coarsely quantized samples is of vital importance in cutting edge array signal processing applications such as range estimation and DOA estimation in millimeter wave radar systems. In this paper, we combine the low complexity gridless variational line spectral estimation (VALSE) and expectation propagation (EP) and propose an MVALSE-EP algorithm to estimate the frequencies from coarsely quantized samples. In addition, the Cram\'{e}r Rao bound (CRB) is derived as a benchmark performance of the proposed algorithm, and insights are provided to reveal the effects of system parameters on estimation performance. It is shown that snapshots benefits the frequency estimation, especially in coarsely quantized scenarios. Numerical experiments are conducted to demonstrate the effectiveness of MVALSE-EP, including real data set.

Three More Decades in Array Signal Processing Research: An optimization and structure exploitation perspective

The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and the availability of powerful and affordable multiantenna hardware platforms.

High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation

For the direction of arrival (DOA) in array signal processing, eigenvalue decomposition (EVD) is one key issue in hardware implementation of the multiple signal classification (MUSIC) algorithm. Therefore, we introduce the look-ahead sim- plified one-sided Jacobi's method to efficiently decompose those symmetric matrices in this article and prove that the new method has the best orthogonality of eigenvector and locates eigenvectors closest to the true solution in theory. Both the numerical perform- ance and real-time are important in engineering, so we present the novel flexible hardware architecture in single floating point arithmetic for EVD on field-programmable gate arrays (FPGAs). Finally, the simulated and raw data are used to investigate the performance of some different approaches in the context of both the EVD and MUSIC algorithm. The experimental results show that our proposed method has the best performance.

DOA Estimation of Underwater Acoustic Array Signal Based on Wavelet Transform With Double Branch Convolutional Neural Network

In underwater acoustic communications, a hydrophone array is often used to receive the underwater acoustic signals to improve the gain of the received signal. The direction of arrival (DOA) estimation is a basic task in underwater acoustic array signal processing. In underwater signal transmission, due to the channel complexity, a large signal transmission loss, and heavy noise interference, the received signal is seriously distorted, and the DOA estimation is poor. In order to improve the accuracy of DOA estimation for underwater acoustic array signals, in this work, we propose continuous wavelet transform with convolutional neural network (CWT-CNN) method. We use a linear factor to improve the calculation process of continuous wavelet transform. The characteristics of time-frequency fusion of signals are calculated by constructing time-frequency array model with modified wavelet factors. The fused features are then used to train the proposed double branch CNN. As compared with other DOA algorithms based on neural networks, the proposed algorithm shows a 5%–20% higher accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.479^{\circ }-1.172^{\circ }$</tex-math></inline-formula> lower RMSE at 7 different values of SNR. In addition, the proposed algorithm is less computationally complex.

Study on the method of passive ranging for the moving target underwater without prior knowledge of the marine environment

The waveguide invariant is used to describe the relationship between the slope of interference fringes, the frequency and the distance of the source in the frequency-distance acoustic intensity diagram. The waveguide invariant method considers the influence of the channel environment on sound propagation and reduces the dependence on environmental parameters, which makes its robustness stronger. In this paper, the ranging algorithm using a single element is discussed without considering the information of waveguide environment parameters. Based on the horizontal hydrophone array, the array signal processing method is used to improve the accuracy of ranging compared to a single element. The experimental results show that the ranging accuracy of this method is improved to less than 2% at a long distance after the conventional beamforming (CBF) processing. The above results show that the array signal processing method used improves the accuracy of the distance measurement obviously.

Novel Auto-Calibration Method for 7-Elements Hexagonal Array with Mutual Coupling

Mutual coupling between antenna array elements will significantly degrade the performance of the array signal processing methods. Due to the Toeplitz structure of mutual coupling matrix (MCM), there exist some mutual coupling calibration algorithms for the uniform linear array (ULA) or uniform circular array (UCA). But few methods for other arrays. In this letter, we derive a new transformation formula for the MCM of the 7-elements hexagonal array (HA-7). Further, we extend two mutual coupling auto-calibration methods from UCA to HA by the transformation formula. Simulation results demonstrate the validity of the proposed two methods.

Underwater multi-source DOA tracking using uniform linear array based on improved GM-PHD filter

Uniform linear array (ULA) is commonly used for collecting underwater acoustic signals, and the direction-of-arrival (DOA) is obtained by array signal processing methods. The ULA-based multi-source DOA tracking suffers from the performance degradation caused by the presence of false tracks from port-starboard ambiguity. To address this problem, we propose a bearing ambiguity discrimination algorithm. Firstly, the sum of real bearing motion velocity between truth and ambiguous targets is established with the Gaussian distribution. Secondly, an improved Gaussian mixture probability hypothesis density filter, which can adaptively generate target birth intensity, is utilized to select truth and ambiguous bearings generated by the same target from the estimated posterior intensity. Thirdly, truth and ambiguous bearings are discriminated according to the fact that the change of array heading will lead to a significant change in ambiguous bearing. Simulation and real experimental results illustrate that the proposed algorithm can provide unambiguous target-generated tracks and accurately estimate the number of targets.

Difference frequency coherent matched autoproduct processing for source localization in deep ocean

Matched autoproduct processing (MAP) refers to a matched field processing (MFP) style array signal processing technique for passive source localization, which interrogates frequency-difference autoproduct instead of genuine acoustic pressure. Due to frequency downshifting, MAP is less sensitive to environmental mismatch, but it suffers from low spatial resolution and a low peak-to-sidelobe ratio of ambiguity surface. These source localization metrics are herein improved with coherent approaches. Specifically, the coherent normalized MFP is extended to coherent matched autoproduct processing (CMAP), a difference frequency coherent algorithm that exploits correlations among the autoproducts at various difference frequencies and eliminates the phase factor of the source spectrum for passive source localization. Phase-only coherent matched autoproduct processing is a CMAP derivation technique that only uses phase information. Through simulations in a Munk sound-speed profile environment, sensitivity analysis in the South China Sea environment, and high signal-to-noise ratio experimental measurements, these two algorithms are validated as compared to the conventional MFP and incoherent MAP. Simulation investigations demonstrate that difference frequency coherent algorithms can suppress sidelobes while simultaneously enhancing the localization resolution and robustness. The experimental results generally support the findings of the simulations.

A fast data-driven method for inverse microphone array signal processing

Microphone arrays have long been used to characterize and locate sound sources. However, existing algorithms for processing the signals are computationally expensive and, consequently, different methods need to be explored. Recently, the trained iterative soft thresholding algorithm (TISTA), a data-driven solver for inverse problems, was shown to improve on existing approaches. Here, a more in-depth analysis of its robustness and frequency dependence is provided using synthesized as well as real measurement data. It is demonstrated that TISTA yields favorable results in comparison to a covariance matrix fitting inverse method, especially for large numbers of sources.

Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections

AbstractIn array signal processing, high‐resolution direction‐of‐arrival (DOA) estimation by eigendecomposition method requires knowledge of the array covariance matrix and an exact characterisation of the array. The dependence of most such methods on the array manifold substantially degrades their array performance when the actual sensors deviate from their assumed nominal values. This article aims at formulating a deep neural network framework for DOA estimation with feasible computational complexity. The two‐stage algorithm is composed of a detection network and a series of parallel DOA estimation networks: the multilayer perceptron is trained to detect the presence of one or more sources in an angular sector at the first stage, the DOA estimation networks then estimate their respective DOAs at the second stage. When a single type of array imperfection is considered, numerical results reveal that the DNN‐based method can obtain DOA estimates with much higher precision than the existing techniques for significant location errors and gain/phase perturbations, they also illustrate the potential usefulness of the proposed algorithm towards better DOA estimation accuracy for mutual coupling as the mutual coupling matrix is not simplified. Moreover, the proposed method is finally extended to deal with the DOA estimation problem when multiple types of array perturbations coexist.

A Source Number Enumeration Method at Low SNR Based on Ensemble Learning

Source number estimation is one of the important research directions in array signal processing. To solve the difficulty of estimating the number of signal sources under a low signal-to-noise ratio (SNR), a source number enumeration method based on ensemble learning is proposed. This method first preprocesses the signal data. The specific process is to decompose the original signal into several intrinsic mode functions (IMF) by using Complementary Ensemble Empirical Mode Decomposition (CEEMD), and then construct a covariance matrix and perform eigenvalue decomposition to obtain samples. Finally, the source number enumeration model based on ensemble learning is used to predict the number of sources. This model is divided into two layers. First, the primary learner is trained with the dataset, and then the prediction result on the primary learner is used as the input of the secondary learner for training, and then the prediction result is obtained. Computer theoretical signals and real measured signals are used to verify the proposed source number enumeration method, respectively. Experiments show that this method has better performance than other methods at low SNR, and it is more suitable for real environment. Keywords—Source number estimation; Array signal processing; SNR; IMF; CEEMD; Ensemble learning.

Uncertainty quantification of deep learning based direction-of-arrival estimation with conformal prediction

Direction-of-arrival (DOA) estimation is an important task in array signal processing with applications in underwater acoustics, biomedicine, geophysics, and robotics. The signals recorded on a spatially distributed array along with an analytical model of wave propagation, are used to estimate the unknown DOA. Recently, end-to-end deep learning-based methods have been proposed for improving DOA estimation performance. Yet, real-time DOA estimation has a number of challenges such as sensor noise, reverberant surroundings, uncertainty in sensor locations and environment. In this work, we propose to use conformal prediction for uncertainty quantification in DOA estimation. Conformal prediction is a statistically rigorous method to provide confidence intervals for an estimated quantity without making distributional assumptions. With conformal prediction, confidence intervals are computed via quantiles of user-defined score values. This easy-to-use method can be applied to any trained classification/regression model as long as an appropriate score function is chosen. The proposed approach shows potential to enhance the real-time applicability of deep learning methods for DOA estimation. We illustrate the advantages of conformal prediction for different deep-learning methods for DOA estimation. To the best of our knowledge, this is the first analysis of using conformal prediction for uncertainty quantification of deep learning methods for acoustic estimation.

Design and Realization of Ultra-wideband Constant Beamwidth Beamforming in a Waveguide Environment

Array signal processing technology has been widely used in signal detection and underwater target radiation noise measurement. A design and realization method of an ultra-wideband and high-robustness constant beamwidth beamformer based on a vertical nested array is proposed. Using the nested array multiplexing, the ultra-wideband constant beamwidth beamforming was realized by using fewer array elements; the main lobe of each sub-band beam in the designed band was made to approach the desired constraint value based on the convex optimization method; the beamforming robustness and array gain were improved through the constraint of the weight vector two-norm. In the paper, the influence on beamforming due to inconsistent array element amplitude-phases in a measured waveguide environment is analyzed. The computer simulation was used to realize the measurement of constant beamwidth beamforming within a 100-octave band range in the measured waveguide environment and verify the effectiveness of the proposed method.

Optimum Multiantenna Ambient Backscatter Receiver for Binary-Modulated Tag Signals

Ambient backscatter communication (AmBC) is becoming increasingly popular as a green Internet-of-things technology by enabling ultra-low-power data exchanges among simple tags. The bit-error-rate (BER) performance of the AmBC receivers is hampered by the low signal-to-interference-plus-noise ratio (SINR), which limits either the range or the data rate that can be supported by these systems. Among the alternatives, the solutions provided by a multiantenna receiver enable several practical methods to improve the SINR using array signal processing. In this paper, the optimum multiantenna AmBC receiver for any binary modulated tag signal is presented. Two receivers are derived from the maximum-a-posterior probability criterion for deterministic-unknown and for Gaussian distributed ambient signals. The closed-form cumulative distribution functions are derived for both of the receivers for performance evaluation purposes. It is discussed that these two receivers are equivalent under practical conditions, and the optimum receiver is a generalization of the multiantenna receivers available in the literature. Several implementation details are discussed, and numerical evaluation is provided to validate the development. Therefore, the presented receiver accommodates different tag modulations and achieves the best possible BER performance, which opens up new application possibilities by improving AmBC system flexibility.