Array Signal Processing Research Articles

A signal processing system for infrasound-based detection and tracking of tornadoes

It has been established that tornadic storms emit infrasound at infrasonic frequencies and that this sound can be detected from practically significant distances exceeding 100 km in some cases. This work describes a signal processing system that is used to ingest data from a network of infrasound arrays and produce near real-time detections and ground tracks of multiple, simultaneously active tornadic storms. Specifically, a brief review of a particular array network and corresponding hardware is presented followed by detailed descriptions of the array signal processing architecture and source geolocation system. Emphasis is placed on algorithms for multiple moving source acoustic source bearing tracking, low SNR signal detection, mitigation of errors created by anomalous acoustic events, and multiple-array data association. Performance results corresponding to simulated and recorded data are presented.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

Wideband off-grid direction-of-arrival estimation based on the improved finite rate of innovation method.

Off-grid direction-of-arrival estimation is a crucial research area in multi-sensor array signal processing to achieve accurate estimation in a finite sparse grid. However, current off-grid estimation methods primarily focus on narrowband processing, which may not be suitable for practical passive estimation scenarios where the targets of interest are wideband signals with various steering vectors and varying signal-to-noise ratios across frequency bins. First, we propose an improved weighting-based wideband joint finite rate of innovation algorithm to address this limitation. This algorithm extends the narrowband approach by approximating the wideband array data as multiple observations at the difference frequency using extended frequency difference weighting. Additionally, we propose an estimation method under non-ideal weighting conditions to mitigate bias caused by deviations in initial weight values through linear fitting of multiple estimation results obtained on a sparse grid. Simulation results demonstrate that our proposed algorithm outperforms existing methods by providing more accurate estimates and lower computational complexity for wideband off-grid multi-targets at low signal-to-noise ratio while unrestricted by grid limitations. Furthermore, experimental data collected from the South China Sea validate our proposed algorithm's effectiveness and superior performance for direction-of-arrival estimation of wideband off-grid targets.

Block-term tensor decomposition in array signal processing

The ability of tensor decomposition (TD)-based methods to recover latent information, including channel parameters and transmitted symbols in the array signal processing (ASP) context, in a deterministic manner and with little or no training overhead, fits well with the data efficiency needs of future-generation multi-user massive multiple-input multiple-output systems. However, such methods have mostly relied on a few classical TD models, notably the canonical polyadic decomposition (CPD), which can be quite restrictive in realistic scenarios. In this paper, a generalized CPD model, known as block-term decomposition (BTD), is re-visited in the context of ASP, and shown to be the natural choice in sensor arrays of increased dimensionality that also involve channel multipath. Semi-blind joint channel estimation/data detection (JCD) is addressed in this context via efficient algorithms that wed existing JCD schemes with BTD approximation. Robust to sensor failures and recursive versions are also developed. The special yet important case of the uniform rectangular array (URA) configuration is adopted to illustrate the ideas and results. The signal detection performance of the BTD-inspired semi-blind JCD schemes is evaluated under various conditions with the aid of simulations and seen to be favorably compared with that of the training-only-based solution.

Position estimation of acoustic elements based on improved delay estimation algorithm

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

Sparse channel estimation for underwater acoustic OFDM systems with super-nested pilot design

Underwater acoustic channels are usually sparse and have large delay spread. In this paper, super-nested array structure in the field of array signal processing is borrowed to be the pilot design of underwater acoustic OFDM systems, in order to better estimate large delay spread channels with limited number of pilots. Specifically, by constructing the pilot subcarriers’ covariance matrix and the pilot position difference, the virtual pilot on the differential co-array are employed for sparse channel estimation. In order to reduce the error between the estimated pilot subcarriers’ covariance matrix and the ideal covariance matrix, the cross-correlation matrix of pilot subcarriers is estimated in advance for interference cancellation. Then the sparse iterative covariance estimation algorithm (SPICE) is adopted to further refine the covariance matrix and improve the channel estimation performance. Simulation, pool and sea experimental results show that the proposed method can effectively estimate the large delay spread sparse channels and improve the performance of underwater acoustic OFDM systems.

GA-BP-Based Low-Noise FBG Hydroacoustic Monitoring System with Reference Sensor.

To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from -173.76 dB to -171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment.

Analysis of infrasound array data from tornadic storms in the southeastern United States

Data recorded by several infrasound sensor arrays deployed in the southeastern United States during the spring of 2018 have been analyzed during a period when a storm front passed through and spawned several tornado touchdowns. The tornadoes ranged from EF-0 to EF-2. Accurate bearings have been obtained, corresponding to tornadoes at ranges up to more than 100 km. Earlier in the day, a convective storm cell passed and triggered a tornado warning but no verified tornado touchdowns. During this earlier passage, a weaker signal was detected with bearings that tracked this convective storm cell. The analyses reveal that on the systems deployed, the dominant band of coherent infrasound measured from the tornadic storms was between 2 and 6 Hz. Atmospheric conditions are known to have a significant influence on signal detection because of propagation effects and local wind noise conditions. Propagation modeling and wind noise analysis were undertaken and are observed to be consistent with signal detection from the tornadoes. Because of the static sources, as well as the multiple strong cells that were in the region simultaneously, it was necessary to use array signal processing methods that are capable of resolving multiple sources.

Design of optimum two-dimensional non-redundant arrays

Recent advancements in array signal processing focus on enhancing source detection and reducing the effects of mutual coupling among array elements. This has been achieved using Direction of Arrival (DOA) estimation via virtual arrays formed by sparse arrays. Non-Redundant Arrays (NRAs) are a very common structure among sparse arrays. Traditionally, one-dimensional NRAs capture either azimuth or elevation angles of sources, but practical scenarios often require both simultaneously. This paper introduces optimized methods for designing two-dimensional (2-D) NRAs to address this need. In addition to the optimized design approach for creating 2-D NRAs with minimum aperture, the optimized design approaches for creating 2-D NRAs with desired aperture, with minimized mutual coupling effect and with hybrid of both are proposed. The designed arrays can be in the form of a rectangle or a regular polygon with the number of sides being a multiple of 4. The proposed array design methods significantly enhance the flexibility in designing NRAs, allowing the creation of various array configurations for any desired number of sensors. Simulation results show that the proposed arrays outperform the existing 2-D arrays in estimating the DOAs of signal sources and show more robustness against the effects of mutual coupling.

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers with 2D Oscillator Array and Inter-Sensor Spectrogram Cross-correlation.

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

MVDR Algorithm Based on Loading Factor and Sample Selection Strategy

The MVDR (Minimum Variance Distortionless Response) algorithm is a classic Wiener filtering method used for beamforming in array signal processing. In a one-dimensional linear array high-frequency ground wave radar system, it can be employed to suppress various types of ionospheric clutter. A key step in suppression is the use of maximum likelihood estimation (MLE) to estimate the ionospheric clutter covariance matrix. However, MLE typically assumes that samples are independently and identically distributed (i.i.d.). Traditional MVDR algorithms estimate the clutter covariance matrix using all samples, which may not satisfy the i.i.d. condition. Therefore, this paper proposes a new sample selection strategy that utilizes the Mahalanobis distance method to select samples. Meanwhile, due to the very small numerical values of the ionospheric clutter covariance matrix, even if the matrix is full-rank, numerical instability may occur during inversion. To address this issue, the paper introduces 4 different forms of regularization factors. Empirical data demonstrate that the proposed new method offers better suppression of ionospheric clutter compared to the traditional MVDR algorithm.

Close approximations to the sound of a cochlear implant.

Cochlear implant (CI) systems differ in terms of electrode design and signal processing. It is likely that patients fit with different implant systems will experience different percepts when presented speech via their implant. The sound quality of speech can be evaluated by asking single-sided-deaf (SSD) listeners fit with a cochlear implant (CI) to modify clean signals presented to their typically hearing ear to match the sound quality of signals presented to their CI ear. In this paper, we describe very close matches to CI sound quality, i.e., similarity ratings of 9.5 to 10 on a 10-point scale, by ten patients fit with a 28 mm electrode array and MED EL signal processing. The modifications required to make close approximations to CI sound quality fell into two groups: One consisted of a restricted frequency bandwidth and spectral smearing while a second was characterized by a wide bandwidth and no spectral smearing. Both sets of modifications were different from those found for patients with shorter electrode arrays who chose upshifts in voice pitch and formant frequencies to match CI sound quality. The data from matching-based metrics of CI sound quality document that speech sound-quality differs for patients fit with different CIs and among patients fit with the same CI.

Online adaptation of fourier series-based acoustic transfer function model and its application to sound source localization and separation

In this paper, we propose an online adaptation method for Fourier series-based acoustic transfer function (FS-ATF) models for robot audition systems using microphone array signal processing. The ATF represents the characteristics of signal propagation from a sound source to a microphone, which is essential for sound source localization (SSL) and sound source separation (SSS). For practical applications in dynamically changing real-world environments, ATF models must satisfy two criteria: (1) they must be adaptable to changes in the acoustic environment; and (2) they must be lightweight to be suitable for resource-constrained systems, such as robots with limited memory and computational capacity. The proposed method addresses these challenges using Fourier series expansions for interpolation, which reduces the memory footprint of the ATF model and facilitates online adaptation to acoustic environmental changes. The experimental results demonstrate that the proposed online adaptation method both improves the SSL and SSS performance while reducing the size of the ATF model, which represents a significant improvement over existing online ATF adaptation methods.

DOA Estimation Based on Virtual Array Aperture Expansion Using Covariance Fitting Criterion

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output’s covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data.

Adaptive digital antenna array signal processing algorithm based on LDL decomposition

Two algorithms for inverting correlation matrices of digital antenna array signals are compared: LDL decomposition and the fringing method. The idea of comparing algorithms was to take into account the limitations on the length of the input data mantissa in the process of space-time processing using FPGAs. It has been found that rounding errors in the signal processing process in FPGAs can lead to the destruction of the directivity pattern. This is due to the fact that when implementing various algorithms for matrix inversion, the distribution of errors in the channels becomes interconnected. This means that when implementing adaptive antenna arrays on FPGAs, it is necessary to carefully choose the length of the mantissa and the conversion algorithm. In this regard, an algorithm for signal processing in an adaptive antenna array is proposed, its difference from the known ones is that when implemented on FPGAs, floating-point arithmetic of limited mantissa bit is used. When comparing two computationally efficient matrix inversion algorithms (the LDL method and the fringing method), the LDL method has higher stability. It was found that with an increase in the rank of the correlation matrix, the effect of rounding errors will be stronger. In this regard, it is necessary to determine in advance at what ratio of power sources and internal noise it is planned to use an adaptive processor.

Electromagnetic informed data model considerations for near-field DOA and range estimates

Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

A High-Performance Antenna Array Signal Processing Method in Deep Space Communication

A High-Performance Antenna Array Signal Processing Method in Deep Space Communication

Guided Ultrasonic Wave Monitoring of Damage in Anisotropic Composites

Lightweight, carbon fibre reinforced composites are often employed for the manufacture of aerospace components due to their good strength to weight ratio. However, due to poor interlaminar strength, composite components are prone to barely visible impact damage, caused by low velocity impacts, during aircraft operation. Guided-wave-based structural health monitoring (SHM) techniques, using a network of distributed sensors, is an important Structural Health Monitoring (SHM) tool for the detection and localization of in-service impact damage in composite structures. However, the material anisotropy of composite laminates influences guided wave propagation and scattering. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Defect characterization can be improved by considering the scattering characteristics of various damage types for the sparse array signal processing. Guided wave scattering (A0 Lamb wave mode) was investigated around an artificial insert delamination in a quasi-isotropic CFRP panel. Permanent magnets, mounted on an undamaged region of the plate, were also used as scattering targets and compared to the delamination case. Full 3D Finite Element (FE) simulations were performed for both the delamination and magnet cases and compared to wavefield data obtained from non-contact laser Doppler vibrometer (LDV) measurements. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. Good agreement was found between the experimental measurements and simulations. Scattered guided wave amplitudes around each damage type show strong directional dependency with energy focusing along the fiber directions of the outer ply layers of the laminate. Distinct scattering behavior was observed for each damage type. Implications of anisotropy and angular scattering on sparse array SHM of different defect types are discussed.

Combination of the biologically inspired coupled system and high‐frequency surface wave radar at signal level

AbstractVirtual aperture extension of small aperture array has attracted wide attention in high‐frequency surface wave radar (HFSWR). A biologically inspired coupled (BIC) system is employed to virtually extend the array aperture. However, the existing researches on BIC only consider the array signal processing model and do not combine it with actual radar signal principle. To indeed apply the BIC system to HFSWR, two detailed methods which combine the BIC and HFSWR at signal level are proposed. A three‐dimensional signal model of HFSWR considering array processing was established and the entire signal processing was derived. Then, two combination methods, namely fast‐time domain (FTD)‐BIC and slow‐time domain (STD)‐BIC are proposed. The former implements the BIC before fast‐time processing, while the latter implements the BIC before slow‐time processing. The authors demonstrate that they can virtually extend the array aperture without affecting the target detection. Meanwhile, their capabilities in multi‐target scenarios are analysed and satisfactory conclusions are obtained. By numerical simulations and experiments, the array aperture and range‐Doppler (RD) spectrum of the standard HFSWR and BIC‐HFSWR are compared. The results show that while the performance of their RD spectrum is almost the same, BIC‐HFSWR has an enlarged virtual aperture than standard HFSWR.