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Open AccessArticle

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

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Electronics 2023, 12(18), 3954; https://doi.org/10.3390/electronics12183954 - 20 Sep 2023

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Abstract

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow [...] Read more.

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

A Novel Low-Complexity Method for Near-Field Sources Based on an S-IMISC Array Model

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Electronics 2023, 12(11), 2435; https://doi.org/10.3390/electronics12112435 - 27 May 2023

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Abstract

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this [...] Read more.

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

Mixed Near-Field and Far-Field Sources Localization via Oblique Projection

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Electronics 2023, 12(10), 2340; https://doi.org/10.3390/electronics12102340 - 22 May 2023

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Abstract

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the [...] Read more.

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessCommunication

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

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Ming-Yan Gong

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Bin Lyu

Electronics 2023, 12(10), 2191; https://doi.org/10.3390/electronics12102191 - 11 May 2023

Viewed by 615

Abstract

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of the expectation–maximization algorithm, for solving the ML direction finding problem of stochastic sources, which [...] Read more.

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of the expectation–maximization algorithm, for solving the ML direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas, and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm that the ECME algorithm can efficiently obtain the ML based DOA estimate of each stochastic source. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

Hybrid T-Shaped Sensor Array Composed of Acoustic Vector Sensors and Scalar Sensors

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Wei Rao

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Yuanqing Li

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Dan Li

Electronics 2023, 12(8), 1813; https://doi.org/10.3390/electronics12081813 - 11 Apr 2023

Viewed by 589

Abstract

Through the more available acoustic information or the polarization information provided, vector sensor arrays outperform the scalar sensor arrays in accuracy of localization. However, the cost of a vector sensor array is higher than that of a scalar sensor array. To reduce the [...] Read more.

Through the more available acoustic information or the polarization information provided, vector sensor arrays outperform the scalar sensor arrays in accuracy of localization. However, the cost of a vector sensor array is higher than that of a scalar sensor array. To reduce the cost of a two-dimensional (2-D) vector sensor array, a hybrid T-shaped sensor array consisting of two orthogonal uniform linear arrays (ULAs) is proposed, where one ULA is composed of acoustic vector sensors and the other is composed of scalar sensors. By utilizing the cross-correlation tensor between the received signals from the two ULAs, two virtual uniform rectangular arrays (URAs) of acoustic vector sensors are obtained, and they can be combined into a larger URA. It is shown that a larger acoustic vector sensor URA with M²+1 degrees of freedom (DOFs) can be obtained from the specially designed T-shaped array with M acoustic vector sensors and 2M scalar sensors. Furthermore, by means of the proposed tensor model for the larger URA, the inter-sensor spacing can be allowed to exceed greatly a half-wavelength. Accordingly, the proposed method can achieve both a high DOF and a large array aperture. Simulation results show that the proposed method has a better performance in 2-D direction-of-arrival estimation than some existing methods under the same array cost. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

Wideband Direction-of-Arrival Estimation Based on Hierarchical Sparse Bayesian Learning for Signals with the Same or Different Frequency Bands

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Electronics 2023, 12(5), 1123; https://doi.org/10.3390/electronics12051123 - 25 Feb 2023

Viewed by 565

Abstract

Wideband sparse Bayesian learning (WSBL) based on joint sparsity achieves high direction-of-arrival (DOA) estimation precision when the signals share the same frequency band. However, when the signal frequency bands are non-overlapped or partially overlapped, i.e., the frequency bands are different, the performance of [...] Read more.

Wideband sparse Bayesian learning (WSBL) based on joint sparsity achieves high direction-of-arrival (DOA) estimation precision when the signals share the same frequency band. However, when the signal frequency bands are non-overlapped or partially overlapped, i.e., the frequency bands are different, the performance of the method degrades due to the improper prior on signal. This paper aims at extending the WSBL to a more general version, which is also suitable for the cases where the signal frequency bands are non-overlapped or partially overlapped. Given that the signals are sparsely distributed in the space, the signal matrix whose column is composed of the signal in each frequency bin is row-sparse. Moreover, the signal vectors in some frequency bins have different sparse supports when the signals occupy the different frequency bands. Therefore, a hierarchical sparse prior is assigned to the signal matrix, where a set of hyperparameters are used to ensure the row-sparsity and the other set are used to adjust the signal sparsity in each frequency bin. The DOAs are finally estimated in the Bayesian framework. The simulation results verify that the proposed method achieves good performance on estimation precision in both the same and different frequency band scenarios. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

A Target Detection Method of Distributed Passive Radar without Direct-Path Signal

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Huijie Zhu

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Changlong Wang

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Lu Wang

Electronics 2023, 12(2), 433; https://doi.org/10.3390/electronics12020433 - 13 Jan 2023

Cited by 1 | Viewed by 1075

Abstract

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path [...] Read more.

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path signals. In this paper, a distributed target detection method is studied under a without-direct-path signals system. In the case of without-direct-path signals, target detection is achieved by the generalized likelihood ratio test (GLRT). At the same time, the input echo signals in target detection need time synchronization, so the impact of time delay should be eliminated. However, the traditional time delay estimation method is realized through coherent processing between echoes, which requires a high signal-to-noise ratio (SNR). Therefore, a time delay estimation method is proposed in this paper. Finally, the experimental results show that the accuracy of target detection by GLRT is improved, and the signal-to-noise ratio is also improved under the condition of without-direct-path signals. Moreover, the accuracy of delay detection is improved. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessFeature PaperArticle

Fast Heterogeneous Clutter Suppression Method Based on Improved Sparse Bayesian Learning

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Electronics 2023, 12(2), 343; https://doi.org/10.3390/electronics12020343 - 09 Jan 2023

Viewed by 1073

Abstract

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method [...] Read more.

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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Open AccessArticle

Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against mDT-STAP System

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Electronics 2023, 12(1), 152; https://doi.org/10.3390/electronics12010152 - 29 Dec 2022

Cited by 2 | Viewed by 634

Abstract

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. [...] Read more.

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulation shows that numerous false targets with different amplitude and intervals can be generated by changing the retransmitted parameters. The independent identical distribution (IID) of system sample data can be destroyed after these false targets are received by the radar system, and the main lobe will be distorted when the system’s adaptive weight vector is formed. The processing performance of the mDT-STAP system is seriously degraded. The jamming method proposed based on interrupted-sampling and the non-uniform periodic repeater offers great potential for the interference research on STAP in real conditions. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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