Compressive sensing (CS) technology has proven highly effective in rapid data acquisition and super-resolution target imaging for through-the-wall radar imaging (TWRI) applications. However, most existing CS-based TWRI techniques focus primarily on high-quality imaging of behind-wall targets, often neglecting the reconstruction of building layouts, which is essential for determining the relative positions of targets in unknown environments. To address this limitation, a structural-prior-guided CS framework is proposed for the joint reconstruction of building layouts and behind-wall targets. Specifically, first, distinct imaging models are developed for layouts and targets, accounting for their unique structural properties: layouts, referring to wall structures, typically manifest as extended, piecewise-continuous line-like structures, whereas targets manifest as compact, point-like structures. Building on these models, a unified constrained optimization problem is formulated by integrating (i) the strong inter-channel correlation of layout echoes, enforced via a low-rank regularization on the layout component, and (ii) structured sparsity priors tailored to both the layout and target images. Then, the resulting composite problem is efficiently solved using proximal gradient algorithm, yielding simultaneous reconstruction of the unknown building layouts and behind-wall targets. Finally, simulations and experimental results demonstrate the effectiveness of the proposed algorithm.

Multi-target tracking (MTT) in real-world environments often faces the challenge of outlier measurements, which severely degrades the performance of standard MTT algorithms. This paper integrates the maximum correntropy criterion (MCC) into the Gaussian mixture probability hypothesis density (GM-PHD) filter, an implicit data association and a highly efficient MTT algorithm. The MCC provides a localized similarity measure that is inherently resilient to impulsive outliers. We embed an iterative fixed-point measurement update for the GM-PHD filter, and an adaptive kernel size design strategy is also devised. The proposed MCC-GM-PHD filter effectively suppresses the influence of large measurement residuals, while maintaining a closed-form Gaussian mixture representation. The performance of the proposed MCC-GM-PHD filter is verified via simulations.

Real-world noise often exhibits complex non-Gaussian characteristics, frequently manifesting as heavy-tailed distributions contaminated by outliers. The effectiveness of filtering algorithms can vary significantly across these diverse noise types. To this end, this letter presents an adaptive filtering algorithm based on the Student's-t mixture model (SMM). The proposed approach systematically employs SMM to model the noise distribution, optimizes the mixture model's hyper-parameters via the Expectation-Maximization (EM) algorithm, and ultimately derives the filter by maximizing the log-likelihood function of the parameterized SMM. Leveraging the inherent heavy-tailed properties of SMM, the algorithm demonstrates strong robustness against extreme outliers and is suitable for a wide range of noise environments. Simulation results confirm its superior performance under various noise disturbances.

In order to address issues such as convergence rate, stability, and computational complexity caused by the identification of long length impulse response systems, an effective nearest Kronecker product (NKP) decomposition strategy has been introduced and extended to various adaptive filters in recent years. However, the theoretical performance of the NKP decomposition-based adaptive filtering algorithms has not been thoroughly analyzed in these studies. In this letter, we focus on analyzing the steady-state performance of the NKP-based least mean square (NKP-LMS) algorithm and presents the theoretical upper bound of the step-size. Finally, simulation results confirm the precision of the theoretical assessment of the NKP-LMS algorithm and highlight its benefits in low-rank system identification.

In this letter, we propose a collaborative neural network framework for acoustic echo cancellation (AEC) tasks, where echo paths and spatial information are efficiently modeled through a two-stage subnetwork cascade to jointly repair the complex spectrum of the target speech. Specifically, we design a two-path structure consisting of a real part and an imaginary part as well as an amplitude phase, a lightweight network module to suppress part of the echo and potential noise in the first stage, and a larger network to estimate the complex residuals for phase correction and spectral restoration in the second stage. In order to maximize the performance of the model at each stage, we propose two convolutional modules: an inplace gate convolutional module and a complex squeezed temporal convolutional module (CSTCM). In addition, a cross-domain loss function is designed to improve the generalization capability. Experiments are conducted under various mismatch scenarios, and the results show that the proposed dual-path staging method provides superior performance over other advanced methods.

With the emergence of complex correntropy, the maximum complex correntropy criterion (MCCC) has been applied to the complex-domain adaptive filtering. The MCCC uses the fixed point method to find the optimal solution, which provides good robustness in the non-Gaussian noise environment, especially for the impulse noise. However, the convergence analysis for the fixed point method is limited to the real-domain filtering. In this letter, we provide the convergence analysis of fixed point based MCCC algorithm in complex-domain filtering. First, by using the matrix inversion lemma, we rewrite the MCCC algorithm to a gradient-like version. In addition, we provide two computationally efficient versions of MCCC. Then, we provide the stability analysis and obtain the excess mean square error for MCCC. Finally, simulation results confirm the correctness of the convergence analysis in this letter.

This paper presents a novel nonlinear adaptive filter method, namely, Hammerstein adaptive filter with single feedback under minimum mean square error (HAF-SF-MMSE). A single delayed output is incorporated into the estimation of the current output based on minimum mean square error criterion, and therefore the history information of output is considered. Moreover, hybrid learning rates and adaptive learning rates with normalization factors are designed to guarantee the convergence and stability of HAF-SF-MMSE. Compared with the traditional Hammerstein adaptive filter which usually consists of a nonlinear filter followed by a linear part, HAF-SF-MMSE can achieve a faster convergence rate and higher filter accuracy. Theoretical analysis regarding convergence behavior is performed to acquire a sufficient condition on the convergence of weight. Simulation results show the excellent filtering performance of the proposed HAF-SF-MMSE.

Channel identifiability for multiple-input multiple-output space-time block code MIMO-STBC systems using Joint Approximate Diagonalization of Eigenmatrices JADE is studied in this paper. Compared with the previous blind MIMO-STBC channel estimation methods in literature, the method proposed in this paper is more suitable for non-cooperative scenario because it needs less prior information and can be applied to a general class of STBCs. The main contribution of the paper consists in the theoretical proof that, although the sources transmitted by different antennas of MIMO-STBC systems are not independent, they can be retrieved from the received data by directly using JADE in most cases. The conclusion is also demonstrated by a simulation. This shows that the classical JADE algorithm can be applied to a wider range of situations rather than strictly independent sources. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY Blind parameter identification is a research topic of high importance for both military and civilian communication systems. To our knowledge, there is no report in literature on blindly recognizing the modulation of noncooperative MIMO systems in association with space–time block code where channel state information and coding matrix are unavailable. In this paper, we first present a classifier based on maximum likelihood on the condition of virtual channel matrix; second, the modulations are classified into two classes according to the independence of the source signals: independent and groupwise independent constellations. In the next step, a multidimensional independent component analysis algorithm is proposed by utilizing the block‐diagonal structure of the cumulant matrices to estimate the virtual channel matrix for these two cases, respectively. Last, the ambiguities are removed partly, and the classifier is proven to be insensitive to the remaining indeterminacy. Parallel computation technique is adopted to accelerate the computation of the logarithm likelihood function. Simulations show that our algorithm can work with high recognition probabilities in noncooperative space–time block code communication systems. Copyright © 2013 John Wiley & Sons, Ltd.