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In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios.

A unitary transformation-based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals. The problem is solved by reorganizing the covariance matrix into a block Hankel one for decorrelation first and then reconstructing a new matrix to facilitate the unitary transformation. By multiplying unitary matrices, eigenvalue decomposition and singular value decomposition are both transformed into real-valued, so that the computational complexity can be reduced significantly. In addition, a fast and computationally attractive realization of the 2-D unitary transformation is given by making a Kronecker product of the 1-D matrices. Compared with the existing 2-D algorithms, our scheme is more efficient in computation and less restrictive on the array geometry. The processing of the received data matrix before unitary transformation combines the estimation of signal parameters via rotational invariance techniques (ESPRIT)-Like method and the forward-backward averaging, which can decorrelate the impinging signalsmore thoroughly. Simulation results and computational order analysis are presented to verify the validity and effectiveness of the proposed algorithm.

Active sonar systems are one of the most commonly used acoustic devices for underwater equipment. They use observed signals, which mainly include target echo signals and reverberation, to detect, track, and locate underwater targets. Reverberation is the primary background interference for active sonar systems, especially in shallow sea environments. It is coupled with the target echo signal in both the time and frequency domain, which significantly complicates the extraction and analysis of the target echo signal. To combat the effect of reverberation, an attention and cepstrum analysis-guided network (ACANet) is proposed. The baseline system of the ACANet consists of a one-dimensional (1D) convolutional module and a reconstruction module. These are used to perform nonlinear mapping and to reconstruct clean spectrograms, respectively. Then, since most underwater targets contain multiple highlights, a cepstrum analysis module and a multi-head self-attention module are deployed before the baseline system to improve the reverberation suppression performance for multi-highlight targets. The systematic evaluation demonstrates that the proposed algorithm effectively suppresses the reverberation in observed signals and greatly preserves the highlight structure. Compared with NMF methods, the proposed ACANet no longer requires the target echo signal to be low-rank. Thus, it can better suppress the reverberation in multi-highlight observed signals. Furthermore, it demonstrates superior performance over NMF methods in the task of reverberation suppression for single-highlight observed signals. It creates favorable conditions for underwater platforms, such as unmanned underwater vehicles (UUVs), to carry out underwater target detection and tracking tasks.

Dolphin signals are effective carriers for underwater covert detection and communication. However, the environmental and cost constraints terribly limit the amount of data available in dolphin signal datasets are often limited. Meanwhile, due to the low computational power and resource sensitivity of Unmanned Underwater Vehicles (UUVs), current methods for real-time generation of dolphin signals with favorable results are still subject to several challenges. To this end, a Masked AutoEncoder Generative Adversarial Network (MAE-GAN) model is hereby proposed. First, considering the few-shot condition, the dataset is extended by using data augmentation techniques. Then, to meet the low arithmetic constraint, a denoising autoencoder with a mask is used to obtain latent codes through self-supervised learning. These latent codes are then utilized in Conditional Wasserstein Generative Adversarial Network-Gradient Penalty (CWGAN-GP) to generate a whistle signal model for the target dataset, fully demonstrating the effectiveness of the proposed method for enhancing dolphin signal generation in data-limited scenarios. The whistle signals generated by the MAE-GAN and baseline models are compared with actual dolphin signals, and the findings indicate that the proposed approach achieves a discriminative score of 0.074, which is 28.8% higher than that of the current state-of-the-art techniques. Furthermore, it requires only 30.2% of the computational resources of the baseline model. Overall, this paper presents a novel approach to generating high-quality dolphin signals in data-limited situations, which can also be deployed on low-resource devices. The proposed MAE-GAN methods provide a promising solution to address the challenges of limited data and computational power in generating dolphin signals.

This paper focuses on the method of an adaptive detector which works against Gaussian background with unknown parameters, such as covariance matrix. The authors design a tunable detector for point‐like targets by mixing the Kelly's generalized likelihood ratio test and the enhanced Rao test. The analytical expression of the false alarm probability and detection probability of the proposed detector are derived. The performances of the new scheme are assessed, and analysed in comparison with its natural counterparts. The results show that it can provide enhanced rejection capabilities of mismatched signals, at the price of a limited detection loss for matched signals.

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

In this paper, we focus on the design of adaptive receivers for nonhomogeneous scenarios. More precisely, at the design stage we assume a mismatch between the covariance matrix of the noise in the cell under test and that of secondary data. Under the above assumption, we show that the Wald test is the adaptive matched filter, while the Rao test coincides with the receiver obtained by using the Rao test design criterion in homogeneous environment, hence providing a theoretical explanation of the enhanced selectivity of this receiver.

The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model’s parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation.

This paper addresses the degradation in direction of departure (DOD) and direction of arrival (DOA) estimation performance caused by array mutual coupling in bistatic MIMO sonar systems. A joint DOD-DOA estimation algorithm based on smoothed Atomic Norm Minimization (ANM) is proposed. The method mitigates the effects of array mutual coupling by applying a decoupling smoothing matrix to preprocess the received signals. The processed signals exhibit a structure resembling line spectral signals, which allows for the formulation of an ANM optimization problem. By exploiting the inherent sparsity of the signals, the proposed algorithm effectively reconstructs a high signal-to-noise ratio (SNR) covariance matrix, even with a limited number of snapshots, enabling accurate estimation of DOD and DOA. Simulation results validate the algorithm’s performance under mutual coupling conditions in MIMO sonar transmit and receive arrays. The algorithm maintains high estimation accuracy even with a limited number of received snapshots, making it suitable for rapid target localization in real underwater detection applications. Compared to existing gridless sparse recovery methods for covariance matrix estimation, the proposed algorithm achieves lower computational dimensions and faster execution. Additional simulation experiments further demonstrate the effectiveness and superiority of the proposed method under varying SNR and target angle separation conditions.

Underwater object detection is a fundamental task in underwater sensing and is generally approached using either optical or sonar sensors. Although optical imaging provides rich semantic information, it is highly susceptible to water turbidity and illumination variations. By contrast, sonar imaging can effectively overcome visibility limitations, yet it suffers from severe speckle noise and blurred object contours. Moreover, resource-limited platforms impose strict demands on model lightweightness and real-time performance. To this end, this paper proposes a novel cross-modal heterogeneous distillation method (CMHD) to balance detection accuracy and computational complexity. CMHD performs cross-modal knowledge transfer by leveraging the rich semantics of RGB images to enhance sonar feature representation, compensating for the information deficiency of the sonar modality. Meanwhile, a heterogeneous distillation scheme compresses the detection capability of a high-capacity teacher YOLOX-M into a lightweight student YOLOX-S-Ghost, enabling strong feature extraction under a highly compact model. To mitigate the modality gap and geometric inconsistency between RGB and sonar modalities, we design a branch-aware heterogeneous distillation strategy. To improve detection accuracy and reduce model parameters, the student network incorporates Coordinate Attention (CA) in its backbone and adopts a lightweight neck design. Experiments on the UXO† dataset demonstrate that CMHD achieves 79.6% mAP and 82.6% mAR, significantly outperforming the compared representative methods and serving as an accurate, efficient, and lightweight solution for underwater sonar object detection.