This letter addresses the problem of adaptive target detection in the presence of possible mismatched sidelobe interfering signals assumed orthogonal to the nominal target signature in the whitened space. To this end, we devise a joint Maximum Likelihood (ML)-Bayesian based detector that simultaneously improves the target detection performance and the rejection capability of the mismatched signals. Specifically, we first inject an orthogonal interfering signal into the null hypothesis of the traditional binary hypothesis test and, then, solve it by means of the latent variable model and the Expectation Maximization algorithm. Finally, we maximize the posterior probability of the hypotheses for decision. In addition, we prove the Constant False-Alarm Rate property of the proposed detection architecture. The illustrative examples conducted on synthetic data corroborate the enhanced detection performance and rejection capability with respect to the state-of-the-art.

This letter presents a fast gridless direction-of-arrival (DOA) estimation algorithm that improves the Fast Interior-Point Method (FastIPM). The proposed algorithm effectively achieves a significant reduction in computational load for large-scale arrays while maintaining an accurate estimation. It reduces the complexity of gridless DOA estimation to$\mathcal {O}(N^{2})$per iteration (according to the Landau notation). Compared to the original FastIPM, we extend the received signal data model to account for more general scenarios that include missing array elements and arbitrary number of snapshots. By formulating the problem as an optimization with an obstacle function, we iteratively minimize the dual gap to obtain the optimal solution for atomic norm minimization problem. Extensive numerical simulations demonstrate the algorithm's computational superiority over the state-of-the-art gridless DOA estimation methods, providing excellent resolution and accuracy.

In this letter, the problem of detecting a multiple subspace-based target in the presence of deterministic interference is considered. To solve the problem, we utilize the Kullback-Leibler information criterion and model order selection rules to design detection schemes. The alternative hypothesis related to the most likely signal subspace is selected from multiple alternative hypotheses, and is tested versus the null hypothesis for target detection. Numerical examples verify the effectiveness of the proposed detection schemes, which can achieve the target detection and subspace-based target classification simultaneously.

Underwater acoustic positioning using ultrashort baseline (USBL) technology is essential for underwater navigation and ocean surveillance. Many research institutions and commercial organizations have conducted extensive studies on USBL, with wideband processing widely applied to enhance the capabilities of related methods. However, the properties of the wideband correlation spectrum have not been thoroughly explored. This study introduces a linear fitting technique, leveraging the frequency-phase relationship within the spectrum to enhance the USBL performance. By combining iterative reweighted least squares and a random sampling and consensus (RANSAC)-type approach, the method reduces the impact of outliers, particularly in multiple-path and low signal-to-noise ratio (SNR) scenarios. Furthermore, we operate a matched filter before the linear fitting process and suggest rescreening based on the consistency of all array elements in the frequency domain to eliminate more outliers. The proposed method significantly reduces positioning errors in both numerical simulations and sea trial data processing.

In this letter, we consider the problem of detecting a multichannel subspace signal in the presence of deterministic interference and structure nonhomogeneity. We derive the gradient test, Durbin test, and their two-step (2S) variants. The gradient test and its 2S variant have the same form as the existing generalized likelihood ratio test for the same detection problem, whereas the Durbin test and its 2S variant are new detectors. Numerical examples show that the two proposed new detectors, i.e., the Durbin test and its 2S variant, can provide better detection performance in some scenarios. In particular, they are robust to signal mismatch, and can perform well when the structure nonhomogeneity is not serious.

This letter deals with the problem of clutter edge detection and localization in training data. To this end, the problem is formulated as a binary hypothesis test assuming that the ranks of the clutter covariance matrix are known, and adaptive architectures are designed based on the generalized likelihood ratio test to decide whether the training data within a sliding window contains a homogeneous set or two heterogeneous subsets. In the design stage, we utilize four different covariance matrix structures (i.e., Hermitian, persymmetric, symmetric, and centrosymmetric) to exploit the a priori information. Then, for the case of unknown ranks, the architectures are extended by devising a preliminary estimation stage resorting to the model order selection rules. Numerical examples based on both synthetic and real data highlight that the proposed solutions possess superior detection and localization performance with respect to the competitors that do not use any a priori information.

This letter addresses the adaptive classification of the Interference Covariance Matrix (ICM) structures in radar applications. This is an essential issue when the design assumptions do not perfectly match the actual operating scenario due to environment uncertainties. Thus, in this letter, we propose a classifier capable of identifying the ICM structure as either complex Hermitian or real-valued symmetric. To this end, a Bayesian approach is employed by assuming a suitable model for the probability density function of the unknown ICM. This classification problem is firstly formulated in terms of a binary hypothesis test and the posterior probability is maximized to devise the classifier. Furthermore, the classifier resorts to secondary data only which are obtained from the adjacent cells around the cell under test and share the same ICM structure as the primary data. The illustrative examples conducted on simulated data have confirmed the superiority of the proposed classifier compared with its state-of-the-art non-Bayesian counterparts.

In this letter, we deal with the problem of high-resolution time delay estimation (TDE) in multipath environments exploiting the matched filter (MF) outputs data. To this end, we develop a systematic post-processing framework, consisting of two sparsity-based algorithms and a refining procedure aimed at reducing the computational load. The TDE problem is formulated as a sparse signal recovery problem and efficiently solved resorting to a majorization-minimization paradigm and a cyclic procedure. At the design stage, we assume a complex-valued Gaussian distribution model for the MF samples and incorporate a module-product prior that promotes the sparsity more significantly than the conventional complex Laplacian distribution. The preliminary performance assessment, conducted on simulated data, shows that, at least for the considered parameter values, the proposed delay estimators approach the Cramér-Rao bound for different signal-to-noise ratios and bandwidths.

This letter addresses the detection problem of dim maneuvering targets in the presence of range cell migration. Specifically, it is assumed that the moving target can appear in more than one range cell within the transmitted pulse train. Then, the Bayesian information criterion and the generalized likelihood ratio test design procedure are jointly exploited to come up with six adaptive decision schemes capable of estimating the range indices related to the target migration. The computational complexity of the proposed detectors is also studied and suitably reduced. Simulation results show the effectiveness of the newly proposed solutions also for a limited set of training data and in comparison with suitable counterparts.

This letter focuses on adaptive target detection in the presence of multiple interference sources, which comprise clutter, thermal noise, noise-like jammers, and fully-correlated (or coherent) signals. In order to account for different operating scenarios, we formulate the problem at hand in terms of a multiple hypothesis test with several alternative hypotheses representative of each considered scenario. In this context, we devise a family of two-stage detection architectures capable of classifying the specific scenario and, hence, of working under different operating conditions. The performance analysis shows the effectiveness of the detector based upon the Generalized Information Criterion also in comparison with traditional adaptive decision schemes.

Towed array shape estimation aided with non‐acoustic sensors is widely used for its rather low computational complexity of solutions and rather explicit results. In addition, previous studies have emphasised that depth sensors’ distribution has a dramatic influence on the accuracy of this kind of array shape estimation method. Established on the basic theory of towed array shape estimation using Kalman Filters, the approximately optimal distribution of a certain number of depth sensors over a certain number of discretised towed arrays yields the approximately best achievable performance in terms of minimum space average mean square error (AMSE), is addressed in this study. The effect of depth sensors’ distribution has been discussed. Then an exact expression for the space AMSE is derived. The expression is simplified in a reasonable way considering the practical issues in order to calculate the minimum space AMSE rapidly and effectively. The performance assessments demonstrate the effectiveness of the newly proposed method.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

In this paper, we address the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. To this end, we exploit the spectral properties of the clutter to transfer the binary hypothesis test problem from complex domain to real domain. Then, we devise and assess a detection strategy based on the so-called two-step Generalized Likelihood Ratio Test (GLRT) design procedure. Finally, a preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterparts which ignore the spectrum symmetry.

We address adaptive radar detection of targets embedded in clutter characterized by a symmetrically structublack power spectral density (PSD) and persymmetric covariance matrix. At the design stage, such properties are jointly exploited to come up with decision schemes capable of guaranteeing superior detection performances with respect to architectures which incorporate either persymmetry or clutter PSD symmetry. The performance analysis, both on simulated and on real radar data, confirms the superiority of the newly proposed architectures over their natural counterparts which do not take advantage of both the sources of a priori information.

In the present letter we investigate the problem of adaptive detection and range estimation for point-like targets buried in partially homogeneous Gaussian disturbance with unknown covariance matrix. To this end, we jointly exploit the spillover of target energy to consecutive range samples and the oversampling of the received signal. In this context, we design a detector relying on the Generalized Likelihood Ratio Test (GLRT). Remarkably, the new decision scheme ensures the Constant False Alarm Rate (CFAR) property with respect to the unknown disturbance parameters. The performance analysis reveals that it can provide enhanced detection performance compared with its state-of-art counterpart while retaining accurate estimation capabilities of the target position.

In this work we propose an adaptive receiver with enhanced range estimation capabilities, which jointly exploits the over-sampling of the noisy returns and the spillover of target energy to adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the Generalized Likelihood Ratio Test (GLRT) is derived and assessed. The performance analysis highlights that better detection performances and increased range estimation accuracies can be achieved exploiting the oversampling at the price of an additional processing cost.

In this work, we devise an adaptive decision scheme with range estimation capabilities for point-like targets in partially homogeneous environments. To this end, we exploit the spillover of target energy to consecutive range samples and synthesize the Generalized Likelihood Ratio Test. The performance analysis, conducted resorting to both simulated data and real recorded datasets, highlights that the newly proposed architecture can guarantee superior detection performance with respect to its competitors while retaining accurate estimation capabilities of the target position.

This letter deals with the problem of adaptive detection in partially-homogeneous Gaussian disturbance with unknown but persymmetric structured covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test and the Wald test design criteria. Remarkably, both detectors ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detectors.

In this paper we consider the problem of detecting multiple point-like targets in the presence of steering vector mismatches and Gaussian disturbance with unknown covariance matrix. To this end, we first model the actual useful signal as a vector belonging to a proper cone whose axis coincides with the whitened direction of the nominal array response. Then we develop two new robust adaptive detectors resorting to the two-step generalized-likelihood ratio test (GLRT) design procedure without assignment of a distinct set of secondary data. Finally, a performance assessment, conducted by Monte Carlo simulation, show that the proposed detectors achieve a visible performance improvement over their natural counterparts.