High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

For high-frequency surface wave radar (HFSWR), the unwanted radio wave originated from the ionosphere is commonly called ionospheric clutter. Its presence seriously affects the performance of the HFSWR and extremely degrades the radar capability to detect target over long distances. To solve this problem, this paper proposes a two-step method for ionospheric clutter mitigation with two-dimensional dual-polarized received array. The proposed method first performs parameter estimation of the clutter, and then gives a polarimetric-adaptive-based oblique projection filter (PAB-OPF) to suppress the clutter. In the first step, due to the fact that vertically and horizontally polarized antennas are at different array elements for reducing mutual coupling and hardware cost, 2-D DOAs are estimated to give phase compensation for polarization phase delay estimation. In the second step, the PAB-OPF is proposed to eliminate phase inconsistency by matching different polarization phase delays for different vertically polarized antennas, and then to suppress the clutter efficiently in space-polarization domain. Error analysis and computational complexity of the proposed method are derived. Experimental results are shown to illustrate the superiority of the proposed method for ionospheric clutter suppression.

We study the convergence of pairwise gossip algorithms and broadcast gossip algorithms for consensus with intermittent links and mobile nodes. By nonnegative matrix theory and ergodicity coefficient theory, we prove gossip algorithms surely converge as long as the graph is partitionally weakly connected which, in comparison with existing analysis, is the weakest condition and can be satisfied for most networks. In addition we characterize the supremum for the mean squared error of convergence as a function associated with the initial states and the number of nodes. Furthermore, on the condition that the graph is partitionally strongly connected, the rate of convergence is proved to be exponential and governed by the second largest eigenvalue of expected coefficient matrix. For partitionally strongly connected digraphs, simulation results illustrate that gossip algorithms actually converge, and broadcast gossip algorithms can converge faster than pairwise gossip algorithms at the cost of larger error of convergence.

High-resolution range profile (HRRP)-based radar automatic target recognition (RATR) is crucial in capturing target structural characteristics across all-day and all-weather environments. However, existing HRRP-based RATR methods struggle to adapt to diverse target aspects and backscattering characteristics due to insufficient modeling of spatial structures and spectral periodicity. To address these limitations, we propose the MSHP-Net, a multi-scale hybrid perception network with granularity decoupling and spectral enhancement for HRRP target recognition. The MSHP-Net employs a multi-scale hybrid perception (HP) encoder to jointly capture spatial-spectral domain features by combining spatial feature decoupling and spectral feature recalibration. Specifically, we extract multi-granularity spatial features and decouple them into granularity-invariant and granularity-variant components based on an adaptive singular value decomposition (SVD). This process explicitly enhances structural consistency and preserves fine-grained variations, effectively modeling varying target aspects and mitigating structural distortions inherent in HRRP data. To capture global spectral correlations and periodic scattering characteristics, we recalibrate the spectral distribution and apply spectrally enhanced attention, emphasizing the critical spectral bands and suppressing background noise. To promote multi-scale hybrid features interaction, we introduce a hierarchical affinity-guided gating to propagate cross-scale relevant information flow, balancing low-level details with high-level semantics for more comprehensive feature representations. Finally, we aggregate scale-wise features and predict the final classification. Comparative experiments on both simulated and measured datasets validate the effectiveness of the proposed network.

To achieve accurate direction parameter estimation of sensor array, mutual coupling between sensor elements should be properly handled. This problem becomes more challenging when the sources are coherent. In this paper, we derive an importance resampling approach (IRA) for joint mutual coupling and DOA estimation. Aiming to DOA estimation of coherent sources using the spatial smoothing technique and MUSIC algorithm, error analysis is first provided for the estimate of the corresponding mutual coupling coefficient matrix in a uniform linear array setting. An optimization formulation based on MUSIC is then established for joint estimation of mutual coupling coefficients and DOAs. The proposed IRA framework, which solves this optimization problem, has the advantages of few parameters and low computational complexity. Four different importance resampling algorithms which are the position resampling algorithm, the number resampling algorithm, the number and position resampling algorithm and the zero forcing resampling algorithm are proposed for the implement of the IRA framework with different arrangement and combination according to the characteristic of the real coupling coefficients vector. The proposed approach is effective not only to the case of coherent sources, but also for independent sources, and it can work in both single-and multiple-snapshot scenarios. The excellent performance of our methods for mutual coupling correction and DOA estimation is demonstrated via extensive simulation results, which can also be applied to other sensor parameter estimation scenarios.

This paper integrates two data-driven learnable modules into the conventional distributed multi-sensor resolvable group target tracking (RGTT) framework based on the labeled multi-Bernoulli (LMB) filter to improve tracking performance. First, in RGTT scenarios with densely distributed targets, the LMB filter is prone to track fragmentation and label switching, which reduce accuracy and complicate multi-sensor data association. While some studies have attempted to address this, their methods typically overlook the group structure and are often offline and computationally intensive. To overcome this, we propose a track optimization module based on attention mechanism that alleviates these issues efficiently in an online setting. Second, we replace traditional data association algorithms, which rely on fixed similarity metrics, with a track matching module based on message passing network (MPN). This module leverages historical track data and learns to infer latent similarities across sensors, significantly improving matching accuracy. Simulation results demonstrate the effectiveness of the proposed modules.