Wireless sensing technologies are crucial for a wide range of real-world applications, from indoor navigation and smart home automation to autonomous vehicle perception and industrial monitoring. While dedicated mmWave radar systems offer high-precision spatial measurements, they encounter deployment challenges due to their high cost and power consumption. In contrast, Wi-Fi-based sensing leverages existing communication infrastructure, offering cost-effective alternatives via channel state information (CSI) in 2.4 GHz systems and beamforming in mmWave Wi-Fi (60 GHz). However, traditional Wi-Fi sensing provides lower spatial fidelity than radar systems. This study introduces Wi-Fi Conditioned Radar point-cloud Generation (WiC-RadGen), which closes this performance gap by generating radar-quality point clouds from dual-band Wi-Fi signals. Our approach employs a two-stage architecture: initially, a discrete variational autoencoder with specialized loss functions (Chamfer distance, annealed KL divergence, and cluster loss) tokenizes sparse radar point clouds while preserving geometric coherence; subsequently, a conditional masked autoregressive transformer integrates 2.4 GHz CSI and 60 GHz beam SNR through cross-attention mechanisms for sequential token prediction. Technical innovations include an empty-token predictor with a differential Neyman–Pearson loss that efficiently identifies uninformative regions, focal loss to address class imbalance, and iterative unmasking for high-fidelity generation. Experimental validation demonstrates competitive localization accuracy approaching mmWave radar performance while retaining Wi-Fi’s deployment advantages. WiC-RadGen advances wireless sensing by enabling radar-precision applications through ubiquitous Wi-Fi infrastructure, reducing deployment costs without compromising spatial fidelity.

Due to changes in sea conditions and radar parameters, the statistical parameters of sea clutter can obviously change. The neural network (NN) trained on the specific statistical parameter dataset (source domain) will face significant performance degradation in other statistical parameter datasets (target domain). Unsupervised domain adaptation (UDA) can effectively mitigate this problem by making the two domain distributions consistent in the feature space, without supervised information from the target domain. However, the similarity between clutter (e.g., sea spike) and the target leads to category confusion when aligning the two domains globally. Therefore, we propose a category-level joint loss (CJL) consisting of cross-entropy, local maximum mean discrepancy (LMMD), and git loss in our UDA radar target detection framework. Among them, based on the pseudo-labels provided by the NN during the training process, the LMMD is used to reduce the feature distance of the same category between two domains. Furthermore, we adopt git loss to increase the depth feature distance of interclass samples, while encouraging the aggregation of intraclass features. Finally, we analyze the performance loss of NN detectors and demonstrate that the proposed method has the best alignment and classification performance compared to existing methods on the constructed shore-based radar datasets.

Icebergs threaten the navigation safety of ships in the Arctic, small icebergs are difficult to detect in harsh sea conditions. Timely detection of these targets is crucial for aiding ships in navigational decisions. Infrared detection technology is an effective solution for detecting icebergs. Due to the low contrast of iceberg infrared images and severe sea wave clutter, current methods can not suit iceberg detection tasks well. In this article, we propose an Adaptive Multi-scale Enhanced Interaction Network (AMEINet) and construct the Single-Frame Infrared Iceberg Target Dataset (SFIIT). First, the Global Contrast Enhance Module (GCE) is designed, which adaptively enhances the image with the target as the center, and reconstructs the potential features submerged by low contrast. Then, to obtain rich features, the Boundary Feature Extraction Module (BFE) and Gated Context Feature Extraction Module (GCFE) are designed to extract target information at different levels. BEF adopts a new computation method to obtain the edge contour structures without edge GroundTruth supervision. GCFE effectively controls redundant information transmission through the gate mechanism and jump connection, filtering out the false alarms and miss detection caused by strong wave clutter. Finally, experiments on the publicized SIRST dataset and proposed SFIIT dataset show that the proposed method can achieve superior performances in infrared iceberg detection.

Infrared small target detection technology has been widely used in various fields. Infrared small target usually shows the characteristics of low gray values and a lack of texture information in complex scenes, which makes it difficult to accurately segment the boundary of infrared small target. To solve this problem, we propose an edge-aided multiscale context network (EAMCNet) in this letter. In the proposed network, we design a two-stream architecture for target detection that considers edge information as a separate processing branch, the edge detection stream processes information in parallel to the target segmentation stream. To emphasize features for different scale of targets, we introduce a gate-based multiscale context information extraction (GMCIE) module to regulate contextual features transmission. Finally, edge features and semantic features are fused by feature fusion module to make full use of their complementarity. Experiments on the SIRST dataset show that the proposed method can achieve excellent performances compared with the state-of-the-art methods.

Compared with the classical constant false alarm ratio (CFAR) detector, the neural network (NN) based detector has data-driven representation learning ability, which can improve the detection performance of weak targets in a nonhomogeneous environment. Under the extreme sample imbalance scenario (ESIS) for marine radar, it is difficult to control the probability of false alarm (PFA) by a variable threshold for the network output by adopting the cross-entropy loss function. The Neyman Pearson (NP) criterion is used to find the optimal detector under the constraint of PFA in radar detection and may be used as a loss function for NN to realize false-alarm-controllable detection. However, it is nondifferentiable and cannot be used directly for NN. Therefore, firstly, we theoretically deduce two differentiable-NP loss functions for NNs under the ESIS to realize the NP criterion approximately. Secondly, we theoretically analyze the differentiable-NP loss under the ESIS from the gradient perspective. Thirdly, based on the differentiable-NP loss, we achieve the false-alarm-controllable detection under the ESIS by utilizing a lightweight U-Net segmentation network. Fourthly, to improve the PFA control capability of the segmentation network, we adopt a smaller fixed-size label for each target to reduce the influence of target random size, and we dynamically adjust the regular loss term to diminish the deviation caused by the nondifferentiable operation. The experimental results show that the proposed method can accurately control PFA and get a better detection performance compared to other methods in the measurement data from marine navigation radar.

Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.

In high-frequency over-the-horizon radar (OTHR), it is a challenging work to detect targets in the nonhomogeneous range-Doppler (RD) map with multitarget interference and sharp/smooth clutter edges. The intensity transition of the clutter edge may be sharp or smooth due to the coexistence of atmospheric noise, sea clutter, and ionospheric clutter in OTHR. The analysis of the RD map shows the spatial correlation among neighboring cell-under-test (CUT) that varies from clutter to clutter. This article proposes an algorithm that uses the spatial relationship to estimate the statistical distribution parameters of every CUT by the adaptive tight frame and the weighted group-sparsity regularization. In the proposed algorithm, the spatial relationship is formulated mathematically by regularization terms and combined with the log-likelihood function of CUTs to construct the objective function. The proposed algorithm is verified by the simulated data and real RD maps collected from both trial sky-wave and surface-wave OTHRs in which it shows robust and improved detection.

Wide range covering, strong intensity, time-variant, fluctuation and irregular distribution of the spread E, F layer ionospheric clutter badly affects the system performance of High Frequency Surface Wave Radar (HFSWR). A spread E, F layer ionospheric clutter identification method is proposed based on the region segmentation results and region characteristics of the clutter. First of all, convolution template is used for locating the edge of the clutter, then the ratio of the number of the samples belonging to some segmented region and the total number of the samples in the region of interest (ROI) is used for setting the determinative threshold of the clutter region. Experiments with real data manifest that the proposed method can describe the effect of the spread ionospheric clutter to HFSWR. The quantitative analysis is consistent with the real data observation. The result can be used as a worthwhile reference for clutter mitigation, carrier frequency selection or radar system evaluation.