Byzantine-robust federated learning (FL) aims to obtain an accurate global model even with potentially Byzantine users. However, most existing schemes rely on measuring the overall differences between the entire gradient vectors of different users, which fail to effectively distinguish malicious gradients from benign ones caused by data heterogeneity under non-IID settings, thereby compromising model performance. To tackle this challenge, we propose BPFLH, a novel Byzantine-robust privacy preserving FL framework for heterogeneous data. BPFLH is the first to introduce Bray–Curtis dissimilarity into FL, capturing the element-wise differences among gradients from different users. This method reduces the risk of misclassifying benign gradi ents as malicious and enhance the model's robustness against Byzantine attacks in non-IID data environments. Furthermore, BPFLH leverages CKKS homomorphic encryption to protect local gradients, enabling secure aggregation and Byzantine user detection without compromising privacy. Extensive experiments on real-world datasets under various attack scenarios and data distributions demonstrate that BPFLH exhibits strong robustness against Byzantine attacks while preserving privacy and maintaining superior accuracy compared to existing Byzantine-robust FL methods, particularly in non-IID environments.

In through-wall indication of a moving human target in enclosed structures, a shadow effect because of the human target blocking parts from illumination on the back wall will emerge, referred to as a “ghost” in indication results. The shadow ghost moves as the human target does, which makes causal change detection (CD) invalid to separate them. To mitigate the shadow ghost, we analyze its differences from the moving human target. Based on the difference that the illumination is only blocked in partial channels of the multiple-input–multiple-output (MIMO) array while target echoes exist in most channels and the fact that shadow ghosts overlap more between successive indication results than the imaged targets as a result of their larger size, we proposed a mitigation method including a coherence factor and noncausal CD processing. Through-wall experiments via a MIMO through-wall imaging radar validate the proposed method.

Soon after its innovation, compressive sensing (CS) was rapidly applied to radar imaging. However, the huge computational complexity and the memory requirements have become the bottlenecks in its widespread applications to large-scale and real-time radar imaging. In this paper, two novel methods based on fast Gaussian gridding nonuniform fast Fourier transform are proposed to speed up CS radar imaging and reduce the memory requirement. By using the proposed methods, the application of CS imaging method can be extended to large-scale and real-time radar imaging with high reconstructing efficiency and small memory requirement. Theoretical analysis and numerical results from the aspects of accuracy, efficiency, and memory requirement validate the proposed methods. Simulation and real data imaging results by spectral projection gradient ℓ1-norm method are given to further demonstrate the efficiency of the proposed methods.

Through-wall indication of human targets is highly desired in many applications. Generally, human targets behind wall are noncooperative, and rare prior knowledge about the circumstance behind wall could be available. Thus, it requires the ability to indicate human targets with different motions from clutters. To investigate this problem, we first examine the conventional time-domain indication methods, and find that their performances are controlled by the historical pulse number adopted to estimate background, which corresponds to the tap-length from the angle of filter. Then, based on an intermittent mode of human target echoes, we define the optimum tap-length as the shortest tap-length that makes the filter output signal-to-clutter-and-noise ratio reach maximum and develop an adaptive indication method with a gradient tap-length control scheme to search the optimum tap-length. Finally, through-wall experiments with an impulse through-wall radar demonstrate that the proposed method can obtain a good adaptive indication performance on human target with different motions.