Jamming attacks can severely disrupt the communications in wireless sensor networks (WSNs). Accurate jammer localization is crucial for the implementation of active anti-jamming strategies. Existing localization methods primarily target the proactive jammer equipped with an omnidirectional antenna within the WSN area, which limits their effectiveness in more complex scenarios. To bridge this capability gap, an accurate localization method based on signal strength map (ALSSM) is proposed to locate the jammer, regardless of whether it is proactive or reactive, and irrespective of its antenna type or location. The ALSSM method constructs a jamming signal strength (JSS) map using JSSs collected by sensor nodes via a deception strategy. This map is then utilized to formulate an evaluation metric to quantify localization estimation errors. Therefore, the localization task is transformed into an optimization problem. This method is convenient to implement, as it requires no additional dedicated hardware, and the collected JSS data can be integrated into the WSN’s normal data transmission process. Simulation and experimental results demonstrate that the ALSSM method can accurately locate the jammer, regardless of whether it is directional or omnidirectional, and irrespective of its deployment inside or outside the WSN area.

Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. In low-altitude environments, where unmanned aerial vehicle (UAV)-borne FMCW radars are increasingly used, mutual interference poses significant challenges to radar performance. In this article, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed using an end-to-end transformer-based structure. In the RIMformer, a dual multihead self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. In addition, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. Extensive simulations and empirical measurements demonstrate that RIMformer significantly improves interference mitigation and signal recovery, which advances the reliability and effectiveness of UAV-borne FMCW radars in complex environments.

To significantly enhance the performance of point cloud semantic segmentation, this manuscript presents a novel method for constructing large-scale networks and offers an effective lightweighting technique. First, a latent point feature processing (LPFP) module is utilized to interconnect base networks such as PointNet++ and Point Transformer. This intermediate module serves both as a feature information transfer and a ground truth supervision function. Furthermore, in order to alleviate the increase in computational costs brought by constructing large-scale networks and better adapt to the demand for terminal deployment, a novel point cloud lightweighting method for semantic segmentation network (PCLN) is proposed to compress the network by transferring multidimensional feature information of large-scale networks. Specifically, at different stages of the large-scale network, the structure and attention information of the point features are selectively transferred to guide the compressed network to train in the direction of the large-scale network. This paper also solves the problem of representing global structure information of large-scale point clouds through feature sampling and aggregation. Extensive experiments on public datasets and real-world data demonstrate that the proposed method can significantly improve the performance of different base networks and outperform the state-of-the-art.

The echo of maneuvering targets are analyzed in this paper, which combined with Polynomial phase signals (PPSs), and compared the parameter estimation performance of PPSs based on the fractional Fourier transform(FRFT), the cubic phase function and the product cubic phase function(PCPF) for the echo characteristics. In the third section of the paper, sparse aperture imaging is performed on the aircraft, which flies smoothly and the echo is randomly missing, and the RD algorithm of the maneuvering target is compared with FRFT and PCPF algorithm.