The interrupted‐sampling repeater jamming (ISRJ) is coherent with an emitted signal, and significantly limits radar's ability to detect, track and recognise targets. This study focuses on the research of ISRJ suppression for linear frequency modulation radars. A new band pass filter design method based on time frequency (TF) analysis is proposed. A function named ‘max‐TF’ is constructed from the TF energy distribution of the de‐chirped signal, reflecting the changes of the maximum signal component amplitude with respect to time. Based on the ‘max‐TF’ function, jamming‐free signal segments are automatically and accurately extracted to generate the filter, which is smoothed subsequently. After filtering, jamming signal peaks in pulse compression results are suppressed while real targets are retained simultaneously. Comparing with the state‐of‐the‐art filtering method, the proposed method has improved jamming suppression ability and extended the feasible scope of signal‐to‐noise ratio and jamming‐to‐signal ratio conditions. Simulations have validated the improvements and demonstrated how the parameters affect performance. The average signal to jamming improvement and average radar detection rate of the proposed method is about 7.4 dB and 23% higher than those of the state‐of‐the‐art filtering method, respectively. The direction of further works is inferred.

Warhead and decoy classification is one of the most important and difficult technical problems in ballistic missile defence. The conventional methods extract features from the measured data and employ some classification algorithms. However, it is hard to extract all the information embedded in the raw data, and there might be contradictory features lowering the classification ability. A one‐dimensional convolutional neural network structure named RCSnet was proposed to classify the warhead and decoy targets of the same shape in midcourse, which directly utilises the radar cross‐section (RCS) time series. It was compared with 5 conventional classification algorithms which used 26 selected features on simulation dataset, and it outperformed them in both classification performance and predicting speed. Different training algorithms and networks of the RCSnet structure with different filter numbers were explored for better utilising the RCSnet.

Interrupted-sampling repeater jamming (ISRJ) is coherent jamming based on digital radio frequency memory (DRFM) device, which repeatedly samples, stores, modulates, and retransmits part of the radar emitted signal, and flexibly forms false targets in the victim radar with relatively low transmitting power. It significantly interferes the radar to detect, track, and recognize targets. There are many electronic counter-countermeasures against ISRJ, among which a series of filtering methods are promising. However, it is not fully addressed. This study proposes a filtering method based on stacked bidirectional gated recurrent unit network (SBiGRU) and infinite training to fulfill the ISRJ suppression for pulse compression (PC) radar with linear frequency modulation (LFM) waveform. SBiGRU method converts signal extraction into a temporal classification problem and accurately extracts the jamming-free signal segments to generate a band pass filter to suppress the ISRJ and retain the real target signal components simultaneously. Comparing with two most advanced filtering methods in the published literature, SBiGRU method has improved the jamming-free signal extraction accuracy, leading to better performances of ISRJ suppression and real targets detection, which are verified by Monte Carlo Simulations.

With the growth of IoT and latency-sensitive applications (e.g., virtual reality, autonomous driving), massive amounts of data are generated at the network and the edge. Such proliferation drives the development of dispersed computing as a promising complementary paradigm to cloud computing and edge computing. Dispersed computing can leverage in-network computing resources to provide lower latency guarantees and more reliable computing power support than the cloud computing. On the other hand, dispersed computing shows excellent performance in highly dynamic and heterogeneous environments. This paper introduces the concepts of dispersed computing and demonstrates the potential of dispersed computing in terms of providing low-latency services and adapting to highly dynamic and heterogeneous environments through several cases. To better grasp the current state of research in dispersed computing, several major research directions and advances are also presented in the hope of attracting the attention of the community and inspiring more researches to promote the implementation of dispersed computing.

Compared with cloud computing, edge computing shows real-time, security and other advantages when dealing with the explosive growth of the Internet of Things. In the process of data processing and mining data value, edge intelligence combines the advantages of edge computing and artificial intelligence to become a research hotspot. Federated learning is one of the most popular machine learning frameworks for edge intelligence. It provides stronger privacy protection for the client through the aggregation of the server-side client-side model. However, the latter often do not participate in the federal learning system due to resource constraints and economic considerations. Therefore, incentive mechanisms are necessary to attract higher quality clients. To this end, we use the Stackelberg game model to model the server and the client, and establish an incentive-driven federated learning algorithm FEDID. Finally, we verified the effectiveness of the incentive mechanism through experiments.