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In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of "predict re-predict" is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with "measurement and then update" and "predict and update" mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance. [ABSTRACT FROM AUTHOR]

• In IR imaging, UAV cluster targets include a lack of detail characteristics, inadequate form features, and a low signal-to-noise ratio. • The weighted tri-layer sliding window local contrast algorithm is capable of resolving the problem of detecting UAV cluster targets. • In terms of SCRG, BSF, and ROC curves, the algorithm in this paper surpasses previous algorithms. Infrared images of unmanned aerial vehicle (UAV) cluster targets are characterized by the low signal-to-noise ratio (SCR), closely spaced, and limited detail characteristics, which exacerbate the detection complexity of contemporary infrared (IR) small target detection algorithms. This paper proposes a weighted tri-layer sliding window local contrast (NTLLCM) algorithm for UAV cluster targets detection. Firstly, the IR imaging characteristics of UAV cluster targets are modeled and evaluated. Secondly, after evaluating the limitations of LCM, the local contrast of the filtered images is calculated by designing a tri-layer sliding window structure with core, middle, and outermost layers. The local contrast is then weighted utilizing a new regional intensity level (NRIL) strategy to suppress the background further, and the target is detected by adaptive threshold segmentation. Finally, a considerable number of experimental results illustrate the superiority of this algorithm in detecting UAV cluster targets under sophisticated backgrounds, especially the proposed algorithm detects multiscale targets utilizing single-scale windows instead of using multiscale windows, which significantly decreases the computationally costs. Furthermore, the algorithm can reduce the overlapping effect of UAV cluster target imaging and correctly detect closely spaced targets. [ABSTRACT FROM AUTHOR]