舰船电子对抗

用传统船舶雷达对海上单个快速移动目标、多个移动轨迹目标进行探测、跟踪时，存在发现概率低、预测精度差和跟踪难度大等问题，通过配置多个船舶无源雷达进行实时探测，并对接收信息进行有效融合可以显著提升航迹预测的精度。通过对多船舶无源雷达进行建模，利用樽海鞘群算法(SSA)对多船舶航迹状态进行预测，并在寻优中采用UKF对参数估计过程进行适时干预，降低估计值实为局部最优值的概率。通过仿真实验表明，该算法能够有效完成对海上多个移动目标的航迹预测，且参数估计的精度更高。

There are problems such as low detection probability, poor prediction accuracy and high tracking difficulty while detecting and tracking single rapidly moving target and multiple targets with moving trajectory at sea by using traditional ship radars.The accuracy of trajectory prediction can be significantly improved by configuring multiple passive ship radars for real-time detection and effectively fusing received information.By modeling the multiple passive ship radars, this article uses the salp swarm algorithm(SSA) to predict the trajectory status of multiple ships, and uses unscented Kalman filter(UKF) to perform timely intervention to the factor evaluation course during the optimization process, which reduces the probability that the estimated value is actually the local optimal value.Simulation experiments show that this algorithm can effectively predict the trajectory of multiple moving targets on the sea, and the accuracy of parameter estimation is higher.