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针对非理想检测环境下多雷达网络对多目标跟踪场景,提出了一种面向射频隐身的驻留时间优化分配算法。首先,推导了非理想检测环境下目标跟踪误差的贝叶斯克拉美-罗下界解析表达式,并将其作为目标跟踪性能的衡量指标。其次,将各雷达节点照射目标的驻留时间总和作为优化目标函数,将系统射频辐射资源以及给定的目标跟踪精度门限作为约束条件,建立了非理想检测环境下面向射频隐身的多雷达网络多目标跟踪驻留时间优化分配模型。在此基础上,采用基于内点法与循环最小法的两步分解算法对上述优化模型进行求解。仿真结果表明,相较于驻留时间均匀分配算法,所提算法可以有效降低多雷达网络的驻留时间消耗,提升系统的射频隐身性能;另外,随着检测概率逐渐减小,系统需要更多的驻留时间,使得射频隐身性能逐渐变差。
Abstract：Aiming at a multi-target tracking scenario of multiple radar networks under imperfect detection environment, a radio frequency stealth-based optimal dwell time allocation algorithm is proposed.Firstly, the analytical expression of Bayesian Cramer-Rao lower bound(BCRLB) of target tracking error under imperfect detection environment is derived and used as the metric of target tracking performance. Secondly, the total dwell time of each target illuminated by each radar node is used as the objective function, and the system radio frequency resources and the predetermined target tracking accuracy threshold are used as constraints,therefore, the radio frequency stealth-based optimal dwell time allocation optimization model for multipletarget tracking in multiple radar networks under imperfect detection environment is established. Then, a twostep decomposition method based on the interior point method and cyclic minimum method is used to solve the above optimization model. The simulation results show that compared with the uniform dwell time allocation strategy, the proposed algorithm can effectively reduce the dwell time consumption of multi radar networks and improve the radio frequency stealth performance of the system. In addition, as the probability of detection gradually decreases, the system needs more and more dwell time, which makes the radio frequency stealth performance gradually worse.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.2021.1.123
China Classification Code:TN953
Citation Information:
[1]唐志诚,时晨光,周建江.非理想检测环境下面向射频隐身的多雷达网络多目标跟踪驻留时间优化分配算法[J].战术导弹技术,2021,No.210(06):1-10.DOI:10.16358/j.issn.1009-1300.2021.1.123.
Fund Information:
国家自然科学基金（No.61801212）; 装备预研重点实验室基金（No.6142401200402）; 江苏自然科学基金（No.BK20180423）; 国防科技创新特区资助; 航空自然科学基金（No.20200020052005,20200020052002）