摘要
针对"当前"统计模型中目标机动频率和极限加速度值人为设定的不合理性,提出一种基于修正模型的模糊自适应算法(CS-MFA),对机动频率建模以便其估计更新,同时利用目标机动信息来实时调整过程噪声方差,提高系统在目标作非机动或者弱机动时的跟踪精度以及在强机动时的快速响应能力。最后,通过仿真验证了该算法的有效性。
In view of the irrationality that the value of maneuvering frequency and maximum acceleration of the maneuvering target are set up artificially in the current statistical model, we present here a fuzzy adaptive algorithm based on the modified model （CS-MFA）, which is used to build up the model of maneuvering frequency for its estimation and updating. At the same time, the target maneuvering information is used to adjust the process noise variance adaptively in real time, which can improve both the tracking accuracy when the target is not in highly maneuvering state and the rapid response capability for a highly-maneuvering target. Simulation results show that the algorithm is valid.
出处
《电光与控制》
北大核心
2009年第10期18-21,共4页
Electronics Optics & Control
基金
航空科学基金(2006ZC12004)
总装备部武器装备预研基金(9140A04050407JB3201)
