多站测角的机动目标最小二乘自适应跟踪算法

为了避免被动跟踪中非线性带来的计算复杂化及精度的下降问题,该文首先采用最小二乘法对目标的状态进行粗估计,然后采用当前机动目标模型和自适应跟踪算法进行线性的卡尔曼滤波,以实现对目标较高精度的定位和跟踪。实验结果表明:该方法对于匀速和匀加速运动的目标都可以达到良好的跟踪效果,其误差远小于经典的singer方法;对于强机动目标,singer方法将失效,而本文方法仍能实时辨识出目标的速度和加速度,并且估计效果良好。

Least Squares Adaptive Algorithm for Bearings-Only Multi-sensor Maneuvering Target Passive Tracking

To avoid the computational complexity and the precision decrease from the nonlinear feature in passive tracking, the state of the target is approximately estimated by least squares algorithm at first, and then a current statistical model and an adaptive algorithm are employed. The simulation results show that the novel least squares adaptive algorithm is of higher tracking precision than Singer algorithm in tracking the target with constant velocity or acceleration, and that it is able to estimate effectively the velocity and acceleration of the maneuvering target, in which case Singer algorithm does not work.