一种基于Renyi信息增量的机动目标协同跟踪方法

A method of maneuvering target collaboration tracking based on Renyi information gain

针对传感器网络中的动态跟踪问题,提出一种基于Renyi信息增量的机动目标协同跟踪方法,首先利用粒子滤波计算每个传感器Renyi信息增量;然后以Renyi信息增量最大为原则选择传感器进行目标跟踪,并在跟踪时通过多模型的交互作用实现对机动目标状态的准确估计,仿真结果表明,在非线性非高斯环境下,所提出的方法与传统方法相比能够有效提高跟踪精度,动态分配传感器资源,实现协同跟踪。

Focusing on the dynamic tracking problem in sensor networks, a maneuvering target collaboration tracking method based on Renyi information gain is proposed. Firstly, the particle filtering algorithm is applied to obtain the Renyi information gain of each sensor. Then, the sensor is selected according to the maximal Renyi information gain. Moreover, the kinematics state of the maneuvering target is estimated by interacting multiple model method. Simulation results show that the proposed method can achieve the desired tracking accuracy compared with traditional methods in a nonlinear nonGaussian system.