基于条件后验克拉美–罗下界的目标跟踪传感器管理

Sensor management for target tracking based on conditional posterior Cramér-Rao lower bounds

对传感器网络下的目标跟踪问题,基于条件后验克拉美–罗下界(CPCRLB)提出一种传感器网络节点管理方法.传感器节点的观测数据是目标信号强度(RSS),基于RSS量测推导了目标状态估计均方误差的CPCRLB.以CPCRLB作为传感器管理的准则,选择激活一组最优传感器节点参与目标跟踪.利用粒子滤波器逼近目标状态,并估计CPCRLB,实现传感器节点在线选择.对基于CPCRLB、无条件后验克拉美–罗下界(PCRLB)和互信息理论的传感器选择方法进行对比仿真,结果表明CPCRLB传感器管理的有效性和优越性.

For target tracking in the sensor networks, a sensor management scheme is proposed based on conditional posterior Cramrr-Rao lower bounds （CPCRLB）. The measurement in the sensor is the received signal strength （RSS） from the target. The CPCRLB on the mean--squared error of target state estimate is derived based on RSS measurements. The CPCRLB is used as the criterion for activating a set of optimal sensors to involve in the target tracking. The particle filtering is employed to estimate the target state and CPCRLB approximately. The online sensor selection is achieved by particle filtering. The sensor selection schemes based on CPCRLB, unconditional posterior Cramrr-Rao lower bounds （PCRLB） and mutual information are compared by simulations. The simulation results demonstrate the efficiency and superiority of the CPCRLB-based sensor management.