基于性能边界和量化数据的WSN目标跟踪传感器选择算法

对能量和带宽受限的无线传感器网络下的目标跟踪问题，基于量化的观测数据和条件后验克拉美-罗下界提出一种传感器选择方法.为了节约网络能量和带宽，对传感器接收到的观测数据进行量化压缩，推导了传感器量化数据下目标状态估计的条件后验克拉美-罗下界，将其作为传感器选择和优化的准则，并且利用粒子滤波器给出一种条件后验克拉美-罗下界的近似计算方法.与基于无条件后验克拉美-罗下界和互信息的传感器选择方法进行了对比仿真，结果表明了条件后验克拉美-罗下界作为传感器选择准则的有效性以及对跟踪性能的改进.

Sensor Selection for Target Tracking in Wireless Sensor Networks Based on Performance Bounds and Quantized Data

For target tracking in the energy and bandwidth-constrained wireless sensor networks,a sensor selection scheme is proposed based on quantized data and conditional posterior Cramér-Rao lower bounds (CPCRLB).The received measurements are quantized and compressed to save the energy and bandwidth.The CPCRLB with quantized data is derived and used as the criterion for sensor selection to optimize the performance of tracking.Moreover,the particle filtering is employed to compute the CPCRLB approximately.The CPCRLB based sensor selection scheme is compared with the mutual information and the unconditional posterior Cramér-Rao lower bounds based sensor selection schemes by simulation.The results show that the CPCRLB is more efficient and the improved performance is achieved.