利用目标检测前后信息熵的变化以及卡尔曼滤波方程中协方差的预测与更新阵 ,可以计算出传感器对目标进行检测或跟踪所产生的信息增量 ,并给出了一类基于信息增量最大化传感器管理方法 .仿真结果表明 :与顺序跟踪方法相比 ,该方法能够更合理分配有限的传感器资源 ,提高跟踪系统的整体性能 ;与随机检测与跟踪方法相比 ,该方法在保证一定跟踪精度的前提下 ,能最大限度地发现并跟踪新目标 The change of information entropy before and after the target detection and the prediction and update matrix of the covariance in the Kalman filter equation can be used to calculate the information increment caused by the sensor detecting or tracking the target. Finally, The results show that compared with the sequential tracking method, this method can allocate more limited sensor resources and improve the overall performance of the tracking system. Compared with the stochastic detection and tracking method, this method can guarantee the tracking accuracy Under the premise of maximizing the discovery and tracking of new targets