多传感器优化部署下的机动目标协同跟踪算法

针对资源有限的传感器网络中目标动态跟踪问题，提出了一种能够自适应选择跟踪传感器的机动目标协同跟踪算法。首先，采用粒子群优化算法优化传感器网络能耗与有效覆盖率，进行传感器位置部署；然后，以最大化候选传感器的Rényi信息增量与最小化传感器间信息传递能耗为适应度函数，采用二进制粒子群优化算法自适应选择最佳跟踪传感器组；最后，利用交互多模型粒子滤波对机动目标位置进行估计并进行分布式融合。仿真结果表明，与现有方法相比，该方法可在非高斯非线性环境下自适应选择最优跟踪传感器，显著提高目标跟踪精度，降低网络能耗。

Maneuvering target collaborative tracking algorithm with multi-sensor deployment optimization

Focusing on the dynamic tracking problem in resource constrained sensor networks, a new maneuvering target collaborative tracking algorithm with selecting tracking sensors adaptively is proposed. Firstly, the particle swarm optimization algorithm is us to trade off the sensor networks’ energy consumption and the effective coverage of the target area, and obtain the optimized sensors location. Then, the tracking sensors are selected according to the maximal Rényi information gain and the minimal energy consumption by a binary particle swarm optimization algorithm. Finally, the kinematic state of the maneuvering target is estimated by the interacting multiple model particle filtering algorithm, and the estimate states of the selected tracking sensors are fused. Simulation results show that the proposed algorithm can adaptively select tracking sensors, achieve the desired tracking accuracy and reduce network energy consumption compared with traditional methods in a nonlinear non-Gaussian system.