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Target tracking in wireless sensor networks using particle filter with quantized innovations

机译:使用具有量化创新的粒子滤波器的无线传感器网络中的目标跟踪

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Due to the bandwidth constraint of wireless sensor networks, there can be physical limitations in the communication links from sensors back to fusion center, or between sensors. In such cases, local data quantization/compression is not only a necessity, but also an integral part of the design of the sensor networks. In this paper, a target tracking approach using particle filter with quantized innovations in wireless sensor networks is proposed. The posterior Cramer-Rao lower bound for quantized innovation information received by fusion center is also given. The simulation results show the good performance of our proposed tracking approach. With a moderate small number of particles sampled at each step, we found that the tracking performance of particle filter is much better than the EKF, especially when the emitted power of each sensor is small.
机译:由于无线传感器网络的带宽约束,从传感器回到融合中心或传感器之间的通信链路中可以存在物理限制。在这种情况下,本地数据量化/压缩不仅是必需的,而且是传感器网络设计的整体部分。在本文中,提出了一种使用粒子滤波器的目标跟踪方法,其使用无线传感器网络中的量化创新。还给出了融合中心接收的量化创新信息的后克拉姆 - RAO下限。仿真结果表明我们提出的跟踪方法的良好表现。通过在每个步骤中采样的适度少量颗粒,我们发现粒子过滤器的跟踪性能远优于EKF,特别是当每个传感器的发射功率小时。

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