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Partial-Information-Based Distributed Filtering in Two-Targets Tracking Sensor Networks

机译:两目标跟踪传感器网络中基于局部信息的分布式过滤

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摘要

In this paper, the partial-information-based (PIB) distributed filtering problem is addressed for two-targets tracking sensor networks. Different from existing distributed filters, the information communication between the sensors are assumed to have an imperfect physical condition-only partial information can be transmitted. Furthermore, for different pairs of adjacent nodes, the partly transmitted information can be completely distinct. The constraint on “partial information transmission” makes the filtering problem in sensor networks more challenging and practical. Some criteria concerning the connection gains are derived and used to design efficient PIB distributed filter to achieve the following objectives: i) the sensor network can efficiently tract two desired targets in the absence of disturbance and noise; ii) the filter satisfies certain given performance constraint. By using the regrouping method, which is an effective way to derive the main results, some simple yet effective criteria are derived for PIB distributed filtering. A numerical example is utilized to illustrate the effectiveness of the theoretical results.
机译:本文针对两目标跟踪传感器网络,解决了基于局部信息(PIB)的分布式过滤问题。与现有的分布式滤波器不同,传感器之间的信息通信被假定为具有不完善的物理条件,仅部分信息可以被传输。此外,对于不同对的相邻节点,部分传输的信息可以完全不同。对“部分信息传输”的限制使得传感器网络中的过滤问题更具挑战性和实用性。得出一些有关连接增益的标准,并用于设计有效的PIB分布式滤波器,以实现以下目标:i)传感器网络可以在没有干扰和噪声的情况下有效地确定两个期望的目标; ii)滤波器满足某些给定的性能约束。通过使用重新分组方法(这是得出主要结果的一种有效方法),可以为PIB分布式过滤导出一些简单而有效的准则。数值例子说明了理论结果的有效性。

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