Optimal Algorithm for "Out-of-Sequence" Estimates with Arbitrary Communication Delays

Chenglin Wen, Quanbo Ge
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Abstract

Sensor data, which are transmitted from local sensors to the central processor, often appear various communication delays because of the uncertain communication channel and various disturbances. Thereby, optimal "Out-of-sequence" Measurements (OOSM) fusion has been presented and attracts plenty of attentions. The OOSM scenario can deal with the delayed fusion problem, but it inevitably comes forth many problems which have influenced its application ability, for example, high computational complexity, the complex algorithm structure, low computational efficiency, and bad real-time performance and so forth. In order to overcome above problems, the concept for the "Out-of-sequence" Estimates (OOSE) fusion has been also presented and it has shown excellent performance to solve the delayed fusion. In this paper, the optimal fusion for single sensor OOSE is researched and the corresponding algorithm is also proposed including its integrated and recursive forms, which are optimal in minimum mean square error (MMSE). Performance analysis and simulation example show that the proposed OOSE method is valid and has many advantages compared with the current OOSM methods and just the estimate accuracy is reduced after a sort.
具有任意通信延迟的“乱序”估计的最优算法
传感器数据从本地传感器传输到中央处理器时,由于通信信道的不确定和各种干扰,往往会出现各种通信延迟。因此,最优“乱序”测量(OOSM)融合得到了广泛的关注。OOSM场景可以处理延迟融合问题,但不可避免地产生了计算量大、算法结构复杂、计算效率低、实时性差等问题,影响了其应用能力。为了克服上述问题,本文还提出了“乱序”估计(OOSE)融合的概念,并在解决延迟融合方面表现出了优异的性能。本文研究了单传感器OOSE的最优融合问题,并提出了在最小均方误差(MMSE)下最优的积分和递归算法。性能分析和仿真实例表明,所提出的OOSE方法是有效的,与现有的OOSM方法相比具有许多优点,只是经过排序后的估计精度有所降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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