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Generalized solution to smoothing and out-of-sequence processing

机译：平滑和乱序处理的通用解决方案

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摘要

The increasing trend toward multisensor systems is driving an interest for distributed tracking algorithms. In such systems, data transmissions with imperfect communication links often lead to varying time delays. The challenge of online processing of delayed data is generally known as the out-of-sequence (OoS) problem. An exact solution to this problem is given by the accumulated state density (ASD) filter. However, the Rauch-Tung-Striebel (RTS) retrodiction1 is inherently integrated to the ASD approach, so a certain number of states must be updated at each filtering step. In this paper, a generalized form of the ASD filter is presented. In particular, the exact update formula for states that refer to time instants that are older and newer than a given measurement is calculated. As a consequence, OoS data can be processed without applying retrodiction. A novel smoothing scheme is presented that uses the exact cross-covariances of a measurement and a past state without recursion. A numerical evaluation shows that this smoothing scheme is about 25% faster than the RTS equations yet is still exact.
机译：朝着多传感器系统发展的趋势正在引起人们对分布式跟踪算法的兴趣。在这样的系统中，通信链路不完善的数据传输通常会导致时间延迟的变化。在线处理延迟数据的挑战通常被称为乱序（OoS）问题。累积状态密度（ASD）滤波器为该问题提供了精确的解决方案。但是，劳赫-东-斯特里贝尔（RTS）追溯1已固有地集成到ASD方法中，因此必须在每个过滤步骤中更新一定数量的状态。本文介绍了ASD滤波器的一般形式。尤其是，针对与给定测量值相比更早或更新的时刻的状态，计算出精确的更新公式。结果，无需进行追溯即可处理OoS数据。提出了一种新颖的平滑方案，该方案使用测量的精确互协方差和过去状态而无需递归。数值评估表明，该平滑方案比RTS方程快约25％，但仍是精确的。

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