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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Generalised Covariance Union: A Unified Approach to Hypothesis Merging in Tracking
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Generalised Covariance Union: A Unified Approach to Hypothesis Merging in Tracking

机译：广义协方差联盟：跟踪中合并假设的统一方法

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摘要

Multi-hypothesis tracking (MHT) techniques can become prohibitively computationally expensive as the number of hypotheses increases. In order to maintain an estimate with bounded computational cost, multi-hypothesis methods often merge the estimates together. When the hypotheses are distributed according to a known probability then standard mixture reduction (SMR) methods exist for merging estimates. Also, covariance union (CU) has become a popular approach to merging hypotheses when their distribution is not known. This paper generalises CU to a new theory, which we refer to as generalised covariance union (GCU). GCU merges estimates when their distribution is not known precisely but is, instead, bounded above and below. We show that CU and the SMR approaches are limiting cases of GCU. We demonstrate the efficacy of the new approach via a Global Positioning System (GPS) tracking application with time delayed satellite signals.
机译：随着假设数量的增加，多假设跟踪（MHT）技术的计算量将变得过高。为了用有限的计算量维持估算，多假设方法通常将估算合并在一起。当假设根据已知概率分布时，则存在用于合并估计的标准混合约简（SMR）方法。同样，当未知时，协方差并集（CU）已成为合并假设的一种流行方法。本文将CU推广到一个新的理论，我们称之为广义协方差联合（GCU）。当无法准确知道其分布但将其限制在上方和下方时，GCU合并估算值。我们表明CU和SMR方法是限制GCU的情况。我们通过具有时滞卫星信号的全球定位系统（GPS）跟踪应用程序演示了该新方法的有效性。

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