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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking
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Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking

机译：基于二阶马尔可夫链的机动目标跟踪多模型算法

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摘要

A multiple-model algorithm for maneuvering target tracking is proposed. It is referred to as a second-order Markov chain (SOMC)-based interacting multiple-model (SIMM) algorithm. The target maneuver process is modeled by a SOMC to incorporate more information. SIMM adopts a merging strategy similar to that of the interacting multiple-model (IMM) algorithm, except that the one-step model transition probabilities are updated based on the SOMC. A scheme is proposed to design the transition probabilities of the SOMC for target tracking. The performance of the proposed SIMM algorithm is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrate the effectiveness of SIMM compared with IMM, the second-order IMM (IMM2) algorithm, and the likely-model set (LMS) algorithm. It is shown that SIMM performs about the same as IMM2 but requires only $n$ filters versus $n^2$ filters in IMM2 for $n$ models. The effectiveness and efficiency of combining SIMM and LMS for state estimation are also demonstrated in the simulation.
机译：提出了一种机动目标跟踪的多模型算法。它被称为基于二阶马尔可夫链（SOMC）的交互多模型（SIMM）算法。目标机动过程由SOMC建模以包含更多信息。 SIMM采用类似于交互多模型（IMM）算法的合并策略，不同之处在于基于SOMC更新单步模型转换概率。提出了一种方案来设计用于目标跟踪的SOMC的转移概率。拟议的SIMM算法的性能是通过几种用于机动目标跟踪的方案进行评估的。仿真结果证明了SIMM与IMM，二阶IMM（IMM2）算法和可能模型集（LMS）算法相比的有效性。结果表明，SIMM的性能与IMM2大致相同，但是对于$ n $模型，IMM2中仅需要$ n $过滤器，而在iMM2中仅需要$ n ^ 2 $过滤器。仿真还证明了组合SIMM和LMS进行状态估计的有效性和效率。

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