Cost-function-based hypothesis control techniques for multiple hypothesis tracking
Cost-function-based hypothesis control techniques for multiple hypothesis tracking
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DOI：
10.1117/12.542325
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发表时间：
2004-08
期刊：
影响因子：
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通讯作者：
Jason L. Williams;P. Maybeck
中科院分区：
文献类型：
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作者：
Jason L. Williams;P. Maybeck
在杂波中跟踪目标的问题自然导致目标状态向量的概率密度函数的高斯混合表示。现代跟踪方法保持了每个假设对应的均值、协方差和概率权值，但依靠简单的合并和修剪规则来控制假设的增长。本文提出了一种结构化的、基于成本函数的方法来解决假设控制问题，利用积分平方误差（ISE）成本度量。将基于iss的滤波器与先前提出的近似方法（包括简单剪枝、Singer的n扫描记忆滤波器、Salmond的连接滤波器以及Chen和Liu的混合卡尔曼滤波器（MKF））进行了轨道寿命性能与计算成本的比较。结果表明，基于ise的混合成分缩减算法的航迹寿命性能明显优于使用相同混合成分数量的比较算法，并且在相似的平均计算时间上与比较算法具有竞争力。
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer's n-scan memory filter, Salmond's joining filter, and Chen and Liu's Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides track life performance which is significantly better than the compared techniques using similar numbers of mixture components, and performance competitive with the compared algorithms for similar mean computation times.