Mismatching Judgment Using PDAF in ICCP Algorithm
Mismatching Judgment Using PDAF in ICCP Algorithm
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DOI：
10.1109/icnc.2008.17
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发表时间：
2008-10
期刊：
影响因子：
--
通讯作者：
Yong Yang;Kedong Wang
中科院分区：
文献类型：
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作者：
Yong Yang;Kedong Wang
B.Kamga-Parsi将迭代最近轮廓点(ICCP)算法应用于水下重力匹配，其中单纯形算法用于估计航行器的最优轨迹。然而，单纯形算法往往收敛于局部最优，容易出现失配甚至发散现象。为了减少ICCP的失配概率，建立了ICCP的失配判断规则。目前常用的误匹配判断准则--M/N法存在定位前多次匹配、匹配误差大、参数难以确定等缺点。本文利用概率数据关联滤波(PDAF)建立了ICCP的失配判断规则。仿真结果表明，与无误匹配判断的ICCP算法相比，PDAF提高了算法的收敛速度和精度，与M/N算法相比，其失配概率降低了35%。采用PDAF方法进行失配判断，提高了ICCPpsilas的精度和稳定性。
B. Kamgar-Parsi applies iterative closest contour point (ICCP) algorithm into underwater gravity matching, in which simplex algorithm is used to estimate the optimal trace of vehicle. However, simplex algorithm is usually convergent to the local optimization so that there is mismatching or even diverging. In this paper, the rule of mismatching judgment for ICCP is established to reduce mismatching probability. At present, the well used mismatching judgment rule, M/N method, has several shortcomings, including matching several times before location, much large matching error, and difficult to decide parameters. In this paper, the rule of mismatching judgment for ICCP is established by probability data association filter (PDAF). Simulation shows that PDAF improves the convergence and precision compared with the ICCP algorithm without mismatching judgment, and its mismatching probability decreases 35 percent compared with M/N method. The PDAF method for mismatching judgment increases the ICCPpsilas precision and stabilization.