A joint robust gravity matching localization method based on hybrid sparse ICCP algorithm
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NETL
NSTL
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针对经典最近等值线迭代(ICCP)算法因重力异常测量误差导致匹配精度下降甚至失效的问题,提出联合抗差匹配算法以提高匹配精度及可靠性.首先,分析了匹配点集间的匹配残差在高斯噪声影响下呈非高斯分布,为抑制其影响,采用lp范数代替l2 范数计算匹配残差,并利用匹配残差重调野值点以获得有效的匹配区域.在此基础上,提出混合稀疏 ICCP 算法,并利用其进行粗匹配,然后将粗匹配后的位置作为惯导系统(INS)指示位置,再使用经典ICCP算法进行精匹配,获得更高的定位精度.仿真结果表明,考虑重力异常测量误差的情况下,重力联合抗差匹配算法的误差最大值小于1 n mile,导航精度较传统ICCP算法提升 60%以上,提升了算法的鲁棒性和匹配精度.
Aiming at the problem of decreased or even ineffective matching accuracy caused by gravity anomaly measurement errors in the iterative closest contour point algorithm(ICCP),a joint robust matching algorithm is proposed to improve the matching accuracy and reliability.Firstly,it is analyzed that matching point set residuals affected by Gaussian noise are non-Gaussian distributed.To reduce this effect,l p-norm is used instead ofl 2-norm to calculate the matching residuals.The outliers are then adjusted using the matching residuals to obtain an effective matching region.Based on this approach,the hybrid sparse ICCP algorithm is proposed and utilized for coarse matching to indicate the position of inertial navigation system(INS).Finally,the classical ICCP algorithm is used for fine matching to obtain higher positioning accuracy.The simulation results show that considering the gravity anomaly measurement error,the maximum error of the gravity joint robust matching algorithm is less than1 n mile,and the navigation accuracy is improved by more than 60%compared with the traditional ICCP algorithm,which improves the robustness and matching accuracy of the algorithm.
gravity matchinghybrid sparse ICCP algorithmrobust algorithmcombined match
NETL
NSTL
万方数据
