CAJ | 학술논문

为了提高潜器导航定位精度，针对等值线算法在惯导系统初始误差较大时易发散的问题，提出基于概率神经网络调优的等值线改进方法。首先，在搜索区域内，利用概率神经网络算法对惯导系统航迹进行调优，并经过卡尔曼滤波器与惯导系统航迹进行信息融合形成待匹配航迹；在此基础上利用实时等值线算法得到最佳匹配位置。分别在不同初始条件下进行仿真分析，得出概率神经网络算法在大的初始误差下不易发散但定位精度不高的结论，然后在潜器行驶6 h 后，初始误差为5.438?的条件下进行仿真验证，结果表明，改进方法定位精度均值优于0.537?，从而证明改进方法是有效的，即使在大的初始误差下仍然能够达到较高的定位精度。
위료제고잠기도항정위정도，침대등치선산법재관도계통초시오차교대시역발산적문제，제출기우개솔신경망락조우적등치선개진방법。수선，재수색구역내，이용개솔신경망락산법대관도계통항적진행조우，병경과잡이만려파기여관도계통항적진행신식융합형성대필배항적；재차기출상이용실시등치선산법득도최가필배위치。분별재불동초시조건하진행방진분석，득출개솔신경망락산법재대적초시오차하불역발산단정위정도불고적결론，연후재잠기행사6 h 후，초시오차위5.438?적조건하진행방진험증，결과표명，개진방법정위정도균치우우0.537?，종이증명개진방법시유효적，즉사재대적초시오차하잉연능구체도교고적정위정도。
The iterated closest contour point(ICCP) algorithm is liable to divergence if the Inertial Navigation System(INS) initial error is large. In order to solve this problem and improve the navigation location precision, an improved ICCP method based on probabilistic neural network(PNN) optimization was proposed. First, in the search area, a PNN algorithm was used for INS track optimization and an awaiting matching track was formed by INS information fusion through Kalman filter. Based on this, a real-time ICCP algorithm was used to obtain the best matching position. In variable initial error conditions, it is proved that the PNN algorithm does not arouse divergence easily when with large initial error but has no high location precision. Then the improved method is verified by simulation after vehicle has run 6 h and the initial matching error is 5.438? , and the results show that the average positioning accuracy of the improved method is superior to 0.537? , which proves that the improved method is effective, and a high location precision can still be achieved even when with large INS initial error.