基于改进高斯和粒子滤波的海底地形辅助导航
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摘要
为了提升低分辨率海底地形图下的导航定位精度,提出一种基于改进高斯和粒子滤波的海底地形辅助导航方法。以高斯和粒子滤波为基础,通过高斯过程回归建立海底地形模型以获得有效粒子观测值。在量测更新阶段引入最小均方误差约束从而提升高斯和粒子滤波的估计效率,再进行滤波并最终获得导航输出。该方法能够解决低分辨率海图下数字地形模型不准确问题并提升高斯和粒子滤波在实时计算过程中的运算效率。在某低分辨率海图下进行仿真实验,结果表明:所提出的算法与采用基本粒子滤波和基本高斯和粒子滤波的海底地形辅助导航方法相比,导航定位精度提升了20%～40%,算法耗时降低了30%～40%。
To improve the accuracy of navigation and location under low resolution chart, a submarine terrain aided navigation algorithm with Improved Gaussian Sum Particle Filter(IGSPF) is proposed based on basic Gaussian sum particle filter. A seabed terrain model is established through Gaussian Process Regression(GPR) to obtain particle observations. Minimum mean square error(MMSE) constraint is introduced in the measurement update stage to improve the estimation efficiency of Gaussian sum particle filter. The proposed method can solve the inaccuracy of digital terrain model under low resolution chart and improve the efficiency of Gaussian sum particle filter in real-time. The simulation results show that,compared with the seabed terrain aided navigation methods using basic particle filter and basic Gaussian sum particle filter, the navigation accuracy of the proposed algorithm is increased by 20%～40%, and the running time is decreased by 30%～40%.
To improve the accuracy of navigation and location under low resolution chart, a submarine terrain aided navigation algorithm with Improved Gaussian Sum Particle Filter(IGSPF) is proposed based on basic Gaussian sum particle filter. A seabed terrain model is established through Gaussian Process Regression(GPR) to obtain particle observations. Minimum mean square error(MMSE) constraint is introduced in the measurement update stage to improve the estimation efficiency of Gaussian sum particle filter. The proposed method can solve the inaccuracy of digital terrain model under low resolution chart and improve the efficiency of Gaussian sum particle filter in real-time. The simulation results show that,compared with the seabed terrain aided navigation methods using basic particle filter and basic Gaussian sum particle filter, the navigation accuracy of the proposed algorithm is increased by 20%～40%, and the running time is decreased by 30%～40%.
引文
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[13]Zhou L,Cheng X,Zhu Y.Terrain aided navigation for autonomous underwater vehicles with coarse maps[J].Measurement Science and Technology,2016,27(9):1-14.
[14]阎述学,李一平,封锡盛.一种基于高斯过程回归的AUV自适应采样方法[J].机器人,2019,41(2):232-241.Yan S,Li Y,Feng X.An AUV adaptive sampling method based on Gaussian process regression[J].Robot,2019,41(2):232-241.
[2]程传奇,郝向阳,张振杰,等.鲁棒性地形匹配/惯性组合导航算法[J].中国惯性技术学报,2016,24(2):202-207.Cheng C,Hao X,Zhang Z,et al.Robust integrated navigation algorithm of terrain aided navigation/INS[J].Journal of Chinese Inertial Technology,2016,24(2):202-207.
[3]谌剑,熊露,石静,等.一种改进粒子滤波水下地形匹配算法研究[J].兵工学报,2015,36(2):200-205.Shen J,Xiong L,Shi J,et al.An improved particle filter algorithm for underwater terrain matching[J].Acta Armamentarii,2015,36(2):200-205.
[4]Li P,Sheng G,Zhang X,et al.Underwater terrain-aided navigation system based on combination matching algorithm[J].Isa Transactions,2018,78(7):80-87.
[5]李雄伟,刘建业,康国华.TERCOM地形高程辅助导航系统发展及应用研究[J].中国惯性技术学报,2006,14(1):34-40.Li X,Liu J,Kang G.Development and application of TERCOM elevation-aided navigation system[J].Journal of Chinese Inertial Technology,2006,14(1):34-40.
[6]Park J,Kim Y,Bang H.A new measurement model of interferometric radar altimeter for terrain referenced navigation using particle filter[C]//Proceedings of Navigation Conference,2017.
[7]Murangira A,Musso C,Dahia K.A mixture regularized rao-blackwellized particle filter for terrain positioning[J].IEEE Transactions on Aerospace&Electronic Systems,2016,52(4):1967-1985.
[8]Yoo Y M,Park C G.Improvement of terrain referenced navigation using a Point Mass Filter with grid adaptation[J].International Journal of Control,Automation and Systems,2015,13(5):1173-1181.
[9]Kotecha J H,Djuric P M.Gaussian particle filtering[J].IEEE Transactions on Signal Processing,2003,51(10):2592-2601.
[10]Kotecha J H,Djuric P M.Gaussian sum particle filtering[J].IEEE Trans Signal Processing,2003,51(10):2602-2612.
[11]Chen P,Chang,J,Han Y,et al.Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering[J].International Journal of Advanced Robotic Systems,2019,16(1):1-7.
[12]Philipp R,Manuel T A.Revisiting Gaussian process regression modeling for localization in wireless sensor networks[J].Sensors,2015,15(9):22587-22615.
[13]Zhou L,Cheng X,Zhu Y.Terrain aided navigation for autonomous underwater vehicles with coarse maps[J].Measurement Science and Technology,2016,27(9):1-14.
[14]阎述学,李一平,封锡盛.一种基于高斯过程回归的AUV自适应采样方法[J].机器人,2019,41(2):232-241.Yan S,Li Y,Feng X.An AUV adaptive sampling method based on Gaussian process regression[J].Robot,2019,41(2):232-241.