IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station

doi:10.3901/JME.2023.04.125

Abstract

Abstract: The traditional Kalman filter algorithm is difficult to accurately localization the vehicle in the process of real-time motion of the vehicle. Therefore, a motion state adaptive interactive multiple model Kalman filter(IMMKF) and multiple base station direction of arrival(DOA) algorithm is proposed to estimation the real-time position of vehicle. Based on the unbiased estimator, the measurement noise covariance is updated in real time and embedded in the standard Kalman filter algorithm to realize the adaptive IMMKF. In view of the impact of different vehicle motion states and dynamic driving environments on the accuracy of vehicle positioning estimation, an adaptive IMMKF and multi-base station information fusion algorithm are constructed to estimate the vehicle position in real time. The proposed IMMKF-DOA fusion algorithm considering the change trend of vehicle positioning accuracy under different speeds of vehicle and different number of base stations, and achieves accurate estimation of vehicle real-time position. Using PreScan-Simulink union simulation platform for virtual simulation verification and real vehicle test verification. The results show that the fusion algorithm based on the IMMKF and the DOA has a higher estimation accuracy than the standard Kalman filter, which better improves the accuracy of the traditional single-model Kalman filter algorithm in the process of real-time vehicle motion state estimation. For the positioning problem, the actual vehicle test verified that the proposed algorithm has improved the accuracy of the vehicle positioning by an order of magnitude compared with the accuracy of the traditional Kalman filter algorithm and achieved more accurate vehicle position estimation.

Key words: interacting multiple model kalman filter (IMMKF), direction of arrival (DOA), fusion algorithm, multiple input multiple output (MIMO)

CLC Number:

U471

WANG Faan, YIN Guodong, ZHUANG Weichao, LIU Shuaipeng, LIANG Jinhao, LU Yanbo. IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station[J]. Journal of Mechanical Engineering, 2023, 59(4): 125-134.

References

[1] XU L,ZHUANG W,YIN G,et al. Modeling and robust control of heterogeneous vehicle platoons on curved roads subject to disturbances and delays[J]. IEEE Transactions on Vehicular Technology,2019,68(12):11551-11564.
[2] CESPEDES S,SALAMANCA J,YANEZ A,et al. Group cycling meets technology:A cooperative cycling cyber-physical system[J]. IEEE Transactions on Intelligent Transportation Systems,2019,20(8):3178-3188.
[3] ZHUANG W,ZHANG X,LI D,et al. Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle[J]. Applied Energy,2017,204:476-488.
[4] ZHANG Y,XU H,WU J. An automatic background filtering method for detection of road users in heavy traffics using roadside 3-D lidar sensors with noises[J]. IEEE Sensors Journal,2020,20(12):6596-6604.
[5] YE Y,WONG S C,LI Y C,et al. Risks to pedestrians in traffic systems with unfamiliar driving rules:A virtual reality approach[J]. Accident Analysis Prevention,2020,142:105565.
[6] LIU A,LIAN L,LAU V,et al. Cloud-assisted cooperative localization for vehicle platoons:A turbo approach[J]. IEEE Transactions on Signal Processing,2020,68:605-620.
[7] CRUZ S B,AGUIAR A. MagLand:Magnetic landmarks for road vehicle localization[J]. IEEE Transactions on Vehicular Technology,2020,69(4):3654-3667.
[8] DONG L,SUN D,HAN G,et al. Velocity-free localization of autonomous driverless vehicles in underground intelligent mines[J]. IEEE Transactions on Vehicular Technology,2020,69(9):9292-9303.
[9] LI C,FU Y,YU F R,et al. Vehicle position correction:A vehicular blockchain networks-based gps error sharing framework[J]. IEEE Transactions on Intelligent Transportation Systems,2021,22(2):1-15.
[10] REZAEI S,SENGUPTA R. Kalman filter-based integration of dgps and vehicle sensors for localization[J]. IEEE Transactions on Control Systems Technology,2007,15(6):1080-1088.
[11] LI W,JIA Y,DU J. Distributed Kalman filter for cooperative localization with integrated measurements[J]. IEEE Transactions on Aerospace and Electronic Systems,2020,56(4):3302-3310.
[12] HAMELMANN P,VULLINGS R,MISCHI M,et al. An extended Kalman filter for fetal heart location estimation during doppler-based heart rate monitoring[J]. IEEE Transactions on Instrumentation and Measurement,2019,68(9):3221-3231.
[13] LIU K,LI J,GUO W. Navigation system of a class of underwater vehicle based on adaptive unscented kalman filter algorithm[J]. Journal of Central South University of Technology,2014,21(2):550-557.
[14] FENG W,LI Q,LU Q. Force localization and reconstruction based on a novel sparse Kalman filter[J]. Mechanical Systems and Signal Processing,2020,144:106890.
[15] LIU J,CAI B G,WANG J. Cooperative localization of connected vehicles:Integrating gnss with dsrc using a robust cubature Kalman filter[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(8):2111-2125.
[16] CHIANG K W,TSAI G J,CHU H J,et al. Performance enhancement of ins/gnss/refreshed-slam integration for acceptable lane-level navigation accuracy[J]. IEEE Transactions on Vehicular Technology,2020,69(3):2463-2476.
[17] HAN J,KIM J,SHIM D H. Precise localization and mapping in indoor parking structures via parameterized slam[J]. IEEE Transactions on Intelligent Transportation Systems,2019,20(12):4415-4426.
[18] GINGRAS D,POLLARD E,GRUYER D. Signal processing requirements and uncertainty modeling issues in cooperative vehicular positioning[C]//IEEE Conf. 7th Int. Workshop Systems,Signal Processing Applications,Tipaza,Algeria,May 9-11,2011:303-306.
[19] LU J,CHEN X,LUO M. Cooperative localization for multiple AUVs based on the rough estimation of the measurements[J]. Applied Soft Computing,2020,91:106197.
[20] ZHANG J,LIU R,YIN K,et al. Intelligent collaborative localization among air-ground robots for industrial environment perception[J]. IEEE Transactions on Industrial Electronics,2019,66(12):9673-9681.
[21] WANG H,WAN L,DONG M,et al. Assistant vehicle localization based on three collaborative base stations via SBL-based robust DOA estimation[J]. IEEE Internet of Things Journal,2019,6(3):5766-5777.
[22] NASERI H,KOIVNEN V. A bayesian algorithm for distributed network localization using distance and direction data[J]. IEEE Transactions on Signal and Information Processing over Networks,2019,5(2):290-304.
[23] TOMIC S,BEKO M,DINIS R. RSS-based localization in wireless sensor networks using convex relaxation:Noncooperative and cooperative schemes[J]. IEEE Transactions on Vehicular Technology,2015,64(5):2037-2050.
[24] XU S. Optimal sensor placement for target localization using hybrid RSS,AOA and TOA measurements[J]. IEEE Communications Letters,2020,24(9):1966-1970.
[25] TOMIC S,BEKO M. A robust NLOS bias mitigation technique for RSS-TOA-based target localization[J]. IEEE Signal Processing Letters,2019,26(1):64-68.
[26] TOMIC S,BEKO M,DINIS R,et al. A closed-form solution for RSS/AOA target localization by spherical coordinates conversion[J]. IEEE Wireless Communications Letters,2016,5(6):680-683.
[27] WANG H,WAN L,DONG M,et al. Assistant vehicle localization based on three collaborative base stations via SBL-based robust DOA estimation[J]. IEEE Internet of Things Journal,2019,6(3):5766-5777.
[28] FASCISTA A,CICCARESE G,COLUCCIA A,et al. Angle of arrival-based cooperative positioning for smart vehicles[J]. IEEE Transactions on Intelligent Transportation Systems,2018,19(9):2880-2892.
[29] ROHANI M,GINGRAS D,VIGNERON V,et al. A new decentralized bayesian approach for cooperative vehicle localization based on fusion of GPS and VANET based inter-vehicle distance measurement[J]. IEEE Intelligent Transportation Systems Magazine,2015,7(2):85-95.
[30] SONG X,LING Y,CAO H,et al. Cooperative vehicle localisation method based on the fusion of GPS,inter-vehicle distance,and bearing angle measurements[J]. IET Intelligent Transport Systems,2019,13(4):644-653.
[31] TOMIC S,BEKO M,DINIS R,et al. Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements[J]. Pervasive and Mobile Computing,2017,37:63-77.
[32] TOMIC S,BEKO M,TUBA M. A linear estimator for network localization using integrated RSS and AOA measurements[J]. IEEE Signal Processing Letters,2019,26(3):405-409.