Maneuvering Target Tracking Based on Adaptive Square Root Cubature Kalman Filter Algorithm
Concerning low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and statistical property, an adaptive Square Root Cubature Kalman Filter (SCKF) is proposed based on the standard SCKF and modified Sage-Husa estimator
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Maneuvering Target Tracking Based on Adaptive Square Root Cubature Kalman Filter Algorithm Sisi Wang and Lijun Wang
Abstract Concerning low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and statistical property, an adaptive Square Root Cubature Kalman Filter (SCKF) is proposed based on the standard SCKF and modified Sage-Husa estimator. The proposed algorithm can estimate the statistical parameters of unknown system noises online, and restrain the tracking error caused by unknown system noises effectively; hence it is applied to maneuvering target tracking. The simulation is preformed latterly and experimental results show that comparing with the standard SCKF algorithm, the adaptive SCKF can achieve better accuracy and stability for maneuvering target tracking while the system noises is unknown and time variation. Keywords Adaptive SCKF
Maneuvering target tracking
95.1 Introduction Maneuvering Target Tracking is a hotspot and difficulty in radar information processing fields at all times. In maneuvering tracking target applications, target dynamics are usually modeled in Cartesian coordinates. State vector encompass velocity and position components. The corresponding measurement information is expressed in polar coordinates, including distance components, bearing S. Wang (&) L. Wang School of Navigation, Guangdong Ocean University, 40#, East Jiefang Road, Zhanjiang, China e-mail: [email protected] L. Wang e-mail: [email protected]
W. Lu et al. (eds.), Proceedings of the 2012 International Conference on Information Technology and Software Engineering, Lecture Notes in Electrical Engineering 210, DOI: 10.1007/978-3-642-34528-9_95, Ó Springer-Verlag Berlin Heidelberg 2013
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components even elevation components and so on. Hence, Maneuvering Target Tracking is considered as a typical multidimensional nonlinear estimate problem. The square root cubature Kalman filter proposed in recent year is a effectively method for the multidimensional nonlinear estimate problem [1]. The simulation result proved that under the hypothesis of equal computational complexity, the SCKF algorithm has the higher accuracy than the common nonlinear estimate method such as Particle Filter and Unscented Kalman Filter and so on [2]. But while the filter is based on the inaccurate model and noise statistical property, it will lead to larger estimate error even divergence. And more often than not, the process noise is difficult represented its statistics property due to external interference, acceleration physical characteristic and manipulate and so on. Therefore, process noise is usually unknown and time variation. To solve this problem, an adaptive SCKF (ASCKF) is proposed, which is a combination of modified Sage-Husa estimator and SCKF. The modified SageHusa suboptimal unbiased estimator which is embedded into the algorithm can recursively estimates and corrects the unknown noise on line, and thus the ASCKF can handle the recursive state filtering in the pres
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