Abstract
In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.
Highlights
Multi-sensor fusion maneuvering target tracking is one of the subjects that has been investigated during the last decades [1,2]
Aiming at the above problem, this paper proposes an improved unscented PF (UPF) (IUPF) approach based on a novel fusion architecture for multi-sensor fusion target tracking and applies it to the multi-target tracking by combining with joint probabilistic data association (JPDA) algorithm [18]
Based on the simulation results, they concluded that the spherical simplex unscented particle filter (SSUPF) method can greatly improve the performance of the navigation system compared with the Unscented Kalman filter (UKF), spherical simplex unscented Kalman filter (SSUKF), and unscented particle filter (PF) (UPF)
Summary
Multi-sensor fusion maneuvering target tracking is one of the subjects that has been investigated during the last decades [1,2]. The more target information, the more accurate the state estimation, so multi-sensor fusion has better performance. In contrast to the mentioned disadvantages of radar, the infrared sensor has powerful anti-jamming capability, higher angle measuring precision, and better target recognizing ability without radiating any energy It cannot directly obtain the distance information of target [4]. Aiming at the above problem, this paper proposes an improved UPF (IUPF) approach based on a novel fusion architecture for multi-sensor fusion target tracking and applies it to the multi-target tracking by combining with joint probabilistic data association (JPDA) algorithm [18].
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