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Quasi-Monte Carlo Gaussian Particle Filter Based Passive Bearings-only Target Tracking

Aiming at the highly nonlinearity of passive bearings-only target tracking, a new algorithm based on Quasi-Monte Carlo Gaussian particle filter is proposed for target tracking, which uses Gaussian particles to approximate the marginal association probabilities. Then, Gaussian particle filter (GPF) is utilized for…

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Aiming at the highly nonlinearity of passive bearings-only target tracking, a new algorithm based on Quasi-Monte Carlo Gaussian particle filter is proposed for target tracking, which uses Gaussian particles to approximate the marginal association probabilities. Then, Gaussian particle filter (GPF) is utilized for approximating the prediction and update distributions. Meantime, the Quasi-Monte Carlo integration method is introduced to predicate the target and update the distribution. Finally, the proposed method is applied to passive multi-sensor target tracking. Simulation results show that the method can obtain better tracking performance than Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). http://www.sj-ce.org/paperInfo.aspx?ID=1244

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Quasi-Monte Carlo Gaussian Particle Filter Based Passive Bearings-only Target Trackingaspx28 KB
Aiming at the highly nonlinearity of passive bearings-only target tracking, a new algorithm based on Quasi-Monte Carlo Gaussian particle filter is proposed for target tracking, which uses Gaussian particles to approximate the marginal association probabilities. Then, Gaussian particle filter (GPF) is utilized for approximating the prediction and update distributions. Meantime, the Quasi-Monte Carlo integration method is introduced to predicate the target and update the distribution. Finally, the proposed method is applied to passive multi-sensor target tracking. Simulation results show that the method can obtain better tracking performance than Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF).
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