Improved UFIR Tracking Algorithm for Maneuvering Target

2016 ◽  
Vol 2 (2) ◽  
pp. 344 ◽  
Author(s):  
Shoulin Yin ◽  
Jinfeng Wang ◽  
Tianhua Liu
Keyword(s):  
Target Tracking ◽  
Finite Impulse Response ◽  
Estimation Error ◽  
Statistical Properties ◽  
Noise Model ◽  
Noise Power ◽  
Actual Process ◽  
Maneuvering Target Tracking ◽  
Process Noise ◽  
Maneuvering Target

Maneuvering target tracking is a target motion estimation problem, which can describe the irregular target maneuvering motion. It has been widely used in the field of military and civilian applications. In the maneuvering target tracking, the performance of Kalman filter(KF) and its improved algorithms depend on the accuracy of process noise statistical properties. If there exists deviation between process noise model and the actual process, it will generate the phenomenon of estimation error increasing. Unbiased finite impulse response(UFIR) filter does not need priori knowledge of noise statistical properties in the filtering process. The existing UFIR filters have the problem that generalized noise power gain(GNPG) does not change with measurement of innovation. We propose an improved UFIR filter based on measurement of innovation with ratio dynamic adaptive adjustment at adjacent time. It perfects the maneuvering detect-ability. The simulation results show that the improved UFIR filter has the best filtering effect than KF when process noise is not accurate.

scholarly journals Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
Feng Han ◽  
Yu Hu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Probability ◽  
Data Association ◽  
Joint Probability ◽  
Nonstationary Process ◽  
Maneuvering Target Tracking ◽  
Process Noise ◽  
Maneuvering Target ◽  
Novel Method

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

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