Limits...
An Improved Particle Filter for Target Tracking in Sensor Systems

View Article: PubMed Central

ABSTRACT

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented Particle Filter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking.

No MeSH data available.


Errors of different filters versus the process noises in a single run.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3756717&req=5

f3-sensors-07-00144: Errors of different filters versus the process noises in a single run.

Mentions: In Figure 2 the estimated results generated from a single run of the different filters are compared, where the particle number is 200 in each particle filter. Figure 3 shows the corresponding errors of different filters versus the process noises.


An Improved Particle Filter for Target Tracking in Sensor Systems
Errors of different filters versus the process noises in a single run.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756717&req=5

f3-sensors-07-00144: Errors of different filters versus the process noises in a single run.
Mentions: In Figure 2 the estimated results generated from a single run of the different filters are compared, where the particle number is 200 in each particle filter. Figure 3 shows the corresponding errors of different filters versus the process noises.

View Article: PubMed Central

ABSTRACT

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented Particle Filter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking.

No MeSH data available.