Tracking and data association
Gaussian mixture CPHD filter with gating technique
Signal Processing
The bin-occupancy filter and its connection to the PHD filters
IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Bayesian Filtering With Random Finite Set Observations
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
CPHD Filtering With Unknown Clutter Rate and Detection Profile
IEEE Transactions on Signal Processing
| Hi-index | 0.08 |
To solve the general multi-target tracking (MTT) problem, an improved Sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter called as Sigma-gating SMC-PHD filter, is proposed that updates particles only using the local nearby measurements inside a specified sigma-gate. The sigma-gate is based on the given measurement noise, e.g. 3@s, where @s is the standard deviation of the measurement noise. Correspondingly, a compensation strategy based on the cumulative distribution function of the measurement model is suggested. Eliminating the contribution of measurements lying outside the gate around the particle will highly reduce unnecessary computation and thus improve the overall processing speed. More importantly, this could shield the estimate from interference from the clutter outside the gate giving more robust and accurate estimation. Especially when the clutter density is high, our approach can yield a win-win that is much faster processing efficiency and better estimation accuracy (as compared with the standard PHD filter). This is demonstrated by simulations of the SMC-PHD filters using measurements of range and bearing, respectively.