Kalman-gain Particle filter Sequential Monte Carlo (SMC) Probability hypothesis density (PHD) filter Multitarget tracking (MTT) Bayesian tracking
DANIYAN, A. ... et al, 2017. Kalman-gain aided particle PHD filter for multi-target tracking. IEEE Transactions on Aerospace and Electronic Systems, doi:10.1109/TAES.2017.2690530.
We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error (MSE) between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.
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This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1, the MOD University Defence Research Collaboration (UDRC) in Signal Processing, UK and the Petroleum Technology Development Fund (PTDF), Nigeria.