187 research outputs found
Trajectory PHD and CPHD Filters
This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles. The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence. The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation. We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan computationally efficient implementations, which only update the density of the trajectory states of the last L time steps
Regional variance for multi-object filtering
Recent progress in multi-object filtering has led to algorithms that compute
the first-order moment of multi-object distributions based on sensor
measurements. The number of targets in arbitrarily selected regions can be
estimated using the first-order moment. In this work, we introduce explicit
formulae for the computation of the second-order statistic on the target
number. The proposed concept of regional variance quantifies the level of
confidence on target number estimates in arbitrary regions and facilitates
information-based decisions. We provide algorithms for its computation for the
Probability Hypothesis Density (PHD) and the Cardinalized Probability
Hypothesis Density (CPHD) filters. We demonstrate the behaviour of the regional
statistics through simulation examples
Robust Multi-target Tracking with Bootstrapped-GLMB Filter
This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters
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