Assessment of multi-hypothesis tracking performance for low signal-to-noise ratio targets

Abstract

The Multi-Hypothesis Tracker (MHT) is generally considered to be the best performing conventional tracker. It assesses the feasible association of sequences of measurements, calculates the probabilities of the association hypotheses and has track initiation capability. Whilst conventional tracking systems use a detection algorithm to extract measurements from the sensor data, Track-Before-Detect techniques remove the detection algorithm and supply all information received from the signal processing system as measurements to be associated and filtered by the tracker. We compare the performance of the MHT with that of a grid-based Hidden Markov Model Track-Before-Detect algorithm for low signal-to-noise ratio targets. The performances of the MHT and grid Hidden Markov Model algorithms are quantified using six measures: root-mean-square position error, overall detection probability, instantaneous detection probability, false track count, false track length, and computation resource. The grid Hidden Markov Model algorithm is found to have better detection and significantly better false track performance at the cost of computation resource. References

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