Proc. SPIE. 6236, Signal and Data Processing of Small Targets 2006
KEYWORDS: Tumor growth modeling, Detection and tracking algorithms, Data modeling, Digital filtering, Error analysis, Composites, Monte Carlo methods, Electronic filtering, Filtering (signal processing), Polonium
Multiple hypothesis trackers (MHTs) are widely accepted as the best means of tracking targets in the presence
of clutter. This research seeks to incorporate multiple model Kalman filters into an Integral Square Error (ISE)
cost-function-based MHT to increase the fidelity of target state estimation. Results indicate that the proposed
multiple model methods can properly identify the maneuver mode of a target in dense clutter and ensure that an
appropriately tuned filter is used. During benign portions of flight, this causes significant reductions in position
and velocity RMS errors compared to a single-dynamics-model-based MHT. During portions of flight when the
mixture mean deviates significantly from true target position, so-called deferred decision periods, the multiple
model structures tend to accumulate greater RMS errors than a single-dynamics-model-based MHT, but this
effect is inconsequential considering the inherently large magnitude of these errors (a non-MHT tracker would
not be able to track during these periods at all). The multiple model MHT structures do not negatively impact
track life when compared to a single-dynamics-model-based MHT.
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer's n-scan memory filter, Salmond's joining filter, and Chen and Liu's Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides track life performance which is significantly better than the compared techniques using similar numbers of mixture components, and performance competitive with the compared algorithms for similar mean computation times.