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A new design for a Bayesian field track-before-detect processor was studied in a joint effort by Metron, Inc. and Raytheon Systems Company. The principal design distinction is that bins in state space are not invoked as in many other field approaches. Instead, sampling and interpolation of the log-odds density field is employed. The plant model, a Markov process, and the measurement log-likelihoods are thereby both more accurately represented, avoiding the losses from diffusion and misrepresentation of likelihoods common to other approaches. A companion performance model based upon the statistics of the log-odds density at the true target position and the statistics of the ambient field elsewhere provided predictions for expected log-odds density growth, time to declare a target at given false-alarm rate, and other critical dependencies. Experiments incorporating synthetic data based upon Gaussian target and noise models and with parameters chosen to approximate an IRST cruise missile detection problem were run. Comparisons between the actual Bayesian tracking results and the analytical predictions showed good agreement. In addition, single-target comparative runs using identical synthetic data were made between the Raytheon multiple-hypothesis tracker (MHT) and the present single-target Bayesian field processor, verifying both that the processing gain of the Bayesian processor was close to optimal and that the resulting gain in single-target sensitivity relative to the unaided MHT baseline was approximately 6 dB. It is believed that such a sensitivity difference is characteristic of any track-file based method compared against the present Bayesian held approach. It does not, however address the added multiple-target, interacting multiple model (1MM), and other functions which MHT provides. Recognizing this, exploratory efforts to employ the track-before-detect processor as a front end to the Raytheon MHT were undertaken which achieved target detection with a single false alarm in a realistically sized surveillance space, suggesting that this hybrid architecture might provide a good design option which complements the sensitivity of the Bayesian field approach with the robustness, efficiency, and added multiple-target capabilities of the MHT. Keywords: Bayesian, tracking, detection, MHT, track-before-detect
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