3 April 2000 Target detection and recognition using Markov modeling and probability updating
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While extract methods (e.g., jump-diffusion algorithms) for performing maximum a posteriori (MAP) target detection and recognition can be very complex and computationally expensive, it is often not clear how to develop effective and less complex suboptimal methods. Also, MAP algorithms typically generate hard decisions, but for fusion applications it would often be more desirable to have probabilities or confidence levels for a range of alternatives. In this paper, we consider the application of a framework called probability propagation in Bayesian networks. This framework organizes computations for iterated approximations to posterior probabilities, and has been used recently by communications researchers to derive very effective iterative decoding algorithms. In this paper, we develop a Bayesian network model for the problem of target detection and recognition, and use it in conjunction with Markov models for target regions to derive a probability propagation algorithm for estimating target shape and label probabilities.
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Patrick A. Kelly, Patrick A. Kelly, Haluk Derin, Haluk Derin, Renjian Zhao, Renjian Zhao, } "Target detection and recognition using Markov modeling and probability updating", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); doi: 10.1117/12.381642; https://doi.org/10.1117/12.381642

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