21 September 2007 Joint MAP bias estimation and data association: algorithms
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Abstract
The problem of joint maximum a posteriori (MAP) bias estimation and data association belongs to a class of nonconvex mixed integer nonlinear programming problems. These problems are difficult to solve due to both the combinatorial nature of the problem and the nonconvexity of the objective function or constraints. A specific problem that has received some attention in the tracking literature is that of the target object map problem in which one tries match a set of tracks as observed by two different sensors in the presence of biases, which are modeled here as a translation between the track states. The general framework also applies to problems in which the costs are general nonlinear functions of the biases. The goal of this paper is to present a class of algorithms based on the branch and bound framework and the "all-pairs" and k-best heuristics that provide a good initial upper bound for a branch and bound algorithm. These heuristics can be used as part of a real-time algorithm or as part of an "anytime algorithm" within the branch and bound framework. In addition, we consider both the A*-search and depth-first search procedures as well as several efficiency improvements such as gating. While this paper focuses on the algorithms, a second paper will focus on simulations.
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Scott Danford, Scott Danford, Bret D. Kragel, Bret D. Kragel, Aubrey Poore, Aubrey Poore, } "Joint MAP bias estimation and data association: algorithms", Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66991E (21 September 2007); doi: 10.1117/12.735202; https://doi.org/10.1117/12.735202
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