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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pattipati, Krishna R. Deb, Somnath Bar-Shalom, Yaakov Washburn, Robert B. |
| Copyright Year | 1989 |
| Description | Author affiliation: U-157, Department of Electrical and Systems Engineering, University of Connecticut, Storrs, CT 06269-3157 (Pattipati, Krishna R.; Deb, Somnath; Bar-Shalom, Yaakov) || Alphatech, Inc., 2 Burlington Executive Center, 111 Middlesex Turnpike, Burlington, MA 01803 (Washburn, Robert B.) |
| Abstract | This paper is concerned with the problem of associating measurements from multiple angle-only sensors in the presence of clutter, missed detections and unknown number of targets. The measurement-target association problem is formulated as one of maximizing the joint likelihood function of the measurement partition. Mathematically, this formulation of the data association problem leads to a generalization of the multi-dimensional matching (assignment) problem, which is known to be NP-complete when the number of sensors S ¿ 3, i.e., the complexity of an optimal algorithm increases exponentially with the size of the problem. The new solution to the optimization problem developed in this paper is a Lagrangian relaxation technique that successively solves a series of generalized two-dimensional assignment problems, with the worst case complexity of max [O(S $n^{3}),$ O(M)], where n is the number of reports from each sensor and M is the number of possible measurement-target associations. The dual optimization problem is solved via an accelerated subgradient method. A useful feature of the relaxation approach is that the resulting dual optimal cost is a lower bound on the feasible cost and, hence, provides a measure of how close the feasible solution is to the (perhaps unknowable) optimal solution. For the passive sensor data association problem, the feasible solution costs are typically within 1% of their corresponding dual optimal costs. The algorithm is illustrated via several examples. |
| Starting Page | 2617 |
| Ending Page | 2627 |
| File Size | 1207518 |
| Page Count | 11 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1989-06-21 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Cost function Position measurement Polynomials Size measurement Lagrangian functions Acceleration Background noise Noise measurement Particle measurements Target tracking |
| Content Type | Text |
| Resource Type | Article |
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