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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gad, A. Majdi, F. Farooq, M. |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., R. Mil. Coll. of Canada, Kingston, Ont., Canada (Gad, A.; Majdi, F.; Farooq, M.) |
| Abstract | In tracking a single target in clutter, many algorithms have been developed ranging in complexity from nearest neighbor (NN) and probabilistic data association (PDA) to the optimal Bayesian filter. In multiple-target tracking, a number of the techniques have been exercised such as the JPDA and the multiple hypothesis (MHT) schemes. Sub-optimal algorithms, such as the PDA filter, have been used widely since the optimal algorithms have an exponentially increasing computational complexity since all the possible sequences of target-to-measurement associations must be considered. In this paper, the Viterbi algorithm (VA) is used to develop a parallel search data association algorithm, called the Viterbi Data Association (VDA) technique. This algorithm includes the gating, automatic track initiation and termination modules. Simulations have been carried out to verify the performance and the robustness of the proposed algorithms Moreover, the VDA algorithm is compared with the fuzzy data association (FDA) algorithm when tracking a target in a cluttered, low signal-to-noise ratio (SNR) environment. |
| Starting Page | 1126 |
| Ending Page | 1133 |
| File Size | 442552 |
| Page Count | 8 |
| File Format | |
| ISBN | 0972184414 |
| DOI | 10.1109/ICIF.2002.1020939 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-07-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Int. Soc. of Information Fusion |
| Subject Keyword | Target tracking Personal digital assistants Filters Viterbi algorithm Nearest neighbor searches Neural networks Bayesian methods Computational complexity Computational modeling Robustness |
| Content Type | Text |
| Resource Type | Article |
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