Hu Wenlong, Mao Shiyi . MULTISENSOR DATA ASSOCIATION APPROACH BASED UPON COMBINATORIAL OPTIMIZATION[J]. Journal of Electronics & Information Technology, 1996, 18(6): 561-566.
Citation:
Hu Wenlong, Mao Shiyi . MULTISENSOR DATA ASSOCIATION APPROACH BASED UPON COMBINATORIAL OPTIMIZATION[J]. Journal of Electronics & Information Technology, 1996, 18(6): 561-566.
Hu Wenlong, Mao Shiyi . MULTISENSOR DATA ASSOCIATION APPROACH BASED UPON COMBINATORIAL OPTIMIZATION[J]. Journal of Electronics & Information Technology, 1996, 18(6): 561-566.
Citation:
Hu Wenlong, Mao Shiyi . MULTISENSOR DATA ASSOCIATION APPROACH BASED UPON COMBINATORIAL OPTIMIZATION[J]. Journal of Electronics & Information Technology, 1996, 18(6): 561-566.
For a tracking system consisting of heterogeneous sensors such as radar and IRST, the combinatorial assignment is applied to solve the multitarget data association which is formulated as a partition of the multiple dimension data set including the measurements and predicted tracks from targets.
Farina A, Studer F A, Radar data processing, Vo1.2. New York: John Wiley and Sons, 1988.[2]Coleman J O. Discriminats for assigning passive bearing observations to radar targets. Proc. of 1988 IEEE Int Radar Conf. Washington D. C. USA: 1988, 361-355.[3]Pattipati K R, et al. A new relaxation algorithm and passive sensor data assciation problem. IEEE Trans.on AC, 1992, AC-37(2): 198-213.[4]Kurien T. Issues in the design of practical multitarget tracking algorithms. in: Bar-Shalom Y, eds. Multitarget-multisensor tracking: advanced applications, NorwAod, MA: Artech, 1990.[5]Fortmann T E, Bar-Shalom Y, Scheffe M. Sonar tracking of multiple target using joint probabilistic[6]data association. IEEE J. of Oceanic Engineering, 1983, OE-8(7): 173-I84.[7]Fitzgerald R J. Tack biases and coalescence with probabilistic data association. IEEE Trans. on AES, 1993, AES-21(3): 822-825.