Abstract
We present a Bayesian approach for multiple target tracking. Target location and velocity are deduced probabilistically through a sequence of continuous observations of amplitude and frequency made by Doppler Radar sensors.
To improve the accuracy and robustness of the estimation, the system utilizes a time framed process model which retroactively fuses the delayed measurements into the target state history. Time is discretized into a sequence of continuous time frames used to stamp the measurements. Estimation is made probabilistically at the end of each frame considering the current measurements of amplitude and frequency and previous target states. The system demonstrated accurate results in tests conducted on simulated and hardware tracking environments.
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Vargas, J.E., Tvalarparti, K., Wu, Z. (2003). Target Tracking with Bayesian Estimation. In: Lesser, V., Ortiz, C.L., Tambe, M. (eds) Distributed Sensor Networks. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0363-7_5
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DOI: https://doi.org/10.1007/978-1-4615-0363-7_5
Publisher Name: Springer, Boston, MA
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