Bayesian Multiple Target Tracking by Lawrence D Stone

By Lawrence D Stone

This moment variation has gone through large revision from the 1999 first variation, spotting lot has replaced within the a number of objective monitoring box. probably the most dramatic adjustments is within the common use of particle filters to enforce nonlinear, non-Gaussian Bayesian trackers. This e-book perspectives a number of goal monitoring as a Bayesian inference challenge. inside this framework it develops the speculation of unmarried objective monitoring. as well as offering a close description of a uncomplicated particle clear out that implements the Bayesian unmarried goal recursion, this source offers various examples that contain using particle filters.

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Bayesian Multiple Target Tracking

This moment version has gone through significant revision from the 1999 first variation, spotting lot has replaced within the a number of aim monitoring box. some of the most dramatic adjustments is within the common use of particle filters to enforce nonlinear, non-Gaussian Bayesian trackers. This booklet perspectives a number of objective monitoring as a Bayesian inference challenge.

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Over short time intervals (up to approximately 250 ms) the intensity observed in a fixed-range cell is Rayleighdistributed. Over longer times, the mean of the Rayleigh component follows a gamma distribution whose shape parameter is a function of the radar and ocean parameters such as grazing angle, resolution cell size, frequency, look direction, and sea state. Since the radar is looking for a submarine periscope, it is reasonable to assume that there is at most one target at any time within a 10-nm radius of the ship.

It is also general and powerful. We can substitute any motion model that we can simulate for the one used above. We can incorporate measurements from any sensor for which we can calculate a likelihood function. We can incorporate information from multiple sensors, colocated or not, and the sensors can be disparate producing different types of measurements. 8 we see that the initial bearing produces a probability distribution on target location with substantial range uncertainty. 5 nm from ownship at the time of the initial bearing.

4, 1972, pp. 439–448. [9] Blom, H. A. , and Y. Bar-Shalom, “The interacting multiple model algorithm for systems with Markovian switching coefficients,” IEEE Trans. Automatic Control, Vol. 33, August 1988, pp. 780-783. , and T. E. Fortman, Tracking and Data Association, New York: Academic Press, 1988. , and R. , Modern Tracking Systems, Norwood: Artech House, 1999. [12] Benes, V. , “Exact finite dimensional nonlinear filters with certain diffusion nonlinear drift,” Stochastics, Vol. 5, 1981, pp.

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