By Lawrence D Stone
This moment variation has gone through significant revision from the 1999 first version, spotting lot has replaced within the a number of aim monitoring box. probably the most dramatic alterations is within the common use of particle filters to enforce nonlinear, non-Gaussian Bayesian trackers. This ebook perspectives a number of goal monitoring as a Bayesian inference challenge. inside this framework it develops the speculation of unmarried aim monitoring. as well as offering an in depth description of a simple particle filter out that implements the Bayesian unmarried goal recursion, this source presents various examples that contain using particle filters.
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Extra resources for Bayesian Multiple Target Tracking
C. ), New York: Marcell Decker, 1988.  Sorenson, H. , “On the development of practical nonlinear filters,” Information Sciences, Vol. 7, 1974, pp. 253-270.  Stone, L. , C. A. Barlow, and T. L. Corwin, Bayesian Multiple Target Tracking, Norwood: Artech House, 1999. , N. de Freitas, and N. html).  Finn, M. pdf). Chapter 2 Bayesian Inference and Likelihood Functions The objective of tracking is to estimate the number and state of targets in a region of interest. The term state is used to indicate a vector of quantities that characterizes the object being tracked in a way that is discussed in Chapter 3.
To tackle this problem  used LRDT. The tracker employed consists of a clutter tracker and a target tracker. , the mean of the Rayleigh distribution mentioned above) in each range cell, every 1/5 of a second using the intensity of the radar returns. 1 Target Tracker Each beam of the radar is treated separately. The nominal periscope is up for only a short period of time, and the chance of transiting from one beam to another in this short period of time is small, especially with overlapping beams.
The elliptical uncertainty region of the measurement overlaps with two target distributions. The measurement could reasonably have been generated by either target. We now show how to compute the probability that a measurement is associated to a target and how to use this computation to perform soft association. 18 Position measurement that may be due to either target 1 or 2. In this example, we show the use of soft association for tracking two targets when the measurements are position estimates with bivariate normal errors.