Titlebook: Locating, Classifying and Countering Agile Land Vehicles; With Applications to David D. Sworder,John E. Boyd Book 2016 Springer Internation

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Book 2016to illustrate the tradeoffs in a simple and direct manner. The text covers the Gaussian wavelet estimator (GWE) which has a flexible architecture that is able to fuse uncommon sensor combinations with non-temporal structural constraints.

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Classification and Target Tempo,r explores the utility of the . for target classification in a noisy measurement environment. We show that in some circumstances the maneuver characteristics of a target are sufficient to give an indication of target type. The result holds promise for the architect of a command system.

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The Gaussian Wavelet Estimator,nstraints of an actual engagement. We propose specific adjustments that we find plausible without any claim that they are optimal in a global sense. Again we note that the notation in the Appendix is very demanding; please refer to the Notation Conventions and Details section in Chap. .

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Target Location Using the Extended Kalman Filter,ng an unpredictable path. From distribution we can not only accurately estimate the location of the target, but we can also place a capture region about the estimate that will contain the target with any pre-specified probability. The Kalman algorithm is based upon a linear-Gauss–Markov (LGM) model

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Serendipitous Events in Tracking and Classification, that can be used in a hybrid-tracking algorithm to improve the tracking quality. In this chapter, a particular engagement is chosen to illustrate the utility of event inclusion. The following analysis is quite detailed and engagement specific. The encounter is chosen to illustrate the advantage of

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