Particle filtering methods for incorporating intelligence updates
Nunez, Jesse A.
Singham, Dashi I.
Atkinson, Michael P.
MetadataShow full item record
Due to uncertainty in target locations, stochastic models are implemented to provide a representation of location distribution. The reliability of these models has a profound effect on the ability to successfully interdict these targets. A key factor in the reliability of a model is the incorporation of information updates. A common method for incorporating information updates is Kalman filtering. However, given the probable nonlinear and non-Gaussian nature of target movement models, the fidelity of solutions provided by Kalman filtering could be significantly degraded. A more robust methodology needs to be employed. This thesis uses an updating algorithm known as particle filtering to incorporate information updates concerning the target's position. Particle filtering is a nonparametric filtering technique that is adaptable and flexible. The particle filter is incorporated into a model that uses a stochastic process known as a Brownian bridge to model target movement. A Brownian bridge models target movement with minimal information and allows for uncertainty during periods when target location is unknown. As new intelligence arrives, the particle filter is used to update a probabilistic heat map of target position. The main goal of this thesis is to design a stochastic model integrating both the Brownian bridge model and particle filtering.
Approved for public release; distribution is unlimited
Showing items related by title, author, creator and subject.
Nunez, Jesse A.; Singham, Dashi I.; Atkinson, Michael P. (Taylor and Francis Group, 2019);We study the problem of modelling the trajectory of a moving object of interest, or target, given limited locational and temporal information. Because of uncertainty in information, the location of the target can be ...
Cheng, Chun Chieh (Monterey, California: Naval Postgraduate School, 2016-09);The Brownian bridge movement model (BBMM) models target movement between two known points as a Brownian bridge. This thesis extended the BBMM to account for multiple starting and ending points and to account for intelligence ...
Two models of time constrained target travel between two endpoints constructed by the application of Brownian motion and a random tour Comstock, William Justin (Monterey, California. Naval Postgraduate School, 1983-03);A target must chose a path between some origin and destination. The total travel times and the target speed are specified, and the target wishes to maximize the "randomness" of its track subject to the spatial and temporal ...