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We consider the problem of state estimation for a dynamic system driven by unobserved, correlated inputs. We model these inputs via an uncertain set of temporally correlated dynamic models, where this uncertainty includes the number of modes, their associated statistics, and the rate of mode transitions. The dynamic system is formulated via two interacting graphs: a hidden Markov model (HMM) and a...
The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity...
Tracking maneuvering targets presents a great challenge to airborne surveillance radar signal processing and sensor systems management systems. Smears caused by an uncompensated maneuver (either translational or rotational) affect target identification (ID) with distorted target images. An unexpected maneuver introduces large position estimation errors to a tracker and in the worst case loss of track...
The focus of this paper is on examining the accuracy of two existing state vector fusion methods, weighted covariance fusion (WCF) and information matrix fusion (IMF), in a multi-sensor environment for computing the fused estimates from distributed Kahnan filters tracking a single maneuvering target. Each sensor tracker utilized in the Reference Cartesian Coordinate System (RCCS) is described for...
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