This paper describes a nonlinear filter for ground target tracking. Hospitability for maneuver derived from terrain, road and vehicle dynamics constraints is incorporated directly into the filter's motion model. The conditional probability density for the target state is maintained and updated with sensor measurements as soon as they become available. The conditional density is time updated bet...

Many radar and sonar sensor systems generate several point measurements at every scan. Some measurements are due to targets and others are due to clutter, or scatterers, in the sensor field of view. The multitarget tracking problem is to estimate the number of targets and their states given the measurements. The multi-hypothesis tracking (MHT) method for solving this problem is based on two wid...

Nonlinear estimation problems, such as range-only and bearing-only target tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions (pdfs). In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators a...

this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman's filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman's filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients...

In this paper, we investigate methods for optimal morphological pattern recognition. The task of optimal pattern recognition is posed as a solution to a hypothesis testing problem. A minimum probability of error decision rule-maximum a posteriori filter-is sought. The classical solution to the minimum probability of error hypothesis testing problem, in the presence of independent and identicall...

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, manageme...

Abelian group, 278 acceptance probability, 24, 37, 246 adaptive Metropolis algorithm, 33 Akaike’s information criterion, 209, 320 alpha-beta recursion, 11, 390 annealed importance sampling, 321 aperiodic chain, 23 APF, see auxiliary particle filter AR model, see autoregressive model assumed density filtering, 17, 143, 388 autoregressive hidden Markov model, 185 autoregressive model, 4, 9, 111 a...

This paper addresses the density based multi-sensor cooperative fusion using random finite set (RFS) type multi-object densities (MODs). Existing methods use scalar weights to characterize relative information confidence among local MODs, and in this way portion of contribution each MOD fused global can be tuned via adjusting these weights. Our analysis shows that mechanism a coefficient oversi...

We propose a novel consensus notion, called “partial consensus”, for distributed GM-PHD (Gaussian mixture probability hypothesis density) fusion based on a peer-to-peer (P2P) sensor network, in which only highly-weighted posterior Gaussian components (GCs) are disseminated in the P2P communication for fusion while the insignificant GCs are not involved. The partial consensus does not only enjoy...