Showing papers by "Neil Gordon published in 1995"
TL;DR: A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter.
Abstract: The bootstrap filter is an algorithm for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples that are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: It may be applied to any state transition or measurement model. A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter. A preliminary investigation of an application of the bootstrap filter to an exoatmospheric engagement with non-Gaussian measurement errors is also given.
168 citations
21 Jun 1995
TL;DR: The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered and a new hybrid bootstrap filter is proposed, an approach where random samples are used to represent the target posterior distributions.
Abstract: The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered A new hybrid bootstrap filter is proposed The bootstrap filter is an approach where random samples are used to represent the target posterior distributions By using this approach, the author circumvents the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models
131 citations
TL;DR: In this article, a near intrinsic active region bounded by more highly doped contact regions was found to exhibit positive or negative luminescence at medium to long infrared wavelengths when forward or reverse biased respectively at room temperature.
52 citations
01 Sep 1995
TL;DR: In this article, a random sample based implementation of a Bayesian recursive filter is proposed for tracking targets with radar, which is based on the Metropolis-Hastings algorithm and the Gaussian sum approach.
Abstract: When tracking targets with radar, changes in target aspect with respect to the observer can cause the apparent center of radar reflections to wander significantly. The resulting noisy angle errors are called target glint. Glint may severely affect the tracking accuracy, particularly when tracking large targets at short ranges (such as might occur in the final homing phase of a missile engagement). The effect of glint is to produce heavy-tailed, time correlated non-Gaussian disturbances on the observations. It is well known that the performance of the Kalman filter degrades severely in the presence of such disturbances. In this paper we propose a random sample based implementation of a Bayesian recursive filter. This filter is based on the Metropolis-Hastings algorithm and the Gaussian sum approach. The key advantage of the filter is that any nonlinear/non-Gaussian system and/or measurement models can be routinely implemented. Tracking performance of the filter is demonstrated in the presence of glint.
8 citations