Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets

Abstract

The majority of tactical weapons systems require that manned maneuverable vehicles, such as aircraft, ships, and submarines, be tracked accurately. An optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets. Using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when maneuvering targets are to be tracked by sensors providing any combination of range, bearing, and elevation measurements. Manuscript

From Saliency to Eye Gaze: Embodied Visual Selection for a Pan-Tilt-Based Robotic Head

“…For the characterization of f we shall consider position, velocity and acceleration along a single spatial dimension, respectively. For the saccade the dynamic model is the Singer model [17]. In this model the target acceleration is correlated in time, with correlation given by σ 2 exp(−α|τ |), α > 0, where σ 2 is the variance of the saccade acceleration and α is the reciprocal of the saccadic behavior, in so depending on the milliseconds needed to accomplish a saccade by the embodied system.…”

“…Therefore it can be determined by α 2 max /3(1+3p max −p 0 ), where α max is the maximum rate of acceleration, with probability p max and p 0 is the probability of no acceleration. The discrete time representation of the continuous model, see [17], is…”

“…Here T = 1/30 is the sampling rate, given by the framerate, u t is the inhomogeneous driving input whose variance is derived in [17], and Ψ (T, α) is, under the saccadic process, the state transition matrix:…”

GNSS Positioning Algorithms by Gaussian Sum Filtering Methods

“…On the other hand, for the kinematic positioning, certain dynamical model of the moving object is required. For example, in the so-called Singer's model [10], the acceleration of the vehicle is assumed as a first order Markov process. Then the state vector θ is defined as it includes vehicle's velocity vector v and acceleration vector a, and the state equation can be formulated as follows.…”

Automatic ship positioning and radar biases correction using the hausdorff distance

“…The choice of filter parameters Q, R and the initial error covariance matrix P(0|0) are crucial for a good performance of the filter. Some well-known approaches to make this selection rigorous can be found in [16] and [17]. In this paper, the practical approach suggested in [18] is followed for the initial estimate of the state estimation error covariance matrix, setting large values in its diagonal ( ).…”