On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking

Skoglar, Per
Orguner, Umut
Gustafsson, Fredrik
In this work we consider a target tracking scenario where a moving observer with a bearings-only sensor is tracking a target. The tracking performance is highly dependent on the trajectory of the sensor platform, and the problem is to determine how it should maneuver for optimal tracking performance. The problem is considered as a stochastic optimal control problem and two sub-optimal control strategies are presented based on the Information filter and the determinant of the information matrix as the optimization objective. Using the determinant of the information matrix as an objective function in the planning problem is equivalent to using differential entropy of the posterior target density when it is Gaussian. For the non-Gaussian case, an approximation of the differential entropy of a density represented by a particle mixture is proposed. Furthermore, a gradient approximation of the differential entropy is derived and used in a stochastic gradient search algorithm applied to the planning problem.


Performance Comparison of Target Tracking Algortihms in Underwater Environment
Ege, Emre; Saranlı, Afşar (2008-04-22)
Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target's true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, bec...
Improved Target Tracking with Road Network Information
Orguner, Umut; Gustafsson, Fredrik (2009-03-14)
In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or one-way roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and...
Learning drag coefficient of ballistic targets using gaussian process modeling
Kumru, Fırat; Özkan, Emre; Department of Electrical and Electronics Engineering (2019)
Ballistic object tracking involves estimating an unknown ballistic coefficient which directly affects the dynamics of the object. In most studies, the ballistic coefficient is assumed to be constant throughout the object’s flight. In reality, the ballistic coefficient is a function of the speed of the object and depends on the object’s aerodynamic properties. In the literature, the impact point prediction is defined as predicting the position that the object is expected to hit on the ground while the object...
Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference
Tuncer, Barkın; Orguner, Umut; Özkan, Emre (2022-01-01)
In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive fil...
A Knowledge based approach in GMTI for the estimation of the clutter covariance matrix in space time adaptive processing
Anadol, Erman; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2012)
Ground Moving Target Indication (GMTI) operation relies on clutter suppression techniques for the detection of slow moving ground targets in the presence of strong radar returns from the ground. Space Time Adaptive Processing (STAP) techniques provide a means to achieve this goal by adaptively forming the clutter suppression filter, whose parameters are obtained using an estimated covariance matrix of the clutter data. Therefore, the performance of the GMTI operation is directly a ected by the performance o...
Citation Formats
P. Skoglar, U. Orguner, and F. Gustafsson, “On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33029.