Improved Target Tracking with Road Network Information

Orguner, Umut
Gustafsson, Fredrik
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 drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.


On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking
Skoglar, Per; Orguner, Umut; Gustafsson, Fredrik (2009-03-14)
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 optimi...
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...
Robust Automatic Target Recognition in FLIR imagery
Soyman, Yusuf (2012-04-24)
In this paper, a robust automatic target recognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then perform...
Enhancing GPS positioning accuracy from the generation of ground-truth reference points for on-road urban navigation
Bshara, Mussa; Orguner, Umut; Gustafsson, Fredrik; Van Biesen, Leo (2012-09-14)
The global positioning system (GPS) is a Global Navigation Satellite System (GNSS) uses a constellation of between 24 and 32 Medium Earth Orbit satellites that transmit precise microwave signals, which enable GPS receivers to determine their current location, the time, and their velocity [1]. Initially, the GPS was developed for military applications, but very quickly became the most used technology in positioning even for end-user applications run by individuals with no technical skills. GPS reading are us...
Bilgin, Ozan Ozgun; Demirekler, Mübeccel (2015-10-30)
In this study, dynamic models for thrusting and ballistic flight modes of multi mode projectile are obtained and Marginalization method is applied by separation of the linear and nonlinear parts of state space model. In Marginalized Particle Filter (MPF), dimension of the nonlinear system is reduced so that the model can be utilized to obtain better estimates of the state using the same number of particles as that of standard particle filter. The Extended Kalman Filter (EKF), the Particle Filter (PF) and th...
Citation Formats
U. Orguner and F. Gustafsson, “Improved Target Tracking with Road Network Information,” 2009, Accessed: 00, 2020. [Online]. Available: