Target tracking and sensor placement for doppler–only measurements

Download
2015
Ayazgök, Süleyman
This thesis investigates the problems of target tracking and optimal sensor placement with Doppler-only measurements. First, a single point track initialization algorithm proposed in the literature is investigated for Doppler-only tracking. The initialization algorithm is based on separable least squares method and involves a grid-based optimization. Second, particle filters are considered for Doppler-only tracking and they are compared to an extended Kalman filter (EKF). It is shown that a classical bootstrap particle filter, rather surprisingly, is inferior to the EKF in a Doppler-only tracking scenario. The reasons for this strange behavior are discussed. Then, classical sequential Monte Carlo tools are investigated to improve the behavior of the bootstrap particle filter. In this regard, two new particle filters, namely, a sequential importance resampling particle filter with optimal proposal distribution and a Rao-Blackwellized particle filter are derived and implemented. The results show that, although there are occasional improvements in the particle filter performance for some specific parameter selections, the improvement mechanisms employed are not sufficiently effective to make the particle filters beat EKF. Finally the problem of optimal sensor placement is considered for Doppler-only tracking. A 1D target motion is considered on a road/line segment and the optimization criterion for sensor placement is selected to be the total position Cramer Rao Lower Bound (CRLB) over the road/line segment. The results obtained using numerical optimization tools are utilized to propose a simple sub-optimal sensor placement strategy with explicit formulae for the sensor positions. The proposed strategy is shown to have very close cost values to the optimal strategy.

Suggestions

Multi-target tracking using passive doppler measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
Target tracking with correlated measurement noise
Okşar, Yeşim; Demirbaş, Kerim; Department of Electrical and Electronics Engineering (2007)
A white Gaussian noise measurement model is widely used in target tracking problem formulation. In practice, the measurement noise may not be white. This phenomenon is due to the scintillation of the target. In many radar systems, the measurement frequency is high enough so that the correlation cannot be ignored without degrading tracking performance. In this thesis, target tracking problem with correlated measurement noise is considered. The correlated measurement noise is modeled by a first-order Markov m...
Tracking of multiple ground targets in clutter with interacting multiple model estimator
Korkmaz, Yusuf; Baykal, Buyurman; Department of Electrical and Electronics Engineering (2013)
In this thesis study, single target tracking algorithms including IMM-PDA and IMM-IPDA algorithms; Optimal approaches in multitarget tracking including IMM-JPDA, IMM-IJPDA and IMM-JIPDA algorithms and an example of Linear Multi-target approaches in multitarget tracking including IMM-LMIPDA algorithm have been studied and implemented in MATLAB for comparison. Simulations were carried out in various realistic test scenarios including single target tracking, tracking of multiple targets moving in convoy fashio...
Multi-target tracking with PHD filter using Doppler-only measurements
Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01)
In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation...
Target Localization and Velocity Estimation Methods for Frequency-Only MIMO Radars
Kalkan, Yilmaz; Baykal, Buyurman (2011-05-27)
Target localization and the velocity estimation methods are proposed for frequency-only MIMO Radar with widely separated stations. For the target localization, time of arrival (TOA), angle of arrival (AOA) and frequency of arrival (FOA) informations can be used in cooperation. When the time resolution of the transmitted signals is not enough or good,i.e.; unmodulated CW radar, we can not rely on the TOA information to localize the target. On the other hand, if the frequency resolution of the transmitted sig...
Citation Formats
S. Ayazgök, “Target tracking and sensor placement for doppler–only measurements,” M.S. - Master of Science, Middle East Technical University, 2015.