Optimal Sensor Placement for Doppler-Only Target Tracking: ID Target Motion Case

Ayazgok, Suleyman
Orguner, Umut
This paper studies the optimal sensor placement problem for Doppler-only target tracking. A ID target motion is considered on a road/line segment and the optimization criterion for sensor placement is selected to be the total position CramerRao 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 suboptimal strategy is shown to obtain very close sensor positions and very similar cost values to the optimal strategy. The merits of the proposed sensor placement strategy are illustrated on a simple target tracking example.


Target tracking and sensor placement for doppler–only measurements
Ayazgök, Süleyman; Orguner, Umut; Department of Electrical and Electronics Engineering (2015)
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 bootst...
Recent results on Bayesian Cramér-Rao bounds for jump Markov systems
Fritsche, Carsten; Orguner, Umut; Svensson, Lennart; Gustafsson, Fredrik (2016-07-08)
In this paper, recent results on the evaluation of the Bayesian Cramer-Rao bound for jump Markov systems are presented. In particular, previous work is extended to jump Markov systems where the discrete mode variable enters into both the process and measurement equation, as well as where it enters exclusively into the measurement equation. Recursive approximations are derived with finite memory requirements as well as algorithms for checking the validity of these approximations are established. The tightnes...
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...
Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...
Extended Target Tracking using a Gaussian-Mixture PHD Filter
Granstrom, Karl; Lundquist, Christian; Orguner, Umut (2012-10-01)
This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. S...
Citation Formats
S. Ayazgok and U. Orguner, “Optimal Sensor Placement for Doppler-Only Target Tracking: ID Target Motion Case,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53654.