Samuel J. Davey

Learn More
A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point-measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches(More)
Authors’ addresses: S. J. Davey, M. G. Rutten, and B. Cheung, Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, PO Box 1500, Edinburgh, South Australia 5111, Australia, E-mail: (samuel.davey@dsto.defence.gov.au); S. J. Davey and B. Cheung are also with the School of Electrical and Electronic Engineering, The(More)
Sensor fusion is the notion of combining the data from two or more sensors in order to obtain enhanced performance compared with that of the individual sensors. In addition, Signal Detection Theory can be used to monitor how well a sensor operates. That is, through the number of hits, misses, false alarms and correct rejections a sensor registers, we gain a(More)
This paper demonstrates how the data-association technique known as the probabilistic multi-hypothesis tracker (PMHT) can be applied to the feature-based simultaneous localization and map building (SLAM) problem. The main advantage of PMHT over other conventional data-association techniques is that it has low computational complexity, while still providing(More)
Tracking, Association, and Classification: A Combined PMHT Approach S. Davey,∗,† D. Gray,∗ and R. Streit‡ ∗Cooperative Research Center for Sensor Signal and Information processing, Australia, and Electrical and Electronic Engineering Department, The University of Adelaide, South Australia, Australia, †Surveillance Systems Division, DSTO, Australia; and(More)
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. The original implementations of H-PMHT dealt with Gaussian shaped targets with fixed or known extent. More recent applications have addressed other special cases of the target shape. This article reviews these recent extensions and(More)
The conventional tracking paradigm is to sequentially apply a single frame detector to each sensor frame and then employ a tracking algorithm to determine which detector outputs originate from targets and which are false alarms. The tracker associates detections from a particular target and estimates parameters of interest for the target [3], [1]. The(More)
Conventional tracking approaches are based on the assumption that the targets to be tracked are point targets and that the measurements to be processed are point measurements. However, when a sensor provides image data of high resolution in which targets might be distributed over several display cells, neither assumption is suitable. In such applications(More)
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. Recent comparisons have shown that it can give performance close to numerical approximations to the optimal Bayesian filter at a fraction of the computation cost. The derivation of H-PMHT makes no explicit assumption about the(More)