Dr. Xu-Hong Xiao Profile

Dr. Xu-Hong Xiao

at DSO National Labs

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Publications (2)

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Proceedings Article | 28 March 2005 Paper

A group tracking algorithm for ground vehicle convoys

Wei Chin, Hian Lee, Xu Xiao, Gee Ng, Khee How

Proceedings Volume 5813, (2005) https://doi.org/10.1117/12.603202

KEYWORDS: Detection and tracking algorithms, Monte Carlo methods, Filtering (signal processing), Algorithm development, Sensors, Target detection, Error analysis, Brain-machine interfaces, Data processing, Data fusion

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Tracking multiple ground targets under clutter and in real time poses several likely challenges: vehicles often get masked by foliage or line-of-sight (LOS) problems, manifesting in misdetections and false alarms. Further complications also arise when groups of vehicles merge or split. This paper presents an attempt to address these issues using a group tracking approach. Group tracking is a way to ameliorate, or at least soften the impact of such issues from the hope that at least partial information will be received from each target group even when the probability of detection, PD of each individual member is low. A Strongest Neighbour Association (SNA) method of measurement-to-track association based on space-time reasoning and track-measurement similarity measures has been derived. We combine the association strengths of the space-time dynamics, the degree-of-overlap and the historical affinity metrics to relate measurements and tracks. The state estimation is based on standard Kalman filter. Lastly, a Pairwise Historical Affinity Ratios (PHAR) is proposed for the detection of a split scenario. This method has been tested to work well on a simulated convoy-splitting scenario. Monte Carlo experiment runs of six different error rates with five different compositions of errors have been conducted to assess the performance of the tracker. Results indicated that the group tracker remains robust (>80% precision) even in the presence of high individual source track error rates of up to 30%.

Proceedings Article | 28 March 2005 Paper

Combination of multisource information to ground target tracking

Xu Xiao, Hian Lee, Wei Chin, Gee Ng, Khee How

Proceedings Volume 5813, (2005) https://doi.org/10.1117/12.603229

KEYWORDS: Detection and tracking algorithms, Target detection, Surveillance, Data processing, Sensors, Artillery, Data acquisition, Target acquisition, Roads, Solids

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Ground targets can be detected by multiple classes of sources in military surveillance. There are two main challenges for the acquisition of ground situation picture from data collected by multiple sources. First, different sources provide different information that describes military entities at different granularities and accuracies. This makes processing of data in one unified tracker difficult. Secondly, the data update rates of these sources vary, some update rates could be very low (such as hours), leading to greater difficulty for data association. This paper presents our attempt in multi-source ground target tracking, taking the above two issues into consideration. Targets are tracked in groups, and multiple trackers are designed so that data of different granularities are processed by the respective trackers. Tracks from these trackers are then correlated to form the common picture. Two strategies are proposed to handle the problem of varying data update rate. The first strategy is to exploit different approaches to calculate the beliefs of data association according to update rates. When update rate is high, the belief is calculated by a distance function based on estimated kinematical states. When update rate is low, the belief of data association is computed using Bayesian network. Bayesian network infers the beliefs based on observed information and domain knowledge. The second strategy is to exploit the complementary information in different trackers to improve data association. The first step is to find the correlation among tracks from different trackers. This track-track correlation information is fed back to modify the beliefs of data associations in the tracks. Experiments demonstrated that such combination of multi-source information not only produces more complete ground picture, but also helps to improve the data association accuracy in the respective trackers.

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