Track Before Detect Algorithms

Call for Papers

Seamless detection and tracking schemes are able to integrate unthresholded (or below target detection threshold) multiple sensor responses over time to detect and track targets in low signal-to-noise ratio (SNR) and high clutter scenarios. These schemes, also called “track-before-detect (TBD)” algorithms are especially suitable for tracking weak targets that would only very rarely cross a standard detection threshold as applied at the sensor level.

Thresholding sensor responses result in a loss of information. Keeping this information allows some TBD approaches to deal with the classical data association problem effectively in high clutter and low SNR situations. For example, in detection scenarios with simultaneous activation/illumination from different signal sources this feature allows the application of triangulation techniques, where in the case of contact tracking approaches essential information about weak targets would often be lost because these targets did not produce signals that cross the normal detection threshold. Extending this example to a multi-sensor network scenario, a TBD algorithm that can use unthresholded (or below threshold) data has the potential to show improved performance compared to an algorithm that relies on thresholded data. In low SNR situations, this can substantially increase performance particularly in the case of a dense multi-target scenario.

Naturally, TBD algorithms consume high computational processing power: An efficient realization and coding of the TBD scheme is mandatory.

Another issue that arises when using the TBD scheme is the quality of the sensor model: Practical experience with thresholded data shows that a coarser modelling of the likelihood function might be sufficient and often leads to robust algorithms. How much have these sensor models to be improved in order to allow the TBD algorithms to exploit the information provided with the unthresholded data?

TBD algorithms that are well known to the tracking community are the likelihood ratio detection and tracking (LRDT), maximum likelihood probabilistic data association (MLPDA ), maximum likelihood probabilistic multihypothesis tracking (MLPMHT), Hough transform based methods and dynamic programming techniques; also related are the probability hypothesis density (PHD), the histogram probabilistic multi- hypothesis tracking (H-PMHT) algorithms, and, of course, various particle filter approaches. Some of these algorithms are capable of tracking extended targets and performing signal estimation in multi-sensor measurements.

The aim of this special issue is to focus on recent developments in this expanding research area. The special issue will focus on one hand on the development and comparison of algorithmic approaches, and on the other hand on their currently ever-widening range of applications such as in active or passive surveillance scenarios (e.g. for object tracking and classification with image and video based sensors, or scenarios involving chemical, electromagnetic and acoustic sensors). Special interest lies in multi-sensor data fusion and/or multi-target tracking applications.

Authors should follow the EURASIP Journal on Advances in Signal Processing manuscript format described at the journal site http://www.hindawi.com/journals/asp/. Prospective authors should submit an electronic copy of their complete manuscript through the EURASIP JASP Manuscript Tracking System at http://www.hindawi.com/mts/, according to the following timetable:

Manuscript DueApril 1, 2007
First Round of ReviewsJuly 1, 2007
Publication DateNovember 1, 2007

Guest Editors:

  • Yvo Boers, Surface Radar, Thales Nederland B.V. Haaksbergerstraat 49, 7554 PA Hengelo, The Netherlands
  • Frank Ehlers, NATO Undersea Research Centre, Viale S. Bartolomeo 400, 19138 La Spezia, Italy
  • Wolfgang Koch, FGAN-FKIE,Neuenahrer Strasse 20, D53343 Wachtberg, Germany
  • Tod Luginbuhl, Naval Undersea Warfare Center, 1176 Howell Street, Newport, RI 02841-1708, USA
  • Lawrence D. Stone, Metron Inc., 11911 Freedom Drive, Suite 800, Reston, VA 20190, USA
  • Roy L. Streit, Metron Inc., 11911 Freedom Drive, Suite 800, Reston, VA 20190, USA