Ground Moving Target Indication (GMTI) for radars with a small number of phase centres with low processing overhead is desirable for large scale deployment of unmanned aircraft (UAVs) in ground surveillance applications. Since there are often limitations associated with communication and onboard processing on UAVs, identifying moving targets from radar data gathered by these platforms for non-GMTI purposes would be an attractive prospect. The work presented here uses real radar data to assess the performance of the Displaced Phase Centre Antenna technique (DPCA), Adaptive DPCA and Joint Domain Localised Space Time Adaptive Processing on such data. It examines the influence of moving from two to three antenna elements and the performance of different processing configurations associated with a larger antenna array. Additionally, extended ground surveillance modes adopted by UAVs often involve a circular flightpath. In this case the DPCA condition is not met and so the GMTI performance is affected. This effect is investigated for both adaptive and non-adaptive signal processing algorithms on real data. The presence of internal clutter motion (ICM) in a scene and its influence on GMTI capability is also observed, using a synthesised clutter model. In pursuit of improving target detection performance while maintaining a relatively simple radar configuration, the use of a knowledge based approach to GMTI is discussed and a system is proposed that can provide rapid access to radar and image data while remaining robust and without limit on storage capacity. Finally the use of historical GMTI data from a common scene is proposed to increase the likelihood of identifying moving targets when using only basic GMTI processing. Experimental results are presented using real radar data, and optimal signal processing approaches are suggested for a variety of radar environments and hardware configurations.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625390 |
| Date | January 2010 |
| Creators | Banahan, C. P. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/756558/ |
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