In this first part of the latest latency-information theory (LIT) and applications paper series powerful and fast `knowledge-unaided' power-centroid (F-KUPC) radar is revealed. More specifically, it is found that for real-world airborne moving target indicator radar subjected to severely taxing environmental conditions F-KUPC radar approximates the signal to interference plus noise ratio (SINR) radar performance derived with more complex knowledge-aided power-centroid (KAPC) radar. KAPC radar was discovered earlier as part of DARPA's 2001-2005 knowledge-aided sensor signal processing expert reasoning (KASSPER) Program and outperforms standard prior-knowledge radar schemes by several orders of magnitude in both the compression of sourced intelligence-space of prior-knowledge, in the form of SAR imagery, and the compression of processing intelligence-time of the associated clutter covariance processor, while also yielding an average SINR radar performance that is approximately 1dB away from the optimum. In this paper, it is shown that the average SINR performance of significantly simpler F-KUPC radar emulates that of KAPC radar and, like KAPC radar, outperforms a conventional knowledge-unaided sample covariance matrix inverse radar algorithm by several dBs. The matlab simulation programs that were used to derive these results will become available in the author's Web site.
展开▼
