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Moreover, the CA-CFAR, which is based on sliding-windowing, is implemented using the shifter registers and a moving averaging scheme for the full pipelined process, without a wait step between each calculation.
It can be seen from the above figures that when [P.sub.FA] = 3.17 * [10.sub.-5], contextual knowledge-based algorithm detects all the 30 targets, only 10 false alarms, while the CA-CFAR detects only 24 targets and 16 false alarms; when [P.sub.FA] = 2.87 * [10.sub.-7], contextual knowledge-based algorithm and CA-CFAR detects 25 and 24 targets, respectively, but the false alarms of CA-CFAR are 12 more than the contextual knowledge-based algorithm.
In Figures 16, (a) is original image added targets; (b)-(c) are results of MRF-based segmentation algorithm; white areas in (b) are woods and shadow, while in (c) are grass, and (d) is the edge image; (e) and (f) represent the detection results of the proposed algorithm and CA-CFAR, respectively, when [P.sub.FA] = 2.87 * [10.sub.-7].
Denidni, "CA-CFAR detection performance of radar targets embedded in non-centered chi-2 gamma clutter," Progress In Electromagnetics Research, Vol.
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