CA-CFARCell Averaging Constant False Alarm Rate
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CA-CFAR Adjustment factor correction with a priori knowledge of the clutter distribution shape parameter.
Instead, the author searches to find the combination of values of the CA-CFAR K and the [beta] parameter for which the Pfs are equal to and.
Besides, samples were processed by a 64 cells CA-CFAR architecture with no guard cells.
Where false positives are those clutter samples mistakenly identified by the CA-CFAR detector as targets.
The X axis provides the CA-CFAR K value and the Y axis the calculated Pf.
Table 2 relates each occurrence of the [beta] Weibull parameter with the better CA-CFAR K.
The founded relation between the K and the Weibull shape parameter achieved an important step toward the creation of an improved CA-CFAR detector, which will vary its adjustment factor according to the conditions of the environment.
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.
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.
To analyze the influences of different contextual knowledge, we compare CA-CFAR, contextual knowledge-based algorithm considering one type of context knowledge and the proposed algorithm under different false alarm rates Pfa.
As to detection rate, performance of algorithm only considering distance to boundary is closest to the proposed algorithm, followed by algorithm only considering terrain, and both of these algorithms are better than CA-CFAR, which indicates that the factor of distance to boundary plays an important role in improving detection rate.
CA-CFAR and algorithm only considering target aggregation do not work well.