CA-CFARCell Averaging Constant False Alarm Rate
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Estimation of the Optimal CA-CFAR threshold multiplier in Pareto clutter with known parameters.
CA-CFAR Adjustment factor correction with a priori knowledge of the clutter distribution shape parameter.
Taking into account the previously presented ideas, the authors aimed to obtain estimates of the optimal threshold multiplier factor (T) for each possible value of the Pareto shape parameter, for a CA-CFAR detector with 64 cells in the reference window.
The CA-CFAR scheme was used as the base of the design because it is the more widely used alternative.
The next section, under the name of "Materials and Methods" makes a brief presentation of the Pareto distribution and the CA-CFAR detector, describing also the performed experiments.
The CA-CFAR scheme is preferred for the analysis over the SO-CFAR, GO-CFAR and OS-CFAR alternatives because it's the internationally recognized reference model for comparing new implementations.
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.
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.