Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming

1 Introduction Cell-Averaging Constant False Alarm Rate (CA CFAR) signal processing proposed by Finn and Johnson in [1] is often used for radar signal detection. The detection threshold is determined as a product of the noise level estimate in the reference window and a scale factor to achieve the design probability of false alarm. The presence of strong pulse jamming (PJ) in both, the test resolution cell and the reference cells, can cause drastic degradation in the performance of a CA CFAR processor as shown in [11]. For keeping of constant false alarm rate in PJ, the CA CFAR processor presented in [8,11] is used. For the minimization of CFAR losses in case of pulse jamming, PI or BI is implemented in CFAR processors as shown in [4,7,9]. The use of excision CFAR detectors, supplemented by a postdetection integrator or a binary integrator as shown in [5,6,9], increases the CFAR losses. Minimum CFAR losses in PJ are obtained in [4,10] with a CFAR adaptive postdetection integration (API) processor with adaptive selection on PJ in reference windows and appriory selection in test windows as shown in [4], and adaptive censoring in reference and test windows as presented in [10]. In such situations, it would be desirable to know the CFAR losses, dependent on the parameters of PJ, for rating of radar behavior. We use the criterion offered by Rolling and Kassam in [2,3], based on the average decision threshold (ADT). The ADT and the detection probability are closely related to each other. The difference between the two CFAR systems is expressed by the ratio between the two ADTs measured in dB, as shown in [2,3]. We assume in this paper that the noise in the test cell is Raleigh envelope distributed and target returns are fluctuating according to Swerling II model, as it is in [3,4]. As a difference from the authors in [4], we assume that the samples of PJ are distributed according to the compound exponential law, where weighting coefficients are the probabilities of corrupting and non-corrupting of the samples. We have used weighting coefficients in the interval between 0 and 1. For values of the weighting coefficients higher than 0.3, the Poisson process model is used, but it is rough [12]. The results in this case are correct as far as processor behaviour is concerned, but the numerical values are not accurate. The binomial distribution is correct in …