The techniques that have been described and analysed in this chapter can be exploited in various ways to develop practical CFAR detection systems. If a CA CFAR system is to be used to estimate the local mean level, choices must be made among the many possible configurations, such as the length (M) of the cell-averagers, the gap (G) surrounding the cell under test and the various schemes described in Section 13.2.3. It may be appropriate to have these parameters or choice of scheme selectable by the radar operator or automatically adapting, according to the prevailing conditions. The method for setting the threshold multiplier must also be determined. The CA CFAR suggests that the surrounding clutter may be used directly to estimate the distribution shape and hence the threshold multiplier, a. The various methods for estimating the clutter shape parameter in Section 13.3 could be assessed for this purpose. As discussed in Section 13.1, the clutter conditions may vary very widely with range and azimuth, and a problem is likely to be encountered in achieving a sufficient number of independent clutter samples within an area of constant statistics. One approach may be to gather statistics over a number of scans. Other estimates of clutter statistics, such as the U estimator described in Section 13.3, may also be used. A closed-loop system (Section 13.4.2) that directly estimates the PFA may also be a practical solution, although there are similar considerations on the number of samples required to give a satisfactory estimate, especially if a low value of PFA is required.

Chapter Contents:

• 13.1 Introduction
• 13.2 Adaptation to changing clutter amplitude
• 13.2.1 Control of received signal dynamic range
• 13.2.2 Log FTC receiver for Rayleigh clutter
• 13.2.3 Cell-averaging CFAR detector
• 13.2.3.1 CFAR variants
• 13.2.3.2 CFAR loss in noise
• 13.2.3.3 GO CFAR in noise
• 13.2.3.4 OS CFAR in noise
• 13.2.3.5 CFAR loss in K distributed clutter
• 13.2.3.6 CFAR loss in K distributed clutter plus noise
• 13.2.3.7 Ideal CFAR detection and CFAR gain in K distributed clutter
• 13.2.3.8 CFAR gain with a cell-averaging CFAR
• 13.2.4 Linear prediction techniques
• 13.2.5 Non-linear predictors
• 13.3 Adaptation to changing clutter PDF
• 13.3.1 Fitting to a family of distributions
• 13.3.2 Distribution-free detection
• 13.3.3 Estimation of the K distribution shape parameter
• 13.3.3.1 Matching moments
• 13.3.3.2 Matching to the tail of the distribution
• 13.3.4 Estimation of a Weibull shape parameter
• 13.4 Other CFAR detection techniques
• 13.4.1 Site-specific CFAR
• 13.4.2 Closed-loop systems
• 13.4.3 Exploitation of transient coherence
• 13.4.4 Scan-to-scan integration
• 13.5 Practical CFAR detectors
• References

Inspec keywords: statistical analysis; radar detection; closed loop systems; parameter estimation; radar clutter

Other keywords: radar operator; clutter shape parameter estimation; closed-loop system; cell-averaging CFAR system; PFA; local mean level estimation; surrounding clutter; constant false alarm rate detection; CA CFAR; CFAR detection system; constant statistics

Subjects: Other topics in statistics; Radar equipment, systems and applications