In the paper the radar detection of targets in sea clutter modelled by the compound K-distribution is examined from a statistical detection viewpoint. Optimum performance is determined for both fixed threshold and ideal constant false alarm rate (CFAR) receivers for targets modelled by Swerling cases 0, 1 and 2. In particular, the performance of dual threshold binary integrators is assessed. The compound K-distribution sea clutter model is not only a good fit to observed amplitude statistics over a wide range of conditions, but also allows the temporal and spatial correlation characteristics to be accurately modelled. The modelling of pulse-to-pulse correlation characteristics is very important for accurate performance prediction when pulse-to-pulse integration is used. The use of the compound K-distribution model not only gives more accurate results than models based on amplitude distribution alone, but also gives a new insight to the detection characteristics of different radar detection radar detection systems over a wide range of sea conditions.

Radar detection prediction in sea clutter using the compound K-distribution model

In practical situations the false alarm probability in double threshold radar detection, sometimes known as binary integration with sliding window detection, is dependent on the nonstationarity and azimuthal correlation of the clutter which is present. Control of the false alarm probability can be achieved, to a certain extent, by the adjustment of the second threshold in the detection process. In this study two adaptive control techniques which are based on the statistical characteristics of the data are compared. Comparing the results for a technique based on first-order statistics with one based on second-order statistics, it is shown that the second-order, or correlation sensitive, technique can give a reduction of 30 to 45 percent in the false alarm probability with no corresponding loss in the detection probability. An interesting aspect of the results is the fact that the effects of the size of the sample area and the bias in the correlation estimator are clearly evident.

Adaptive False Alarmrm Regulation in Double Threshold Radar Detection

The modified moving window detector for binary integration has a shorter window length compared with the ordinary moving window detector because of the addition of a third threshold. When the second threshold is reached, a counter begins to count until the third threshold is reached. A graphical method using a probability chain is developed here to analyze and to calculate the detection performance of this type detector. The detection performances for some practical cases are calculated by computer and are compared with the ordinary moving window detector.

The Detection Performance of a Modified Moving Window Detector

Moving-window false report probability is shown to overestimate effects of noise. "False-report-start" probability is calculated and compared to the usual false report probability. The ratio of the two exceeds a simple function of window length, threshold, and noise probability, which is tabulated for some representative values.

A. H. Miller

Papers

Noise, Radar Moving-Window Detectors, and False Returns