Robust target detection using order statistic filters

A theoretical analysis of order statistic processors for improved target detection is presented. The sort function is used to provide insight into the optimal rank for detection of various targets and clutter environments where observations are independent and identically distributed. When observations do not contain identical statistical information the analysis becomes more complex. Both simulation and experimental results are used to illustrate the extent of robustness that can be obtained from a particular rank in the presence of observations containing significant target information (i.e. null observations)