Design and application of KP test for target detection distribution law

Scientific error analysis for the test data is critical to improve the RD product evaluation quality of the test range. Using testing data not verified by distribution law for accuracy analysis will mislead the evaluation and final conclusion. With the help of new data processing technique and drawing software (such as Excel, Matlab, Origin and so on), a lot of test data were studied, it is concluded the partial sample data and the whole sample data conform with normal distribution after they are separated into some parts. Kolmogorov and Pearson x2 methods are introduced to function as a unified norm for verifying the distribution of large amount of data obtained from target detection in the test range. This norm can greatly increase the credibility of the test. If the confidence level is reasonably selected, the number of flights can be decreased to 10%～50%. The method has the potential to be used in almost all the system and device testing.