An adaptive multialternative sequential target track detection algorithm using the decision statistics of plots and discarding the failed hypotheses

Abstract

Introduction. It is shown that the synthesis of an adaptive target track detection algorithm having the opportunity along with the decision of the main task to perform SNR estimation is of great practical importance. Problem statement. In accordance with the problem statement of multialternative hypotheses testing is necessary to obtain a sequential decision rule at conditional probability of erroneous recognition hypotheses and correct recognition probabilities, not less than specified, that will allow for observation to take one of the hypotheses. Development of an multialternative sequential target track detection algorithm. On the basis of the simple additions sequential test an adaptive multialternative sequential target track detection algorithm using the decision statistics of plots and discarding the failed hypotheses is developed. Effectiveness Analysis of Algorithm. Analysis of the adaptive algorithm is carried out as an example of target trajectory detection due to surveillance radar which measures range and range rate using the statistical modeling. The decision statistics related to noise- and target-originate measurements are described by an exponential distribution and Swerling 1 distribution respectively. Conclusions. On the basis of the SNR estimates can be determined radar cross-section of the target that can recognize its class. Discarding the failed hypotheses can reduce the computational cost. Designed adaptive algorithm provides hypotheses recognition performance is not less than specified.