BAYES AMBIGUITY FUNCTIONS: SOME SIMPLE APPLICATIONS TO RESOLUTION AND RADAR COUNTERMEASURES
MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
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Bayes ambiguity functions are defined as an important parameter governing the performance of optimum i.e., Bayes or minimum average risk systems. Bayes ambiguity functions are generalizations of the classical ambiguity functions of Woodward and are specifically derived from an appropriate decision process. It is shown here that it is the real part of the ambiguity function that is significant, rather than its modulus. Optimum target resolution is formulated as a detection problem involving the two hypothesis states H sub o unresolved signals versus H sub 1 resolved signals, and general conditions for the qualitative utility of the ambiguity functions are discussed. These latter are additive gaussian noise and threshold operation otherwise the ambiguity function is an inadequate description of system performance. The analysis is extended to a number of situations involving interfering signals, such as electronic countermeasures ECM and is illustrated with simple examples showing quantitiatively, as well as qualitatively, the typical roles played by the Bayes ambiguity function in a variety of ECM applications. It is emphasized that one must also consider the probability of correct and incorrect decisions, in conjunction with the properties of the ambiguity functions, to achieve a reliable measure of expected performance.
- Radar Countermeasures
- Active and Passive Radar Detection and Equipment