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Performance analyses of the power-domain kernel adaptive locally optimum processor (ALOP) against interference waveforms with continuous power density

The power kernel adaptive locally optimal processor (ALOP) is an implementation of non-linear locally optimum detection in the presence of non-Gaussian noise using a kernel estimation technique to model the noise power density function. Methods of analyzing the expected performance of this algorithm are developed for a class of noise processes with continuous power density functions. These methods are used to illustrate the performance and the parameter tradeoffs that can be made with the power kernel ALOP.