Distributed emitter parameter refinement based on maximum likelihood

Purpose-Emitter parameter estimation via signal sorting is crucial for communication,electronic reconnaissance and radar intelligence analysis.However,due to problems of transmitter circuit,environmental noises and certain unknown interference sources,the estimated emitter parameter measurements are still inaccurate and biased.As a result,it is indispensable to further refine the parameter values.Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers,the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data.The paper aims to discuss these issues.Design/methodology/approach-In this work,the author brings forward a distributed emitter parameter refinement method based on maximum likelihood.The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode.Findings-Experimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods.Originality/value-With the refined parameter values,the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together.Actually,the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification.The superior performance ensures its wide application in both civil and military fields.