THE COHERENT DATA PROCESSING IN RADAR SIGNAL SPECTRAL ANALYSIS WITH SUPERRESOLUTION

 Daniel S. Grigoryan

Radar Data Processing Laboratory of Army Air Defense Academy, Smolensk 

 Received February 23, 2012

 

Abstract. It is established that at enough big power of signals, final values of relations a signal/noise in elements of correlation matrixes don't depend on a way of accumulation of a signal. Influence of size of a relative interval of the analysis on relative norm of a indignations vector of weight factors is investigated and its boundary value is defined. It is defined that at the same order of model relative norm of a indignations vector of weight factors at use of preliminary coherent data processing in sample essentially more low, than norm of a vector of indignations of weight factors at use initial autoregressive methods of the digital spectral analysis. It is theoretically defined and proved experimentally that efficiency of the superresolution at identical relations a signal/noise depends on a relative interval of the analysis without dependence from the chosen method of data processing. Obvious advantage from the point of view of computing expenses of coherent methods of a linear prediction with прореживанием under the superpermission of harmonious signals in comparison with initial methods of the spectral analysis is shown.

Keywords:  superresolution, linear prediction, weight vector, conditionality, spectral function.