Multi-Segment Auto Regression Algorithm of Processing of Complex-Modulated Signals in the High-Speed Target-s Detection Tasks

The efficiency of using of multi-segment auto regression algorithm of spectrum processing of complex-modulated signals for the speedy target-s detection is considered. Comparative analysis of classical Fourier transform-based and auto regression algorithms is performed. Analysis was performed for slow and high-speed targets, lying out of single-valued measurement zone. The impossibility of using of classical Fourier transform-based algorithm for high-speed target detection is illustrated. The immunity of multi-segment auto regression algorithm for centred-spectrum interferences and for interferences with smoothly colored spectrum is researched. The influence of self-noises of the receiver is analyzed. The possibility of reduction of the false-alarm probability in the adjacent receive channels is considered. Detection characteristics and noise immunity characteristics were calculated by means of using probing signal such as magnitude-phase-manipulated regular pulse pattern with internal anti-phased binary manipulation. Characteristics were calculated for constant and Rayleigh-distributed signal amplitude, all signals have random initial phase.