Pulse-Sorting Algorithm Based on Correlation Peak Detection with Adjustable Parameters

The conventional pulse sequence detection algorithms do not consider the feature information of the pulse repetition interval (PRI) in different work modes, bring about some disadvantages of larger detection error in low signal-to-noise ratio circumstance and higher computational complexity with TACAN. To solve this problem, an algorithm based on correlation peaks' positions with adjustable parameters was proposed for pulse sequence detection and sorting. Firstly, determining the optimal objective in virtue of the signal cross-correlation detection model, a negative definite Hermitian Matrix was developed for detecting pulses. The pulse sequences, not conforming to PRIs, were filtered out by detecting the correlation peaks' positions in the matrix. Then the interference components caused by signal fading were eliminated using pulse blanking. And the problem of missing pulses in incomplete period was solved by using the pulse sort method of cyclic queue. Simulation results show that, the proposed algorithm can improve the detection accuracy of the pulse time of arrival (PTOA) and the detection accuracy of the benchmark pulse sequence while improving the signal-to-noise ratio.