2 Problem description and hypothesisĪfter the signals are pre-processed by the radar reconnaissance receiver, the mixed signals are sorted according to different parameters to attain the single radar emitter signal. Simulations and discussions are displayed in Section 5 to indicate the improvement of our work. In Sections 3 and 4, the FashICA algorithm is analysed and two MWD algorithms are proposed. Section 2 describes the problem, makes the hypothesis, and establishes the research model. The modified wavelet denoising (MWD) achieves improving the blind separating performance based on FastICA. In this work, we establish the model of radar emitter intropulse signal blind sorting, modify the wavelet denoising algorithm to improve the signal quality, and use the FastICA algorithm to realise the intropulse signal blind sorting. Therefore, lots of work have been done on the former two problems, and attained good performance, but they did not considerate the algorithm's anti-noise performance. Add pre-denoising to improve the algorithm's anti-noise performance.Improve the iterative algorithm to be independent of the initial value and has a faster convergence speed.Find the initial value to eliminate its influence on the algorithm's convergence.
Also, all these researches are centring on how to improve the blind separating effect in the complex electromagnetic environment: Huang first applied the technology of blind signal extraction to radar signal sorting and found feasible Li analysed the FastICA algorithm, used it in radar signal sorting, and achieved good separating performance Xiong raised a new improved algorithm combining the Newton method with negentorpy as an objective function to optimise the FastICA algorithm and eliminate the influence of the initial value. In the complex electromagnetic environment, radar signal blind separation is an effective method for radar emitter intropulse signal sorting. Radar signal sorting based on the intropulse parameters mainly selects distinctive features to represent each signal, such as multi-dimensional statistical features, time–frequency features, and entropy, by which the signal component is extracted in sequence. Also, signal sorting is finished according to the correlation of the same radar's parameters. At present, most of researches on the radar signal sorting are based on the interpulse parameters: carrier frequency, pulse width (PW), pulse repetition frequency, time of arrival (TOA), angle of arrival, pulse amplitude, etc.
Radar signal sorting mainly depends on signal parameters, including interpulse and intropulse parameters. Radar signal sorting aims to separate a single radar signal from the random mixed signal flows by the radar reconnaissance equipment in the high-density signal environment. Hence, it is vital to complete the radar signal sorting precisely and rapidly in the complicated environment. Radar signal sorting is the front part of radar emitter recognition, location, and tracking, whose result directly influences the latter reconnaissance counter-measure system's performance. In the radar reconnaissance and confrontation of modern electromagnetic warfare, the electromagnetic environment is more and more adverse and the signal becomes more and more complex. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.