A Nonlinear Frequency Modulation Agile Waveform Optimization Algorithm for Pulse Doppler Radar

Traditional pulse Doppler radar faces limitations such as range ambiguity and clutter folding when detecting long-range slow-moving small targets in strong clutter backgrounds. The agile waveform technique aims to suppress these issues by varying the parameters of the transmitted waveform between pu...

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Bibliographic Details
Published in:2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) pp. 1 - 6
Main Authors: Deng, Qing-Song, Gao, Yu-Hang, Ren, Li-Xiang, Liu, Quan-Hua, Fan, Hua-Yu
Format: Conference Proceeding
Language:English
Published: IEEE 22.11.2024
Subjects:
Clutter
Doppler effect
Doppler radar
Frequency modulation
genetic crossover
Impedance matching
Information filters
nonlinear frequency modulation waveform(NLFM)
Optimization
Particle swarm optimization
Pulse Doppler radar
range sidelobe modulation(RSM)
Signal to noise ratio
Simulation
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Summary:Traditional pulse Doppler radar faces limitations such as range ambiguity and clutter folding when detecting long-range slow-moving small targets in strong clutter backgrounds. The agile waveform technique aims to suppress these issues by varying the parameters of the transmitted waveform between pulses within the coherent processing interval (CPI). However, due to the differing range sidelobes produced by the matched filtering output of the agile waveform, range sidelobe modulation (RSM) effect occurs after Doppler processing, resulting in higher sidelobes in the Doppler domain. To address these challenges, a nonlinear frequency modulation (NLFM) agile waveform optimization algorithm for Pulse Doppler radar is proposed in this paper. First, the spectrum of NLFM waveforms is initially mathematically formulated by using a cosine series representation. Then, considering the RSM effect and waveform orthogonality, with the optimization objectives being the similarity and orthogonality of the agile waveforms. Finally, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm is employed to solve the optimization objectives, resulting in a set of NLFM waveforms with improved performance. Simulation results demonstrate that the proposed algorithm can strike a good balance between the RSM suppression and waveform orthogonality. The designed waveforms, when filtered by mismatched filters, effectively reduces the signal-to-noise ratio loss(SNRL) relative to the initial waveforms.
DOI:10.1109/ICSIDP62679.2024.10867922