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Knowledge-Aided Integrated Radar and Jamming Waveform Design via Iterative Fractional Programming Algorithm
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AI
DOI：10.1109/TSP.2025.3645929.png)
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Abstract
En 中文
This paper deals with the dual function waveform design problem for a knowledge-aided integrated radar and jamming (IRAJ) system. Supposing the IRAJ system has access to an information database obtained from a reconnaissance system for the threatening radar's transmit waveform knowledge, the signal-to-jamming and noise ratio (SJNR) at the output of the threatening radar filter is considered as a figure of merit to reduce its detection probability, which represents the performance of blanket jamming. Besides, along with energy and peak-to-average ratio (PAR) constraints to comply with the hardware realization, let us minimize the weighted peak sidelobe level (WPSL) or maximize the signal-to-interference and noise ratio (SINR) of the IRAJ system for the detection performance according to the delay structure of reflected echoes. To tackle the resulting non-convex multi-objective optimization problems, iterative fractional programming algorithms (IFPA) leveraging cyclic algorithm-new (CAN) and alternating direction method of multipliers (ADMM) are proposed, respectively. Finally, simulation results are provided to demonstrate the competition between radar and jamming functions within the proposed Pareto optimization framework and validate the effectiveness of the conceived algorithms.
Keywords:
Radar
Jamming
Signal to noise ratio
Radar detection
Interference
Vectors
Fast Fourier transforms
Phase shift keying
Peak to average power ratio
Iterative algorithms
Integrated radar and jamming (IRAJ) system
signal-to-jamming-and-noise ratio (SJNR)
weighted peak sidelobe level (WPSL)
signal-to-interference and noise ratio (SINR)
iterative fractional programming algorithm (IFPA)
Journal
I
IF:
5.8
Papers:
276
Citations:
0
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