The article examines theoretical and practical approaches to the development of airborne radar systems (ARS), analyzes historical background, basic radar methods, and modern requirements for hardware and software implementation. The research aim is to determine key requirements for ARS location and operation, investigate radar data flow processing methods, and substantiate the concept of an adaptive multi-frequency coherent ARS as a means of improving detection accuracy and jamming resistance. The methods described include: pulse and Doppler radar, phased array antennas, beamforming and jamming suppression algorithms, coherent integration, and adaptive spatial filtering; a multi-frequency sounding architecture with coherent processing and compressed sensing elements is proposed. Mathematical models of key characteristics are presented (bistatic range equation, SNR after integration, covariance matrices for adaptive filtering, Doppler coherence criteria), hardware resource assessments, and software recommendations (VHDL, C++, CUDA, Python). The following quantitative results were obtained: the concept of an adaptive multi-frequency coherent ARS was substantiated, providing a 35-40% improvement in target detection accuracy and a 2.5-3.0 times enhancement in jamming resistance compared to the existing systems; a mathematical model of the system was developed, allowing station performance prediction under various operating conditions with up to 92% accuracy; a system architecture integrating multi-frequency sounding and compressed sensing elements was proposed, ensuring a 45% reduction in power consumption while maintaining and improving functional characteristics; technical requirements for hardware and software implementation were defined, including the use of modern computing platforms (VHDL, C++, CUDA, Python) and artificial intelligence technologies, enabling data processing speeds of up to 2800 Gbit/s. The main conclusion: integration of multi-frequency capabilities, adaptive algorithms, and modern computing platforms together with AI ensures a significant increase in ARS efficiency and autonomy, providing prospects for reduced power consumption, miniaturization, and enhanced system stealth.
Prikhodskiy et al. (Thu,) studied this question.