Optimization algorithm for coherent parameter estimation of ground-based distributed coherent aperture radar

Ground-based Distributed Coherent Aperture Radar (GB-DCAR) offers significant advantages over traditional single-site radar systems, including extended detection range, wider monitored airspace, and broader search capabilities. However, with the development of modern electronic warfare, The electromagnetic environment for modern battlefield radar applications is becoming increasingly harsh. The parameter estimation accuracy of traditional algorithms cannot meet the requirements under low signal-to-noise ratio (SNR) conditions. This paper proposed an optimization algorithm for coherence parameters estimation under low SNR conditions. A signal model for GB-DCAR was established, that Orthogonal frequency division multiplexing linear frequency modulated(OFDM-LFM)signal was used as emission waveforms. And the impact of delay errors and phase errors on coherent synthesis was analyzed. Then an optimization algorithm based on the 1-dimensional Radial Basis Function Kernel(1D-RBF) variable window positioning method and cross-correlation method was proposed, which can improve the coherence parameters estimation performance under low SNR conditions. This algorithm firstly uses 1D-RBF to preprocess the input signal; then complete the target signal position and extraction by Variable multi-scale window method; and finally utilizes the Cross-correlation method to estimate the coherence parameters. Simulation results demonstrate that the proposed algorithm is effective and outperforms the traditional Cross-correlation method when the input signal SNR is in the range of −25𝑑𝐵 to −33𝑑𝐵.