Coherent Synthesis Method for Flexible DCAR Under Low-Power and High-Precision Requirements

To enhance the coherent synthesis efficiency of Distributed Coherent Aperture Radar (DCAR) under low-power calibration conditions, an adaptive subarray position calibration method based on Manifold Distance Estimation (MDE) and Symplectic Geometry Mode Decomposition (SGMD) is proposed. The method first employs manifold distance measures to approximate the local neighbourhoods of low-power calibration signals, thereby preserving the intrinsic structural information of the underlying signal manifold and deriving a trajectory matrix suitable for low-power calibration scenarios. The signal trajectory matrix is then embedded into a symplectic phase space, where Hamiltonian eigenvalues are obtained via symplectic similarity transformations. This process enables the accurate extraction of subarray spatial information from highly noise-contaminated calibration signals, effectively mitigating noise-induced distortion and improving the accuracy of DCAR coherent parameter estimation. Simulation and experimental results under low-power conditions indicate that when the received calibration signal has a Signal-to-Noise Ratio (SNR) of 12 dB, the proposed method can achieve at least 80% coherent synthesis efficiency in wide-area DCAR. For small-scale DCAR configurations, even with a reduced calibration signal SNR of 18 dB, the method still achieves an SNR gain close to the ideal performance level.