A Low-Complexity Near-Field Imaging Method for Multistatic Radar Systems Based on Receiver-Domain Decomposition

Area of Science:

• Electrical Engineering
• Signal Processing
• Electromagnetics

Background:

• Near-field multistatic radar imaging presents significant computational challenges due to nonlinear matched-filter operators over large 3D search regions.
• Sparse, large-aperture receiver layouts exacerbate these computational burdens, limiting practical system applications.

Purpose of the Study:

• To develop a computational burden mitigation strategy for static-target near-field multistatic radar imaging.
• To investigate an approximation method based on receiver-domain decomposition for efficient image formation.

Main Methods:

• Formulated a maximum-likelihood model for static targets with a known signal envelope.
• Reduced the estimation problem to a coherent spatial matched filter using time-compressed data.
• Developed a receiver-domain decomposition approach, partitioning the receiver set into subapertures for low-resolution subimage computation, phase correction, and interpolation.

Main Results:

• The proposed approximation enables efficient image formation by coherently aggregating interpolated subimages.
• Phase correction was shown to significantly smooth low-resolution block images, facilitating accurate interpolation.
• Derived interpolation-based error bounds and analyzed computational complexity, demonstrating the approximation's effectiveness under specific conditions.

Conclusions:

• Receiver-domain decomposition offers a viable method to mitigate the computational burden in near-field multistatic radar imaging.
• The accuracy of the approximation depends on factors like phase compensation, coarse-grid resolution, and target characteristics.
• This approach provides a computationally efficient alternative to brute-force image formation for specific radar imaging scenarios.