Radar Imaging with Distributed Arrays and Compressive Sensing
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Solution Overview
Problem
Conventional radar imaging using distributed arrays often results in low resolution and artifacts such as aliasing, ambiguity, or ghost images due to the small aperture size and non-uniform distribution of antenna arrays, making it difficult to distinguish targets effectively.
Innovation Solution
The method employs a single static transmitter and multiple spatially distributed static linear antenna arrays with compressive sensing to improve image resolution by imposing sparsity on complex coefficients of targets, using iterative compressive sensing procedures and sparsity-driven imaging techniques to combine signals from multiple arrays, thereby enhancing image quality without artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple distributed sensing platforms with small aperture arrays are used to achieve flexibility and low cost, then the effective aperture increases and operation cost decreases, but the image resolution deteriorates and artifacts such as aliasing and ambiguity appear
Solution Approach 1:
The radar system is divided into multiple independent sensing platforms, each equipped with a small aperture array. These distributed arrays independently receive echoes from targets, and their measurements are subsequently integrated through compressive sensing to reconstruct high-resolution images. This segmentation enables flexible platform placement while maintaining imaging capability.
Solution Approach 2:
Multiple distributed sensing platforms are merged into a unified imaging system through joint signal processing. The measurements from all distributed arrays are combined using compressive sensing algorithms, which coherently integrate the information to achieve an effective aperture equivalent to a large centralized array, thereby improving azimuth resolution despite individual small apertures.
2Length of stationary object
If multiple distributed sensing platforms are used to increase effective aperture, then azimuth resolution improves, but device complexity and signal processing sophistication increase
Solution Approach 1:
The patent replaces complex mechanical signal processing operations with compressive sensing algorithms. Instead of using traditional sophisticated signal processing methods that require precise calibration and complex computations, the system uses compressive sensing to directly reconstruct images from undersampled measurements, simplifying the overall system complexity while achieving high resolution.
3Ease of operation
If conventional matched filter processing is used for each sensor platform individually, then processing simplicity is maintained, but image quality deteriorates with artifacts such as aliasing, ambiguity, and ghost
Solution Approach 1:
The patent applies preliminary sparsity constraints and structured signal models before the final image reconstruction step. By assuming that target reflectivity vectors are sparse in certain domains and incorporating this prior knowledge into the compressive sensing framework, the system can reconstruct high-quality images without the artifacts that plague conventional individual-platform processing methods.
Data Source
AI summary
A method and system for generating a high resolution two-dimensional (2D) radar image, by first transmitting a radar pulse by a transmit antenna at an area of interest and receiving echoes, corresponding to reflection the radar pulse in the area of interest, at a set of receive arrays, wherein each array includes and a set of receive antennas that are static and randomly distributed at different locations at a same side of the area of interest with a random orientation within a predetermined angular range. The the echoes are sampled for each receive array to produce distributed data for each array. Then, a compressive sensing (CS) procedure is applied to the distributed data to generate the high resolution 2D radar image.