Sparse Array Radar Angle-of-Arrival Estimation with Ambiguity Suppression
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Solution Overview
Problem
Radar systems face challenges in achieving high spatial resolution for distinguishing closely spaced and similarly moving objects, particularly in dynamic environments, due to ambiguities in virtual antenna arrays and the inefficiencies of MIMO implementations.
Innovation Solution
The use of a multi-input multi-output (MIMO) virtual array with embedded uniform sparse linear arrays, employing unique co-prime antenna-element spacings and iterative refinement techniques, to enhance angle-of-arrival estimation by mitigating ambiguities and suppressing spurious sidelobes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual antenna arrays are used to mitigate ambiguity issues, then spatial resolution is improved, but ambiguities and grating lobes persist yielding less-than-optimal resolution
Solution Approach 1:
The patent segments the uniform sparse linear array into multiple subarrays with different element spacings (e.g., first subarray with spacing d1, second subarray with spacing d2). Each subarray processes signals independently to generate separate output data, which are then combined. This segmentation eliminates ambiguities and grating lobes by ensuring that each subarray's spatial frequency spectrum has unique correlation peaks, thereby improving spatial resolution while maintaining reliability.
Solution Approach 2:
Different subarrays are assigned different local qualities in terms of antenna element spacing. The first subarray uses spacing d1 optimized for certain angular ranges, while the second subarray uses spacing d2 optimized for other angular ranges. This local optimization ensures that each subarray provides high-resolution detection in its specific operational domain, and the combination of all subarrays achieves comprehensive high-resolution coverage without ambiguities.
2Measurement precision
If MIMO antennas are used to achieve higher spatial resolution, then detection capability is improved, but implementation becomes challenging in rapidly-changing environments
Solution Approach 1:
The patent merges the advantages of uniform sparse linear arrays with MIMO technology by embedding the uniform sparse structure within the MIMO framework. The transmitting and receiving antennas are arranged in a uniform sparse linear array configuration, and the signals from multiple transmitting antennas are combined at the receiving end. This merging approach achieves high spatial resolution through the uniform sparse structure while simplifying implementation compared to traditional MIMO approaches, making it suitable for rapidly-changing environments.
3Measurement precision
If antenna elements are spaced closer to distinguish closely-spaced objects, then spatial resolution is improved, but quantization errors increase
Solution Approach 1:
The patent transitions from a one-dimensional uniform spacing approach to a multi-dimensional approach by using multiple subarrays with different spacing values (d1, d2, etc.). Instead of simply reducing the spacing of a single array, the invention adds the dimension of multiple spacing configurations. Each subarray's output data is processed separately and then combined, allowing the system to achieve fine angular resolution without the quantization errors that would result from simply decreasing element spacing in a single uniform array.
Data Source
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AI summary
In one example, a radar circuit uses computer processing circuitry for processing data corresponding to reflection signals via a sparse array. Output data indicative of signal magnitude associated with the reflection signals is generated, and then angle-of-arrival information is discerned therefrom by (e.g., iteratively): correlating the output data with at least one spatial frequency support vector indicative of a correlation peak for the output data; generating upper-side and lower-side support vectors which are neighbors along the spatial frequency spectrum for said at least one spatial frequency support vector, and providing, via a correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector.