Sparse MIMO Radar Array Layout for Higher Angle Resolution
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Solution Overview
Problem
Conventional methods for designing sparse arrays for automotive radars, such as minimum hole arrays and minimum redundancy arrays, are limited and do not support the design of multiple-input multiple-output (MIMO) arrays, which are becoming prevalent in automotive applications, and lack efficiency in increasing array aperture without increasing the number of antenna elements.
Innovation Solution
A particle swarm optimization method is used to design sparse arrays for automotive radars, allowing for the iterative search of antenna element placements that improve a cost function associated with the Fast Fourier Transform (FFT) response, enabling the design of MIMO arrays as well as conventional sparse arrays with arbitrary apertures and numbers of antenna elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the array aperture is increased to achieve better angle resolution, then the beamwidth decreases and angle resolution improves, but the number of antenna elements must increase which increases power, complexity, and cost
Solution Approach 1:
The patent applies non-uniform spacing of antenna elements within the array, where different regions of the array have different element densities. This local variation in element distribution allows the array to achieve the desired aperture size and angle resolution while using fewer total elements compared to uniform spacing, thereby reducing complexity, power consumption, and cost.
2Length of stationary object
If conventional uniform arrays are used, then the array design is simple, but increasing array aperture requires increasing the number of antenna elements which increases power and complexity
Solution Approach 1:
By implementing non-uniform element spacing where elements are denser in certain regions and sparser in others, the array achieves the required aperture length without proportionally increasing the total number of elements. This localized optimization reduces the overall element count, thereby reducing power consumption while maintaining the desired aperture size.
3Adaptability or versatility
If existing sparse array design methods are used, then the array aperture can be increased without increasing the number of elements, but these methods do not support MIMO array design
Solution Approach 1:
The patent develops a sparse array design method based on particle swarm optimization that is universally applicable to both conventional single-input single-output arrays and multiple-input multiple-output (MIMO) arrays. The optimization framework can handle arbitrary aperture sizes and element counts while supporting the complex spatial requirements of MIMO configurations, thereby eliminating the design limitations of existing methods.
4Measurement precision
If the number of antenna elements is increased to improve angle resolution, then the beamwidth decreases, but the cost of the array increases
Solution Approach 1:
The non-uniform spacing strategy concentrates antenna elements in regions that provide the most beneficial contribution to angle resolution while reducing element density in less critical regions. This localized optimization achieves the desired measurement precision without proportionally increasing the total element count, thereby controlling array cost while maintaining high angle resolution performance.
Data Source
AI summary
A method is disclosed for designing a sparse array for an automotive radar. The method moves each of a number of antenna elements to candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function. The cost function for each candidate placement may be determined from characteristics of the FFT response associated with the candidate placement. The method may search for a candidate placement with the lowest cost function among the multiple candidate placements based on the random seed placement. The search may be repeated for a large number of random seed placements to find the candidate placement with the lowest cost function corresponding to each random seed placement. The method may compare the lowest cost functions corresponding to the multiple random seed placements to determine an optimized placement having the minimum cost function.