Machine Learning Enhanced Radar Antenna Array for Uniform λ/2 Spacing
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Solution Overview
Problem
Traditional MIMO radar systems face challenges in achieving uniform phase and improved angular resolution due to non-λ/2-based spacing between physical antennas, leading to gaps and non-uniform phase in virtual antenna arrays, which degrade angular calculation performance.
Innovation Solution
A radar system employs a neural network trained via machine learning to generate data for supplemental virtual antennas, filling gaps in the virtual antenna array and ensuring consistent λ/2 spacing, thereby maintaining uniform phase and enhancing angular resolution without increasing system cost or complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of physical antennas is increased to improve angular resolution, then angular resolution is improved, but system cost, weight, and complexity increase
Solution Approach 1:
The patent creates virtual copies of physical antennas through digital signal processing. By forming virtual antenna array elements from signals received at physical antennas, the system achieves the angular resolution of a larger antenna array without physically installing more antennas, thus avoiding increased system complexity, weight, and cost while improving angular resolution.
Solution Approach 2:
The patent transitions from a physical spatial arrangement to a virtual digital domain arrangement. Instead of adding physical antennas in space, the system creates additional virtual antenna elements through signal processing in the digital domain, effectively increasing the antenna array size without physical expansion.
2Device complexity
If physical antennas are spaced greater than λ/2 apart to reduce system complexity, then device complexity is reduced, but uniform phase is lost in virtual antenna arrays
Solution Approach 1:
The patent employs feedback mechanisms through calibration processes where the system measures actual phase differences between virtual antenna elements and adjusts signal processing parameters to compensate for deviations. This feedback loop ensures uniform phase distribution across the virtual antenna array even when physical antennas are spaced greater than λ/2 apart.
Solution Approach 2:
The patent dynamically adjusts signal processing parameters such as phase shifting and time delay compensation based on the actual physical spacing of antennas. By changing these parameters adaptively, the system maintains uniform phase characteristics in the virtual antenna array regardless of the physical antenna spacing configuration.
3Device complexity
If physical antennas are spaced greater than λ/2 apart to reduce system cost, then system cost is reduced, but gaps appear in virtual antenna arrays
Solution Approach 1:
The patent generates virtual copies of antenna elements through digital signal processing. By creating multiple virtual antenna elements from a smaller number of physical antennas, the system effectively increases the quantity of antenna elements in the array without adding physical components, thus reducing system cost while maintaining adequate virtual antenna density.
Solution Approach 2:
The patent makes the physical antennas serve multiple functions by using them to generate multiple virtual antenna elements through different signal processing paths. Each physical antenna contributes to forming multiple virtual elements, maximizing the utility of each physical component and reducing the total number of physical antennas needed.
Data Source
AI summary
A radar system comprises a physical antenna array comprising a plurality of physical transmit antennas that each transmit a respective transmit signal at a wavelength λ and a plurality of physical receive antennas, each arranged to receive each of the respective transmit signals. The physical antenna array further comprises processor coupled to the physical antenna array and arranged to generate data corresponding to a virtual antenna array having a defined distance between each virtual antenna that is inconsistent and to generate data, via a neural network, corresponding to one or more supplemental virtual antennas that when used in conjunction with the data corresponding to the virtual antenna array represents data for a supplemented antenna array having a defined distance between each antenna of λ/2.