Machine Learning Radar Antenna Optimization
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Solution Overview
Problem
MIMO radar systems require multiple receive channels for high angular resolution, leading to high costs and limited commercial viability, especially in applications like automotive industry, where reducing the number of antennas is necessary without compromising image quality.
Innovation Solution
A method for training a machine learning procedure to simultaneously learn optimal antenna locations and reconstruction parameters, allowing for the construction of a radar system with fewer antennas while maintaining high image quality through a combination of learned antenna locations and neural network-based reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple receive channels are used in MIMO radar systems to achieve high angular resolution, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the traditional mechanical approach of using multiple physical antennas with a machine learning-based signal processing system. The neural network learns optimal antenna locations and reconstruction parameters from training data, enabling high-resolution imaging with fewer physical antennas by computationally synthesizing the missing spatial information.
Solution Approach 2:
The patent transforms the fixed physical antenna configuration into learnable parameters. The machine learning model optimizes antenna locations and reconstruction parameters dynamically based on the imaging task requirements, allowing the system to adapt the effective antenna configuration without physically changing the hardware setup.
2Device complexity
If the number of antennas is reduced to lower costs, then device complexity is decreased, but measurement precision deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the limited physical antennas and the desired high-resolution output. The neural network acts as a computational mediator that processes the limited antenna signals and synthesizes high-resolution images, bridging the gap between reduced hardware and maintained performance.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using simulated or measured data from full-aperture antenna configurations. This pre-training phase allows the model to learn optimal reconstruction strategies before being deployed with reduced antennas, ensuring high measurement precision is achieved even with fewer physical sensors.
Data Source
AI summary
A method of designing a radar, comprises receiving data pertaining to a set of reflected signals received from a distribution of objects by a respective set of receiving antennas at a respective set of locations, and feeding the data and the locations as training data to a machine learning procedure. The machine learning procedure calculates, simultaneously, a set of learned antenna locations and a set of learned parameters associating the signals with the objects, thereby providing a trained machine learning procedure parametrized by the set of learned parameters.