Sparse MIMO Radar Signal Processing via Virtual Array Synthesis
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Solution Overview
Problem
Conventional radar systems suffer from poor spatial-resolution and Doppler-resolution due to the large wavelength of radio waves, making it difficult to detect small objects or distinguish close objects, and existing solutions are either costly or impractical for space-constrained applications.
Innovation Solution
A sparse MIMO radar array system that processes radar signals using a missing-data iterative adaptive approach (MIAA) and multi-dimensional folding (MDF) to generate a virtual array, estimating information from missing antennas and improving resolution with fewer transmitters and receivers, resulting in a smaller footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a basic radar system is used, then the system is simple and low cost, but the spatial-resolution and Doppler-resolution are poor
Solution Approach 1:
The patent applies MIMO (Multiple-Input Multiple-Output) technology to create a virtual array that extends the effective aperture in spatial dimensions. By using multiple transmit and receive antennas, the system synthesizes a larger virtual array aperture without proportionally increasing physical hardware complexity, thereby improving spatial-resolution through dimensional expansion of the signal processing space.
Solution Approach 2:
The patent introduces signal processing algorithms as intermediaries between the physical antenna array and the final detection output. Advanced processing techniques bridge the gap between limited physical aperture and desired resolution performance, enabling high-resolution detection without requiring a proportionally large number of physical antennas.
2Measurement precision
If multiple antennas are used to improve resolution, then the resolution increases, but the system cost and complexity increase
Solution Approach 1:
The patent creates virtual antenna elements through signal processing that copy and combine signals from physical antennas. The virtual array synthesizes additional antenna positions mathematically, providing the resolution benefits of a large antenna array without the proportional increase in physical hardware. This copying approach allows the system to achieve high resolution with fewer actual antennas.
3Measurement precision
If a full MIMO array is used to achieve necessary resolution, then the resolution is sufficient, but the system becomes cost prohibitive
Solution Approach 1:
The patent employs sparse MIMO array configurations where antennas are selectively placed at specific positions rather than forming a complete dense grid. This local optimization approach concentrates resources at critical array positions to maintain resolution performance while reducing the total number of antennas required, thereby lowering system cost.
Solution Approach 2:
The patent uses partial MIMO array configurations that provide sufficient resolution for the application without implementing a complete full-density array. By applying partial action—using only the necessary number of antennas for the required performance level—the system achieves cost-effective resolution without the excessive cost of a complete MIMO implementation.
Data Source
AI summary
The present disclosure generally pertains to systems and methods for processing radar signals from a sparse MIMO array. In some embodiments, the signals from a MIMO radar array are processed to generate a sparse virtual array. Then, by using a two-dimensional (2D) variant of missing-data iterative adaptive approach (missing-data IAA or MIAA) to process the virtual array, the system can estimate information from the missing antennas of the sparse virtual array. Then, by using the now full virtually array, the system can process the virtual array using a variant of multi-dimensional folding (MDF) to discover the existence and location (e.g., distance, elevation, and azimuth) of objects (also called scatterers) within the MIMO radar array's field of view.