MIMO Radar Calibration Matrices Derived From Full Matrix Data
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Solution Overview
Problem
Existing MIMO radar systems require large calibration matrices that consume significant computing resources, memory, and power, and existing methods for determining independent transmit and receive matrices rely on chamber measurement data, which may not be available for previously manufactured systems in the field.
Innovation Solution
A method to derive independent transmit and receive calibration matrices directly from an existing full calibration matrix without chamber measurement data, using singular value decomposition and Kronecker product optimization, allowing for software updates to existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full NM×NM calibration matrix is used for MIMO radar systems, then calibration accuracy is maintained, but computing resources, memory, and power consumption increase significantly
Solution Approach 1:
The full NM×NM calibration matrix is segmented into N independent transmit calibration vectors and M independent receive calibration vectors. This segmentation reduces the calibration matrix from a complex NM×NM structure to simpler N×1 and M×1 vectors, significantly reducing computational complexity while maintaining calibration accuracy through the independent calibration of transmit and receive channels.
2Reliability
If a full NM×NM calibration matrix is stored in memory, then complete calibration data is available, but memory requirements and power consumption increase
Solution Approach 1:
The essential calibration information is extracted from the full NM×NM calibration matrix by identifying and storing only the N transmit calibration vectors and M receive calibration vectors. This extraction process removes redundant data while preserving the critical calibration parameters needed for accurate MIMO radar operation, significantly reducing memory storage requirements.
3Productivity
If chamber measurement data is used to determine independent transmit and receive matrices, then calibration efficiency is improved, but the method cannot be applied to previously manufactured systems in the field
Solution Approach 1:
Instead of determining calibration matrices from chamber measurement data (forward approach), the method inverts the process by extracting transmit and receive calibration matrices from an existing full calibration matrix that is already stored in the system. This inverted approach enables calibration efficiency improvements for previously manufactured systems without requiring access to original chamber measurement data or physical recalibration equipment.
Data Source
AI summary
Systems and method are provided and include receiving a full calibration matrix for a multiple-input multiple-output (MIMO) radar system having N physical transmit channels, M physical receive channels, and N×M virtual transmit-receive channels, the full calibration matrix being an NM×NM complex matrix previously generated based on chamber measurement data. Transmit and receive calibration matrices are determined based on the full calibration matrix, with the transmit calibration matrix being an N×N complex matrix and the receive calibration matrix being an M×M complex matrix. The MIMO radar system uses the calibration values of the transmit and receive calibration matrices to compensate antenna responses to adjust for physical characteristics of the MIMO radar system while performing at least one of ranging and detection of an object in an environment of the MIMO radar system.