Radar Signal Processor Power Estimation via Adjacent Azimuth Matrix
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Solution Overview
Problem
Conventional vehicle-mounted array radar systems face challenges in real-time power estimation due to the computational complexity of inverse matrix operations in the maximum likelihood method, which is necessary for accurate collision avoidance and inter-vehicle distance maintenance.
Innovation Solution
The introduction of an approximating expression that simplifies the power estimation process by using an adjacent azimuth array response matrix, limiting elements to reflected waves in adjacent azimuths, reduces the order of the inverse matrix and computing volume, allowing for faster processing without requiring high-function CPU capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the maximum likelihood method is used for power estimation to maintain measurement precision, then the accuracy of reflected wave power estimation is improved, but the computational complexity and processing time increase due to inverse matrix operations
Solution Approach 1:
The patent segments the full array response matrix into sub-matrices corresponding to adjacent azimuth regions. Instead of inverting the complete K×K matrix, the system inverts only the sub-matrix related to the specific azimuth region where the target is detected, significantly reducing the computational burden while maintaining estimation accuracy for that region.
Solution Approach 2:
The patent applies local quality by focusing the inverse matrix operation only on the local sub-matrix corresponding to the specific azimuth region where the reflected wave is detected. This localized approach maintains the precision of power estimation for the target direction while avoiding the computational complexity of inverting the entire array response matrix.
2Measurement precision
If the full array response matrix is used for power estimation to ensure measurement precision, then the accuracy is improved, but the device complexity and computational load increase
Solution Approach 1:
The patent divides the full array response matrix into multiple sub-matrices based on azimuth regions. The system then processes only the relevant sub-matrix corresponding to the detected target azimuth, reducing the computational complexity from inverting a K×K matrix to inverting a smaller sub-matrix while maintaining estimation accuracy.
Solution Approach 2:
The patent applies partial action by performing the inverse matrix operation only on the necessary sub-matrix rather than the complete array response matrix. This partial processing approach maintains sufficient accuracy for the target direction while significantly reducing computational complexity and processing requirements.
3Productivity
If real-time processing is implemented for collision avoidance to improve productivity, then the processing speed is improved, but the computational load and processing requirements increase
Solution Approach 1:
The patent segments the computational task by processing only the sub-matrix corresponding to the specific azimuth region where the target is detected. This segmentation enables real-time processing by reducing the computational load from inverting the full K×K matrix to inverting a smaller sub-matrix, making real-time collision avoidance feasible.
Solution Approach 2:
The patent applies partial action by performing computations only on the necessary sub-matrix rather than the complete array response matrix. This partial processing enables real-time processing capability while reducing the computational load, allowing the system to meet real-time requirements for collision avoidance without excessive processing power demands.
Data Source
AI summary
A radar signal processor has observation means for outputting a predetermined observation signal from a reflected wave, means for extracting an observation signal component concerning a target from the observation signal, means for computing a sample correlation matrix showing a correlation characteristic between the observation means from the observation signal components, means for estimating power of the reflected wave from the sample correlation matrix, and an array response matrix which is comprised of response vectors of the reflected waves. When estimating the power, an adjacent azimuth array response matrix having only the reflected waves of a predetermined arrival direction which power is to be estimated and the reflected waves of an azimuth adjacent to the reflected wave as elements is determined so as to estimate the power of the reflected wave.