Large-Scale MIMO Antenna Angle Estimation via Segmented Models
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Solution Overview
Problem
Conventional target signal detection methods for radar antennas are unable to accurately identify the position of objects in a timely manner, particularly in dynamic environments like vehicle radar systems, where fast and precise detection is crucial for collision avoidance.
Innovation Solution
A method using large-scale MIMO array antennas that involves inputting an input signal matrix into a series of calculation models for singular value decomposition and angle iteration, combining orthogonal matching pursuit and MUltiple SIgnal Classification algorithms to efficiently estimate the angle of target objects, thereby improving detection accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional target signal detection methods are used, then the detection process is simpler, but the detection accuracy and speed are insufficient for dynamic environments
Solution Approach 1:
The detection process is divided into multiple sequential calculation models: first obtaining initial angle estimates, then performing singular value decomposition to separate signal and noise subspaces, and finally conducting angle iteration to refine estimates. This segmentation transforms a single complex detection task into manageable stages, improving accuracy without overwhelming computational burden.
Solution Approach 2:
The method performs preliminary angle estimation before singular value decomposition and noise matrix calculation. By obtaining rough angle estimates first, the system narrows down the search space for subsequent precise calculations, making the overall process more efficient and accurate for dynamic target detection.
2Productivity
If conventional detection methods are used, then the calculation process is faster, but the detection time period is too long to prevent collisions
Solution Approach 1:
The method performs preliminary angle estimation before singular value decomposition and noise matrix calculation. By obtaining rough angle estimates first, the system narrows down the search space for subsequent precise calculations, making the overall process more efficient and accurate for dynamic target detection.
Solution Approach 2:
The angle iteration process focuses computational resources on specific angle ranges identified in preliminary estimation, rather than uniformly processing all possible angles. This localized refinement approach reduces overall calculation time while maintaining high detection accuracy for moving targets.
3Measurement precision
If conventional detection methods are used, then the system is easier to operate, but the position identification accuracy is insufficient
Solution Approach 1:
The detection process is divided into multiple sequential calculation models: first obtaining initial angle estimates, then performing singular value decomposition to separate signal and noise subspaces, and finally conducting angle iteration to refine estimates. This segmentation transforms a single complex detection task into manageable stages, improving accuracy without overwhelming computational burden.
Solution Approach 2:
The singular value decomposition acts as an intermediary step that separates the input signal matrix into signal and noise subspaces. This intermediate processing enables more accurate angle estimation by filtering out noise components before final angle calculation, thereby improving position identification accuracy.
Data Source
AI summary
A method for estimating object angle with high-angle analysis using a large-scale MIMO array antenna, wherein the array antenna receives the input signal matrix which is the transmission or reflection signal of at least one object. The method includes: step S1: inputting the input signal matrix to a first calculation model to obtain a target object amount and a rough object angle with respect to the location thereof; step S2: inputting the object amount and the input signal matrix to a second calculation model for singular value decomposition to obtain a noise matrix; step S3: obtaining an iteration angle range from the rough angle in S1; step S4: inputting the plurality of pursuit matrices corresponding to the iteration angle range and the noise matrix to a third calculation model for angle range iteration, thereby acquiring an accurate object angle.