OFDM Signal Subspace Estimation for High-Resolution Target Sensing
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Solution Overview
Problem
Existing OFDM-based ISAC systems require increased bandwidth, coherent processing interval, and number of antennas to improve parameter resolution, leading to resource consumption and complexity.
Innovation Solution
A parameter estimation method and apparatus using a first reference matrix based on the signal subspace matrix of an OFDM signal, determining parameters like distance, speed, and horizontal angle by eigenvalue decomposition, reducing resource consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bandwidth, coherent processing interval, and number of antennas are increased to improve parameter resolution, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent changes the processing approach by using eigenvalue decomposition on reference matrices derived from signal and noise subspaces. This mathematical transformation allows accurate parameter estimation without increasing physical system parameters like bandwidth, processing interval, or antenna count, thereby resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent replaces the traditional mechanical/approach of improving resolution through physical resource allocation (more antennas, wider bandwidth, longer processing intervals) with a mathematical signal processing approach using subspace decomposition and eigenvalue analysis. This substitution achieves the same goal with fewer physical resources, reducing system complexity
2Measurement precision
If bandwidth, coherent processing interval, and number of antennas are increased to improve parameter resolution, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The patent transforms the problem from one requiring increased physical parameters to one solved through mathematical parameter extraction. By computing eigenvalues of reference matrices from existing signal subspaces, the system achieves high-resolution parameter estimation without the energy cost of increasing bandwidth, processing interval, or antenna数量
Solution Approach 2:
The patent substitutes energy-intensive physical resource allocation with computationally efficient mathematical operations. The eigenvalue decomposition approach processes existing signal data to extract precise parameters, avoiding the need for additional energy-consuming hardware or extended processing times
3Measurement precision
If eigenvalue-based matrix operations are used for parameter estimation, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent segments the signal space into distinct signal and noise subspaces through eigenvalue decomposition. This segmentation allows separate processing of signal-containing and noise-containing components, improving parameter estimation accuracy while organizing the computational task into manageable, structured steps that reduce overall computational burden
Data Source
AI summary
A parameter estimation method based on an orthogonal frequency division multiplexing (OFDM) signal, includes: determining a first reference matrix related to a first parameter of a target according to a signal subspace matrix of the OFDM signal, wherein the signal subspace matrix includes relevant information of at least one parameter of the target; and determining the first parameter based on a first reference eigenvalue of the first reference matrix.