Radar Target Detection With Sub-Sample Spectral Offset Estimation
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Solution Overview
Problem
Radar systems face limitations in target detection accuracy due to discrete spectral analysis, which is constrained by the sampling interval of the discrete spectrum, leading to inaccuracies in estimating physical quantities such as range, velocity, and angle.
Innovation Solution
A method involving discrete spectral function analysis and inverse function calculation is employed to estimate the offset between discrete point data and target real data, using polynomial fitting and Fourier transforms to improve accuracy, allowing for ultra-accuracy measurements without being limited by discrete spectrum sampling points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete spectrum analysis is used to obtain target parameters, then the detection process is simple and fast, but the measurement accuracy is limited by the sampling interval of the discrete spectrum
Solution Approach 1:
The patent introduces an intermediary polynomial fitting process between the discrete spectrum analysis and the final target parameter estimation. The polynomial function fits the spectral peak region to estimate sub-sample offsets, acting as a mediator that bridges the discrete spectrum data and the continuous target parameter estimation, thereby improving accuracy without requiring more discrete samples
Solution Approach 2:
The patent changes the parameter representation from discrete spectrum bin indices to continuous offset values relative to the peak bin. By estimating the offset between the actual spectral peak and the discrete bin center, the method transforms the problem from discrete sampling to continuous parameter estimation, achieving higher precision beyond the original sampling interval
2Measurement precision
If the sampling interval of the discrete spectrum is reduced to improve accuracy, then the target detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by focusing the polynomial fitting only on the spectral peak region rather than processing the entire spectrum. This localized approach achieves high-precision offset estimation with minimal computational effort, avoiding the need to increase the overall sampling rate or process all frequency bins with high precision
3Measurement precision
If more discrete sampling points are used in the discrete spectrum analysis, then the measurement accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the relevant information from the discrete spectrum data - specifically the peak bin location and the offset from the peak. By taking out only the essential features needed for accurate estimation and discarding the rest of the discrete data structure, the method achieves high accuracy with simplified processing that doesn't require handling large numbers of discrete sampling points
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves ultra-accuracy measurements with an estimation error reduced to 1/20 of existing discrete estimation methods, enhancing the precision of target detection in radar systems.
Implementation Method 1
performing Fast Fourier transform on the windowed discrete sampling points; wherein the fast Fourier transform includes at least one of a range Fourier transform, a velocity Fourier transform, and an angle Fourier transform
Implementation Method 2
constructing a fitting function based on energy values of the at least one first target point and the adjacent points of the at least first target point
Data Source
AI summary
A method and apparatus for improving target detection precision, an electronic device, and a non-transient computer-readable storage medium. The method comprises: obtaining a discrete spectrum function of an echo signal according to the discrete spectrum analysis process of the echo signal, wherein an independent variable of the discrete spectrum function is an offset between discrete point data obtained in discrete spectrum analysis on the basis of energy and target real data; constructing an inverse function of the discrete spectrum function by using the discrete spectrum function, wherein an independent variable of the inverse function is a discrete spectrum value of the echo signal; and calculating the offset by using the inverse function.