Radar Target Detection Using Estimation Before Detection
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Solution Overview
Problem
Conventional radar target detection methods face challenges such as high computational complexity, large reconstruction errors for off-grid targets, and non-constant false alarm rates due to the ultrahigh dimensionality of signal models and the nonlinearity of reconstruction methods.
Innovation Solution
The proposed radar target detection method employs Estimation Before Detection (EBD) by first performing pulse compression and coherent integration to obtain a range-Doppler map, estimating pre-detected target parameters, establishing a dimension-reduction observation model, and using sparse recovery algorithms for target reconstruction and detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sparse recovery methods are used for target detection, then target reconstruction accuracy is improved, but computational complexity increases under ultrahigh dimension
Solution Approach 1:
The patent performs parameter estimation before detection to obtain preliminary target parameter information. This preliminary action enables the construction of a dimension-reduced observation model that focuses only on relevant parameter spaces, thereby reducing computational complexity while maintaining reconstruction accuracy.
Solution Approach 2:
The patent transforms the ultrahigh-dimensional target parameter space into a lower-dimensional parameter space by estimating target parameters first and then constructing an observation model only in the estimated parameter dimensions. This dimensionality reduction significantly decreases computational complexity while preserving reconstruction accuracy.
2Ease of operation
If conventional matched filtering is used, then detection process is simple, but detection performance deteriorates due to sidelobe effects and mismatch loss
Solution Approach 1:
The patent performs parameter estimation before detection to obtain preliminary target parameter information. This enables the construction of an adapted observation model that matches the actual target characteristics, thereby improving detection performance while maintaining operational simplicity through the systematic EBD framework.
Solution Approach 2:
The patent changes the detection approach from fixed matched filtering to an adaptive sparse recovery-based detection. By estimating target parameters first and then constructing a dimension-reduced observation model tailored to the estimated parameters, the system adapts to actual target characteristics, improving detection performance while maintaining simplicity through the structured EBD process.
3Device complexity
If dimension reduction is applied to AMP method, then computational complexity is reduced, but target detection effectiveness deteriorates
Solution Approach 1:
The patent performs parameter estimation before constructing the observation model. This preliminary action enables the dimension-reduced AMP method to focus computational resources on the most relevant parameter spaces, maintaining detection effectiveness while reducing complexity by avoiding unnecessary computations in irrelevant dimensions.
Solution Approach 2:
The patent transforms the full-dimensional observation model into a dimension-reduced model based on estimated target parameters. This dimensionality change allows the AMP method to operate in a lower-dimensional space that still captures the essential target characteristics, maintaining detection effectiveness while reducing computational complexity.
Data Source
AI summary
The present invention provides a radar target detection method based on estimation before detection (EBD), which comprises: obtaining pre-detect targets (PDTs) based on conventional pulse-Doppler processing and pre-detection; estimating ranges and speeds of PDTs, i.e., performing parameter EBD; establishing a dimension-reduction observation model of a received signal based on PDTs and parameter thereof; reconstructing a target vector in the dimension-reduction observation model based on a sparse recovery algorithm; and designing a generalized likelihood ratio detector based on the reconstruction result for target detection. The method of the present invention can significantly reduce the radar signal processing loss, and the target detector used in the method has the constant false alarm rate (CFAR) property, so that the weak target detection performance can be greatly improved.