Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR
Abstract
:1. Introduction
- (1)
- The proposed method belongs to the class of DOA estimation algorithms and relies solely on angle information for multi-target pairing.
- (2)
- Unlike track association methods, the proposed method eliminates the need for multiframe association operations and complex mathematical computations. Moreover, the proposed method does not require iterative operations or spectral peak searching, making it computationally efficient.
- (3)
- If the two subarrays are considered as a single non-uniform linear array for angle estimation and multi-target pairing, the long baseline can lead to grating lobes and angle ambiguity. The proposed method can avoid these issues.
- (4)
- By employing the generalized virtual array aperture extension, we construct a large generalized received data matrix, which enables the proposed method to maintain robust angle estimation and multi-target pairing performance under small angular interval target conditions.
2. Signal Model
- (1)
- The K targets are mutually uncorrelated, and the number of targets is less than the number of sensors in the subarray, i.e., .
- (2)
- The additive noises and on the two subarrays are both zero-mean, Gaussian white noises with a variance of in time and space. The noises are statistically independent from each other, i.e., , and their covariance matrix is as follows: , .
- (3)
- To facilitate theoretical performance analysis, the incident signal at the -subarray is modeled as the zero-mean, temporally complex Gaussian random process, and the associated variance is determined by and for .
- (4)
- The incident signals and are statistically independent from the additive noise and at the two subarrays.
3. Proposed Method
3.1. Construction of Cross-Correlation Matrix Based on Long Baseline Array Signals
3.2. Generalized Virtual Array Aperture Extension Technique
3.3. Multi-Target Pairing with DOA Estimation
3.4. Complexity Analysis
4. Simulation Results
4.1. Simulation Parameter Settings
4.2. Simulation Results and Analysis
4.2.1. Performance vs. SNR: Example 1
4.2.2. Performance vs. Number of Snapshots: Example 2
4.2.3. Performance vs. Number of Subarray Elements: Example 3
4.2.4. Performance vs. Amplitude and Phase Variation Factor: Example 4
4.2.5. Performance vs. Length of Baseline: Example 5
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Li, S.; Wu, X.; Chen, S.; Deng, W.; Zhang, X. Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR. Remote Sens. 2024, 16, 3128. https://doi.org/10.3390/rs16173128
Li S, Wu X, Chen S, Deng W, Zhang X. Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR. Remote Sensing. 2024; 16(17):3128. https://doi.org/10.3390/rs16173128
Chicago/Turabian StyleLi, Shujie, Xiaochuan Wu, Siming Chen, Weibo Deng, and Xin Zhang. 2024. "Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR" Remote Sensing 16, no. 17: 3128. https://doi.org/10.3390/rs16173128