Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression
Abstract
:1. Introduction
- (1)
- Subarray division on the radar transmitter. The transmitting elements can be divided into several subarrays with the same number of elements in each subarray. In order to realize the spatial diversity of the transmitting subarrays, the orthogonal waveform signals are transmitted among different subarrays. Furthermore, the coherent waveform signal is transmitted within each subarray. In the case of this transmitting form, it can not only acquire the orthogonal transmitting waveform and reduce the dimension of the receiving data, but also utilize the directional gain and the coherence processing gain inside the transmitting subarray. Therefore, greater transmitting domain selectivity is provided for inhomogeneous clutter suppression.
- (2)
- Echo data acquisition of CUT. Multi-group echo data corresponding to different transmitting subarrays can be obtained by a single matched filter bank in each receiving element. Simultaneously, equivalent multi-frame echo data corresponding to all of the transmitting subarrays can be obtained in the whole receiving array.
- (3)
- Sparse spectrum calculation of CUT. Combined with a fast sparse Bayesian learning algorithm, the sparse spectrum of CUT is calculated by the joint sparse processing of the multi-frame echo data.
- (4)
- CCM estimation and clutter suppression. According to the approximate prior information of the target under test, it is removed from the sparse spectrum of CUT. Then, CCM is effectively estimated and the filtering weights are obtained.
2. Signal Model of Uniform Transmitting Subarray Diversity
3. Space–Time Joint Sparse Processing Based on One Snapshot Echo Observed Data and SBL
3.1. Equivalent Conversion of the Single Snapshot Echo Observed Data
3.2. Fast Equivalent Multi-Frame Echo Data Joint Sparse Processing Based on SBL
3.2.1. Sparse Solution Calculation of CUT
3.2.2. Clutter Suppression of STAP
- (1)
- The single snapshot echo observed data of CUT are obtained;
- (2)
- The equivalent W frames’ echo data of the phased array system can be reconstructed;
- (3)
- W frames’ echo data are represented by the joint sparse recovery method;
- (4)
- A fast sparse recovery algorithm based on the block SBL framework is used to obtain the sparse solution expression of the multi-frame echo data of CUT;
- (5)
- The sparse solution should be previously treated so as to adequately utilize the sparse results of W frames’ echo data;
- (6)
- The target components in the sparse solution are eliminated based on the approximate prior knowledge;
- (7)
- CCM can be separately estimated in the ideal and non-ideal conditions;
- (8)
- The filter weight is calculated to realize clutter suppression in the two conditions.
4. Simulation Experiments and Comparative Analyses
4.1. STAP Performance Comparison Using Different Algorithms
4.1.1. Simulation Results on the Sparse Spectrum
4.1.2. Simulation Results on Improved Factor
4.1.3. Simulation Results on Running Time
4.2. STAP Performance Comparison Using Different Radar Systems
4.2.1. Simulation Results on Improved Factor
4.2.2. Simulation Results on Running Time
4.3. STAP Performance Comparison in Different Non-Ideal Conditions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | Value | Parameter | Value |
---|---|---|---|
transmitting element size | 16 | number of coherent pulses | 8 |
receiving element size | 16 | airborne velocity (m/s) | 140 |
radar wavelength (m) | 0.23 | airborne height (m) | 8000 |
number of transmitting subarrays | 4 | target velocity (m/s) | 28 |
number of elements in each subarray | 4 | signal-to-noise ratio (dB) | 20 |
transmitting element interval (m) | 0.115 | clutter-to-noise ratio (dB) | 60 |
receiving element interval (m) | 0.115 | normalized temporal frequency | 0.4 |
pulse repetition frequency (Hz) | 2434.8 | normalized spatial frequency | 0 |
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Wang, Q.; Xue, B.; Hu, X.; Wu, G.; Zhao, W. Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression. Remote Sens. 2022, 14, 2647. https://doi.org/10.3390/rs14112647
Wang Q, Xue B, Hu X, Wu G, Zhao W. Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression. Remote Sensing. 2022; 14(11):2647. https://doi.org/10.3390/rs14112647
Chicago/Turabian StyleWang, Qiang, Bin Xue, Xiaowei Hu, Guangen Wu, and Weihu Zhao. 2022. "Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression" Remote Sensing 14, no. 11: 2647. https://doi.org/10.3390/rs14112647
APA StyleWang, Q., Xue, B., Hu, X., Wu, G., & Zhao, W. (2022). Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression. Remote Sensing, 14(11), 2647. https://doi.org/10.3390/rs14112647