scholarly journals Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qi-yong Liu ◽  
Qun Zhang ◽  
Fu-fei Gu ◽  
Yi-chang Chen ◽  
Le Kang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Linear Array ◽  
Multiple Measurement ◽  
Memory Space ◽  
Multiple Measurement Vectors ◽  
Huge Data ◽  
Imaging Result ◽  
Sar Imaging ◽  
Cross Track

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.

scholarly journals 3D Imaging Algorithm for Down-Looking MIMO Array SAR Based on Bayesian Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaozhen Ren ◽  
Lina Chen ◽  
Jing Yang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Additive Noise ◽  
Signal Reconstruction ◽  
3D Images ◽  
High Quality Image ◽  
Quality Image ◽  
New Strategy ◽  
Noisy Measurements ◽  
Cross Track

Down-looking MIMO array SAR can reconstruct 3D images of the observed area in the inferior of the platform of the SAR and has wide application prospects. In this paper, a new strategy based on Bayesian compressive sensing theory is proposed for down-looking MIMO array SAR imaging, which transforms the cross-track imaging process of down-looking MIMO array SAR into the problem of sparse signal reconstruction from noisy measurements. Due to account for additive noise encountered in the measurement process, high quality image can be achieved. Simulation results indicate that the proposed method can provide better resolution and lower sidelobes compared to the conventional method.

scholarly journals A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

2017 ◽  
Vol 9 (3) ◽  
pp. 297 ◽  
Author(s):  
Dongyang Ao ◽  
Rui Wang ◽  
Cheng Hu ◽  
Yuanhao Li
Keyword(s):  
Imaging Method ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Sar Imaging

scholarly journals Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Peng Song ◽  
Xinwen Shu
Keyword(s):  
Compressive Sensing ◽  
Satellite System ◽  
New Method ◽  
Signal Acquisition ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Navigation Data ◽  
Long Time ◽  
Global Navigation Satellite ◽  
Sparse Features

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

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