Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth–Range Decouple $L_{1}$ Regularization for TOPS SAR Imaging via CAMP

Abstract

This paper proposes a novel azimuth-range decouple based L1 regularization imaging approach for the focusing in Terrain Observation by Progressive Scans (TOPS) synthetic aperture radar (SAR). Due to conventional L1 regularization technique requires transferring the two-dimensional (2-D) echo data into a vector and reconstructing the scene via 2-D matrix operations leading to significantly more computational complexity, it very difficult to apply in high-resolution and wide-swath SAR imaging, e.g., TOPS. The proposed method can achieve azimuth-range decouple by constructing an approximated observation operator to simulate the raw data, the inverse of matching filtering (MF) procedure, which makes large-scale sparse reconstruction, or called compressive sensing (CS) reconstruction of surveillance region with full-or down-sampled raw data in TOPS SAR possible. Compared to MF algorithm, e.g., extended chirp scalingbaseband azimuth scaling (ECS-BAS), it shows huge potential in image performance improvement. While compared with conventional L1 regularization technique, it significantly reduces the computational cost, and provides similar image features. Furthermore, this novel approach also can obtain a non-sparse estimation of considered scene retaining a similar background statistical distribution as MF based image, which can be used to the further application of SAR images with precondition being a preserving image statistical properties, e.g., constant false alarm rate detection (CFAR). Experimental results along with a performance analysis validate the proposed method. Index Terms-Synthetic aperture radar (SAR), terrain observation by progressive scans (TOPS), extended chirp scaling (EC-S), baseband azimuth scaling (BAS), azimuth-range decouple, L1 regularization, complex approximated message passing (CAMP).

Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances

“…The use of regularization methods in radar imaging goes back at least to the year 2000 [21,24]. Since the CS theory was proposed in 2006, it has been explored for a wide range of radar [25,26,27,28,29,30,31,32,33] and radar imaging applications [4,34,35,36,37,38], including synthetic aperture radar (SAR) [39,40,41,42], inverse SAR (ISAR) [43,44,45], tomographic SAR [46,47,48,49,50,51], three-dimensional (3D) SAR [52,53,54], SAR ground moving target indication (SAR/GMTI) [55,56,57,58,59,60,61], ground penetrating radar (GPR) [62,63,64], and through-the-wall radar (TWR) [65,66,67]. In this paper, we will focus on two-dimensional (2D) imaging radar systems, i.e., SAR, GPR, and TWR.…”

Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple

“…The coupling of the 2D data can be removed by constructing an echo simulation operator to replace the observation matrix, which can effectively relieve the computational complexity [16]. This method has been widely used in TOPS SAR [17], Sliding Spotlight SAR [18], displaced phase center antenna (DPCA) imaging [19], wide-angle SAR (WASAR) [20] and ground moving target indication (GMTI) [21]. We can combine it with automatic parameter estimating methods to achieve an adaptive parameter estimation of the large-scale sparse SAR imaging.…”

Comparison of Raw Data-Based and Complex Image-Based Sparse SAR Imaging Methods

“…The method can reduce imaging time efficiently and improve image performance based on the fully sampled or down-sampled echo data for the sparse region [14,15]. The method has been widely applied to ScanSAR [16], TOPSAR [17], Sliding Spotlight SAR [18] and so on. Quan et al applied sparse signal processing methods to nonuniform displace phase center sampling SAR imaging [19].…”