A study of BP-camp algorithm for SAR imaging

Abstract

Recently, the sparse reconstruction algorithms (SRAs) based on compressive sensing (CS) have been applied in the fields of synthetic aperture radar (SAR) imaging and show plenty of potential advantages. However, due to the great computational complexity and memory cost caused by matrix-vector multiplications, most of these algorithms are not suitable to reconstruct large-scale observed scenes. To solve this problem, we construct a backprojection based imaging operator, and introduce it to the complex approximate message passing algorithm (CAMP). The new image formation algorithm is called BP-CAMP in this paper. Compared with the approximated observation methods deduced from the FFT-based imaging technology, BP-CAMP is not limited by observation models of the radar and motion modes of the platform, and it therefore possesses universal applicability. By the simulations and real data processing, the experimental results show that BP-CAMP has lower computational complexity and memory cost than CAMP, and also achieves SAR imaging with undersampled echo data.Index Terms-Backprojection, complex approximate message passing algorithm (CAMP), compressive sensing (CS), synthetic aperture radar (SAR).

A Novel SAR Imaging Method Based on Morphological Component Analysis

“…However, for scenes with complex spatial distribution of scattering coefficient, it is necessary to find a dictionary that can sparsely represent a morphological component and only this component is recovered through the corresponding sparse reconstruction process. With dictionary 𝚽 𝑡 for sparse representation of 𝐭 and 𝚽 𝑐 for sparse representation of 𝐜, (9) can be rewritten as: 𝐬 = 𝐇(𝚽 𝑡 𝜶 𝑡 + 𝚽 𝑐 𝜶 𝑐 ) + 𝐧 = 𝐀 𝑡 𝜶 𝑡 + 𝐀 𝑐 𝜶 𝑐 + 𝐧 (10) where 𝜶 𝑡 and 𝜶 𝑐 are the sparse coefficients of 𝐭 and 𝐜 in the dictionary 𝚽 𝑡 and 𝚽 𝑐 , respectively. In (10), 𝐀 𝑡 = 𝐇𝚽 𝑡 , 𝐀 𝑐 = 𝐇𝚽 𝑐 , the observation matrix 𝐇 is defined in the same way as that in CS-SAR, and please refer to [17], [19] for its construction in detail.…”

“…With dictionary 𝚽 𝑡 for sparse representation of 𝐭 and 𝚽 𝑐 for sparse representation of 𝐜, (9) can be rewritten as: 𝐬 = 𝐇(𝚽 𝑡 𝜶 𝑡 + 𝚽 𝑐 𝜶 𝑐 ) + 𝐧 = 𝐀 𝑡 𝜶 𝑡 + 𝐀 𝑐 𝜶 𝑐 + 𝐧 (10) where 𝜶 𝑡 and 𝜶 𝑐 are the sparse coefficients of 𝐭 and 𝐜 in the dictionary 𝚽 𝑡 and 𝚽 𝑐 , respectively. In (10), 𝐀 𝑡 = 𝐇𝚽 𝑡 , 𝐀 𝑐 = 𝐇𝚽 𝑐 , the observation matrix 𝐇 is defined in the same way as that in CS-SAR, and please refer to [17], [19] for its construction in detail.…”

“…The first type includes the back-projection (BP) algorithm [6], the range-Doppler algorithm (RDA) [7], the chirp-scaling algorithm (CSA) [8], and the Omega-K algorithm (𝜔KA) [9], etc. The BP algorithm obtains the SAR image by range compression, spatial domain interpolation, and azimuth coherent addition [10]. The other algorithms, known as the frequency-domain approach, usually consist of the following main operations: range compression, range migration correction, and azimuth compression.…”

A Modified Three-Stage Inversion Algorithm Based on R-RVoG Model for Pol-InSAR Data

“…Relevant simulation parameters are provided in Table 1. Back Projection (BP) imaging algorithm is recruited here to achieve fully polarimetric SAR imaging without destructing any phase information [25]. Figure 3 gives the imaging results of Horizontal transmit and Horizontal receive (HH), Horizontal transmit and Vertical receive (HV), Vertical transmit and Horizontal receive (VH) and Vertical transmit and Vertical receive (VV) polarimetric data derived from the master sensor when range slope is set as 0°.…”

“…Relevant simulation parameters are provided in Table 1. Back Projection (BP) imaging algorithm is recruited here to achieve fully polarimetric SAR imaging without destructing any phase information [25]. …”