Narrowband Interference Separation for Synthetic Aperture Radar via Sensing Matrix Optimization-Based Block Sparse Bayesian Learning

Abstract

High-resolution synthetic aperture radar (SAR) operating with a large bandwidth is subject to impacts from various kinds of narrowband interference (NBI) in complex electromagnetic environments. Recently, many radio frequency interference (RFI) suppression approaches for SAR based on sparse recovery have been proposed and demonstrated to outperform traditional ones in preserving the signal of interest (SOI) while suppressing the interference by exploiting their intrinsic structures. In particular, the joint recovery strategy of SOI and NBI with a cascaded dictionary, which eliminates the steps of NBI reconstruction and time-domain cancellation, can further reduce unnecessary system complexity. However, these sparsity-based approaches hardly work effectively for signals from an extended target or NBI with a certain bandwidth, since neither of them is sparse in a prescient domain. Moreover, sub-dictionaries corresponding to different components in the cascaded matrix are not strictly independent, which severely limits the performance of separated reconstruction. In this paper, we present an enhanced NBI separation algorithm for SAR via sensing matrix optimization-based block sparse Bayesian learning (SMO-BSBL) to solve these problems above. First, we extend the block sparse Bayesian learning framework to a complex-valued domain for the convenience of radar signal processing with lower computation complexity and modify it to deal with the separation problem of NBI in the contaminated echo. For the sake of improving the separated reconstruction performance, we propose a new block coherence measure by defining the external and internal block structure, which is used for optimizing the observation matrix. The optimized observation matrix is then employed to reconstruct SOI and NBI simultaneously under the modified BSBL framework, given a known and fixed cascaded dictionary. Numerical simulation experiments and comparison results demonstrate that the proposed SMO-BSBL is effective and superior to other advanced algorithms in NBI suppression for SAR.

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

“…With the intrablock correlation (IBC) (i.e., the temporal amplitude correlation among the elements within each block) taken into consideration, block sparse Bayesian learning (BSBL) is more effective to deal with highly underdetermined recovery problem. The modified BSBL-based methods are proposed in [90,91], which is verified to have superior recovery performance in various non-sparse cases.…”

Special Issue on “Advanced Technology Related to Radar Signal, Imaging, and Radar Cross-Section Measurement”

“…After a very careful peer-review process, a total of 32 papers were accepted. These works include SAR/ISAR [2][3][4][5][6][7][8][9], polarimetry [10][11][12], MIMO [13,14], direction of arrival (DOA)/direction of departure (DOD) [13][14][15], sparse sensing [5,14,16], ground-penetrating radar (GPR) [17][18][19], through-wall radar [20,21], coherent integration [22,23], clutter suppression [24,25], and meta-materials, among others [26][27][28][29][30][31]. All of these accepted papers are the latest research results and are expected to be further advanced, applied, and diverted.…”

Wideband Noise Interference Suppression for Sparsity-Based SAR Imaging Based on Dechirping and Double Subspace Extraction

“…In this part, we first set up a simulation environment for range profile imaging of point targets, where geometric and waveform parameters for SAR observations are listed in Table 1 [33,34]. Given that the distribution of the dechirped signal in the frequency domain depends on time width and distance differences according to Equation (27), the theoretical bandwidth ratio after dechirping observations is about 0.3 of the original.…”

“…The ACS algorithm eliminated large amounts of interference but also useful information, leading to serious distortion since the signal prior was not fully exploited. The BSBL algorithm also generated undesired components in the process of WBNI suppression, though it was superior in narrowband interference (NBI) separation by utilizing structural information and time correlation [33,34].…”

“…To further investigate the WBNI suppression effects with the proposed DDSE algorithm, we extended the simulation to the case of range-azimuth reconstruction, where an aircraft target with multiple scattering points was modeled and utilized for SAR imaging. The main parameters in this part are listed in Table 5 [33,34], and the intuitive results of range-azimuth imaging based on different WBNI suppression algorithms are shown in Figure 10. In Figure 10a,b, the reconstructed aircraft target is almost covered by WBNI and can hardly be distinguished if no measures are taken.…”