An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation

Abstract

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

“…Although an undertaken verification of sampling points did not reveal any false samples, it was not possible to manually inspect all of them due to the large amount of sampling points (60,000 for each study site). Detection results could be further improved by incorporating each of the following additional bands-if made available in GEE at a reasonably high spatial resolution (e.g., of 20 m or better): TWI, STI, geological structure, and soil moisture data from advanced SAR processing [80], or a raster image with annual rainfall data. This study also lacked post-earthquake elevation data, but if available, the inclusion of data containing precise elevation changes could potentially further enhance the accuracy of the detection results.…”

A 2-D Range Ambiguity Suppression Method Based on Blind Source Separation for Multichannel SAR Systems

“…Furthermore, it is also worth to recall that the significance of this paper is also referred to its adoption of a realistic data model for formulating the range ambiguity problem. Specifically, the model employs a complex-valued mixing matrix with dependencies in both range-time and Dopplerfrequency directions, in contrast to the approach in [22], [23] which utilizes a real-valued mixing matrix and incorporates only stationary values. Moreover, as explained in Section III, the proposed higher-order BSS is expected to provide a better robustness to noise, compared to a second-order BSS approach, as the one in [22], [23].…”

“…Specifically, the model employs a complex-valued mixing matrix with dependencies in both range-time and Dopplerfrequency directions, in contrast to the approach in [22], [23] which utilizes a real-valued mixing matrix and incorporates only stationary values. Moreover, as explained in Section III, the proposed higher-order BSS is expected to provide a better robustness to noise, compared to a second-order BSS approach, as the one in [22], [23]. [53], the blue curve indicates SOBI BSS in [23], and the green curve represents the proposed method.…”

“…Moreover, as explained in Section III, the proposed higher-order BSS is expected to provide a better robustness to noise, compared to a second-order BSS approach, as the one in [22], [23]. [53], the blue curve indicates SOBI BSS in [23], and the green curve represents the proposed method. The spatial separation between the simulated swath segments is not visualized.…”

“…The idea behind the proposed method was originally proposed in [15] as one opportunity to obtain the calibration coefficients for the cross elevation beam range ambiguity suppression technique CEBRAS; first results, presented already in short conference papers [20], [21], are here extended and analysed in detail. A BSS-based method, to retrieve the useful SAR signals in a multibeam SAR system, was recently proposed also in [22] and [23]. Compared to these last publications, the uniqueness and novelty of the present approach relies on the detailed data model and the realistic analysis.…”