A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD’s functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China’s first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second.

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties.

Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering.

Multi-baseline phase unwrapping (PU) is an extension of single-baseline PU. Its accuracy directly affects the reliability of results in engineering tasks, such as InSAR topographic mapping and geological hazard monitoring, in complex scenarios. Meanwhile, its efficiency determines the timeliness of data delivery in emergency scenarios. The cluster-analysis (CA)-based algorithm represents a significant advancement in multi-baseline PU algorithms, wherein a strategy for pixel clustering and uniform PU is introduced. However, in the CA algorithm, phase noise degrades pixel clustering performance, leading to deviations in the determination of intercept centerlines and ultimately errors in ambiguity number search. In addition, the computational complexity is increased by the search for intercept peaks and ambiguity numbers. To address these limitations and ensure that accuracy and efficiency requirements are met in practical applications, an improved closed-form multi-baseline PU algorithm is proposed in this article. Compared with conventional CA algorithms, this algorithm offers the following four improvements. First, differential phase processing is introduced into the algorithm, which not only mitigates the impact of phase noise on pixel clustering but also provides new inputs for subsequent ambiguity-number solution. Secondly, a novel method for calculating the theoretical intercept is proposed, which depends solely on the external reference DEM and the ambiguity height. Thirdly, to eliminate the need for peak-intercept search and to suppress error propagation from incorrect intercepts, an intercept filtering method is introduced into the algorithm. In this method, a categorized filtering of actual intercepts for all pixels is performed. Fourthly, to address the phase-noise sensitivity and low efficiency in ambiguity-number search, the algorithm proposes a closed-form ambiguity-number solution method based on the Chinese Remainder Theorem (CRT). In this method, calculation accuracy can be ensured and solution efficiency improved by constructing and solving CRT equation groups with filtered error-free intercepts as remainders. The aforementioned four points are not independent of each other, but are strongly logically dependent and correlated. The effectiveness of the proposed algorithm is validated through one simulated data experiment and two real data experiments. The proposed algorithm achieves improvements in accuracy and efficiency across the three datasets. In terms of accuracy, the RMSE is reduced by at least 11.52%, while the PUSR increases by at least 1.36%. In terms of efficiency, runtime is shortened by at least 29.75%.