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%.

In azimuth multichannel synthetic aperture radar (AMC-SAR) systems, channel phase errors can induce ghost targets. The estimation of signal parameters by the rotational invariance technique (ESPRIT) assumes that the principal signal subspace corresponds to the steering vector at zero Doppler frequency. However, this assumption is frequently violated in practice, resulting in performance degradation. To address this issue, a robust ESPRIT (RESPRIT) algorithm is proposed, which integrates initial ESPRIT-based phase error estimation with residual phase error correction through image contrast maximization. Both simulation and real airborne data experiments demonstrate the effectiveness and robustness of the proposed method.

The current phase error estimation approaches, such as the signal subspace comparison (SSC) method and the orthogonal subspace (OS) method, neglect the effect of noise. Consequently, their estimation accuracy deteriorates under low signal-to-noise ratio (SNR) conditions. In contrast, the adaptive weighted least squares (AWLSs) method accounts for noise. Its accuracy, however, deteriorates under highly nonuniform sampling conditions. Although the maximum normalized image sharpness (MNIS) algorithm achieves high estimation accuracy, it suffers from high computational complexity. To address these limitations, the novel phase error estimation methods based on weighted subspace fitting are proposed. The proposed methods explicitly account for noise by constructing two weighted fitting models between the subspace and the steering vector, from which phase errors are estimated via optimal solutions. Depending on the employed subspace, the proposed methods are classified as weighted signal subspace fitting (WSSF) and weighted noise subspace fitting (WNSF). Simulations and airborne experiments verify the accuracy and robustness of the proposed methods.