[1] D YANG, L X WU, S A WANG et al. How big data enriches maritime research–a critical review of automatic identification system (AIS) data applications. Transport Reviews, 39, 755-773(2019).

[2] T ERIKSEN, G HØYE, B NARHEIM et al. Maritime traffic monitoring using a space-based AIS receiver. Acta Astronautica, 58, 537-549(2006).

[3] [3] YANG M H, ZOU Y Z, FANG L. Collision detection perfmance with three overlap signal collisions in spacebased AIS reception[C]2012 IEEE 11th International Conference on Trust, Security Privacy in Computing Communications. Liverpool: IEEE, 2012: 1641−1648.

[4] W HASBI, KAMIRUL. Tracking capability and detection probability assessment of space-based automatic identification system (AIS) from equatorial and polar orbiting satellites constellation. IEEE Access, 8, 184120-184136(2020).

[5] [5] KIM Y B, KIM J H, WANG Y P, et al. Application scenarios of nautical adhoc wk f maritime communications[C]OCEANS 2009. Biloxi: IEEE, 2009: 1−4.

[6] [6] CHERRAK O , GHENNIOUI H , THIRIONMEAU N E, et al. Successive interference cancellation technique f decollision of AIS signals in maritime surveillance context by a LEO satellite[C]The International Conference on Wireless Technologies, Embedded Intelligent Systems (WITS) . [S. l. ]: [s. n. ], 2014.

[7] M FOURNIER, HILLIARD R CASEY, S REZAEE et al. Past, present, and future of the satellite-based automatic identification system: areas of applications (2004 –2016). WMU Journal of Maritime Affairs, 17, 311-345(2018).

[8] Z XIAO, X J FU, L Y ZHANG et al. Traffic pattern mining and forecasting technologies in maritime traffic service networks: a comprehensive survey. IEEE Trans-actions on Intelligent Transportation Systems, 21, 1796-1825(2020).

[9] A KRISHNA, A NIMBAL, A MAKAM et al. Implementation of fast independent component analysis on field-programmable gate array for resolving the slot collision issue in the space-based automatic identification system. International Journal of Satellite Communications and Networking, 38, 480-498(2020).

[10] C JUTTEN, J HERAULT. Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1-10(1991).

[12] J J RIETA, F CASTELLS, C SÁNCHEZ et al. Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Transactions on Biomedical Engineering, 51, 1176-1186(2004).

[13] A HYVÄRINEN, E OJA. A fast fixed-point algorithm for independent component analysis. Neural Computation, 9, 1483-1492(1997).

[14] S C DOUGLAS, J C CHAO. Simple, robust, and memory-efficient FastICA algorithms using the Huber M-estimator cost function. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 48, 143-159(2007).

[15] X MENG, L Y YANG, P XIE et al. An improved FastICA algorithm based on modified-M estimate function. Circuits, Systems, and Signal Processing, 37, 1134-1144(2018).

[16] [16] MA S X, GUAN Y Q. Spacebased AIS multiuser separation based on the improved complex value FastICA[C]Proceedings of 2013 2nd International Conference on Measurement, Infmation Control. Harbin: IEEE, 2013: 537541.

[17] J W E, J M YE, L L HE et al. Performance analysis for complex-valued FastICA and its improvement based on the Tukey M-estimator. Digital Signal Processing, 115, 103077(2021).

[18] V ZARZOSO, P COMON. Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Transactions on Neural Networks, 21, 248-261(2010).

[19] F Y CHEN, N P MAO, Y WANG et al. Improved utility harmonic impedance measurement based on robust independent component analysis and bootstrap check. IET Generation, Transmission & Distribution, 14, 910-919(2020).

[20] D MALLIS, T SGOUROS, N MITIANOUDIS. Convolutive audio source separation using robust ICA and an intelligent evolving permutation ambiguity solution. Evolving Systems, 9, 315-329(2018).

[21] R H R PRUIM, M MENNES, ROOIJ D VAN et al. ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267-277(2015).

[22] L F CAI, X M TIAN, S CHEN. A process monitoring method based on noisy independent component analysis. Neurocomputing, 127, 231-246(2014).

[24] [24] DEVILLE Y, PUIGT M, ALBOUY B. Timefrequency blind signal separation: extended methods, perfmance evaluation f speech sources[C]2004 IEEE International Joint Conference on Neural wks. Budapest: IEEE, 2004: 255−260.

[25] H HASSANI. Singular spectrum analysis: methodology and comparison. Journal of Data Science, 5, 239-257(2007).

[26] S H CHAO, C H LOH. Application of singular spectrum analysis to structural monitoring and damage diagnosis of bridges. Structure and Infrastructure Engineering, 10, 708-727(2014).