In response to the needs of future multi-domain space fighting situations such as multi-platform, strong confrontation, clustering, Guidance, Navigation and Control (GNC) et. al, a robust navigation filtering algorithm with adaptive weight distribution is proposed. The algorithm breaks through the innovation measurement update method of the centralized satellite navigation system during the traditional integrated navigation, which is adapted by strapdown inertial navigation attitude, speed, position update and satellite navigation system innovation measurement. Combining the elevation angle of each tracked visible satellite by the integrated navigation receiver and the satellite signal carrier-to-noise ratio, it is carried out by real-time adjustment of the integrated navigation Kalman filter observation noise matrix and performing adaptive weight distribution of the observation equation. Updating the integrated navigation Kalman filter state equation and error covariance matrix is used by the satellite and inertial navigation pseudorange and pseudorange rate measurement values. Finally, the inertial device measurement error is corrected by the information fusion state results of the satellite and inertial navigation, which improves GNC system information fusion positioning accuracy and system robustness.
Under the condition of a low sampling rate, hyperspectral image (HSI) reconstruction faces important challenges in remote sensing. How to efficiently process HSI data is an urgent problem to be solved. We propose a hyperspectral image compressive sensing reconstruction (HSI-CSR) model based on tensor decomposition and a low-rank constraint. This model can efficiently exploit the underlying structure information in the HSI. Specifically, we study how to exploit reasonably the low-rank constraint of the core tensor and nonlocal self-similarity, respectively, to explore the nonlocal spatial–spectral similarity hidden in an HSI. To solve the proposed HSI-CSR model, we design an efficient algorithm based on the alternating direction method of multipliers knowledge. Finally, extensive simulations show that the proposed model achieves superior reconstruction performance, compared with other state-of-the-art methods.
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