Proper mission control plays a key role in the lifetime of space mission operation, as it ensures that all resources are efficiently utilized when achieving mission goals. Ground control station operation mainly depends on received telemetry together with models simulating spacecraft`s subsystems. Created models help in raising the level of autonomy of MCC (Mission Control Center). Data driven models describe the actual state of the subsystem in real operation situations rather than theoretical costly physical models. This paper proposes data driven models for satellite battery subsystem based on Bayesian ridge regression algorithm. The ridge coefficients minimize a penalized residual sum of squares Thirty models of all thirty battery variables (capacitance, voltage, pressure and temperature) are built from normal operation data. Sensor reading value can be predicted from an observation of all other 29 values. Faults present in sensors or in system can be detected if predicted values are not equal to actual downloaded data from satellite. Bayesian ridge regression models are validated in terms of slope, intercept, R2-value, Q2 -value P-value and standard error.

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Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors. In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

Speckle noise is one of the most critical disturbances that alter the quality of Synthetic Aperture Radar (SAR) coherent images. Before using SAR images in automatic target detection and recognition, the first step is to reduce the effect of speckle noise. Several adaptive and non-adaptive filters are widely used for despeckling in SAR images. In this paper, a novel mathematical morphological filter is proposed to reduce the speckle noise in SAR images. The new filter performance is compared with a number of despeckling filters with different parameters. For performance measurements, four parameters were evaluated to test the filter ability to attenuate the speckle noise and keep target information. From experimental results, the new proposed morphological filter gives promising results for significantly suppressing speckle noise and preserving the potential targets.

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