Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.

Global Navigation Satellite System (GNSS) is the most reliable navigation system for location-based applications where accuracy and consistency is an essential requirement. The LSE (least squares estimator) has been used since the start of GNSS for position estimation. However; LSE is affected by outliers and errors in GNSS measurements and results in wrong user position. In this paper; we proposed a novel three-phase estimator for enhancing GNSS positioning accuracy in the presence of outliers and errors; relying upon the robust MM estimation theory. In the first phase; a subsampling process is proposed on available observations. IRWLS (iterative reweighted LS) is applied to all subsamples up to a predefined number of observations to obtain a positioning estimate and a scale factor. Secondly; IRWLS is applied up to the convergence point on a set of selected subsamples. The third phase involves the selection of optimum positioning solution having minimum scale factor. An outlier detection and exclusion process is applied on a probabilistic set of outlying observations to maintain the integrity and reliability of the position. Multiple simulated and real scenarios are tested. Results show high accuracy and reliability of the proposed algorithm in challenging environments.

Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process. In this study, we proposed a hybrid binary differential evolution and BAT optimization model to enhance contrast stretching by optimizing a decision variables in the transformation function. To validate its efficiency, the proposed approach is utilized as a preprocessor before feature extraction in image registration. Cross-comparison of detected features of enhanced images verses the original images during image registration validate the improvements in the image registration process.