In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at −40 °C, −20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

Accurate bathymetric mapping for shallow-water areas is essential for coastal and maritime engineering applications. However, traditional multibeam or light detection and ranging (LiDAR) survey techniques used to produce high-quality bathymetric maps are expensive. Satellite-derived bathymetry provides a fast and inexpensive method for the large-scale mapping of shallow-water areas and can overcome the complexities of traditional bathymetric mapping methods in these areas. Traditionally, linear regression models, most commonly the Stumpf model, are used for satellite-based bathymetric modeling. However, nonlinear artificial neural network (ANN) models have been recently developed and implemented for satellite-based bathymetric modeling and are under significant investigation to develop the most accurate and optimal model. This article proposes two new hybrid ANN-based models for bathymetric modeling and investigates their performance using satellite imagery data and “truth” depth data for a coastal shallow-water study area. Two-hybrid ANN algorithms are developed, namely, particle swarm optimization (PSO)-ANN and optimally pruned extreme learning machine (OPELM), and their results are compared with the traditional Stumpf method and current state-of-the-art ANN model. The study area dataset comprises the “truth” depth data from a nautical chart of the Alqumriyah Island study area in Saudi Arabia and the corresponding spectral reflection values of green, blue, and near-infrared bands from the free-of-charge Level-1C product of Sentinel-2A images used to train and validate the two newly developed models and the traditional models. The results show that the developed OPELM method can accurately derive the bathymetry and is superior to the developed PSO-ANN model, the current state-of-the-art ANN model, and the traditional Stumpf model by 12.10%, 18.76%, and 32.46%, respectively. The OPELM model can also be used for bathymetric modeling of shallow-water areas with depths up to 30 m with a high level of accuracy compared with the current state-of-the-art ANN and traditional methods. The significant contribution of this research is that it is the first investigation of the artificial intelligence-based hybrid OPELM method for accurate bathymetric modeling and will certainly encourage further investigations of hybrid models. Moreover, this research explores whether these developed hybrid models can meet the International Hydrographic Organization standards for hydrographic survey applications.

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.