Raw measurements from Global Navigation Satellite Systems (GNSS) provide important data for aerogravimetry [1, 2]. While commercial GNSS software focus primarily on determination of coordinates, the problem of determining acceleration specifically using raw satellite measurements—doppler/carrier phase—has never been addressed. The paper presents a postprocessing solution to this problem using raw GNSS measurements and ephemeris data. The basic idea is to differentiate single differences of Doppler and phase measurements. A qualitative analysis of the experimental data processing results is provided. The authors develop the approach outlined in [2] for the velocity determination problem.

The paper presents the main principles of extending stationary data models relevant for testing the navigation equipment. Two groups of extended data models are considered. The first group includes stationary random processes with the added systematic errors; the second group is a set of random processes with stationary increments. The features of the described nonstationary data models are considered. Parameter estimation methods in extended models and conditions for consistent estimates are discussed. Consistency conditions are compared with those for stationary models.

This paper introduces TriLayer-Nav, a modular tri-layer hybrid navigational system that integrates global planning, reactive local planning, and predictive control optimization to achieve smooth, energy-efficient, and robust navigation of differential-drive ground robots. The architecture employs the A* algorithm for the computation of a collisionfree global path, the Dynamic Window Approach for reactive obstacle avoidance, and Model Predictive Control for refining the Dynamic Window Approach commands while at the same time satisfying non-holonomic and actuator constraints. The different layers function in cascade (A* at a low frequency, Dynamic Window Approach in real-time, and Model Predictive Control continuously refining commands), with final wheel-level tracking being performed by a PID controller. TriLayer-Nav has undergone intensive simulation and validation in a physics-based environment exploiting the MuJoCo platform, allowing detailed modelling of rigid-body dynamics, frictional interactions, and actuator feedback. In the simulation protocol, a complete hierarchical allocation of commands was highlighted throughout the system and at the same time maintained realtime computation throughput. The findings show that TriLayer-Nav produces smoother paths with less curvature discontinuities, reduced control oscillations, better heading accuracy, and lower energy use, and with a success rate of 96.6%, while path efficiency is better than that of implementations that solely rely on A* or Dynamic Window Approach. Layer interaction provides better reaction to environmental changes and sensor noise. It is also computationally viable to run the framework in real-time by using a nonlinear Model Predictive Control solver and its modular hierarchy allows it to be implemented in structured and semi-structured environments on a large scale. To sum up, the findings deliver strong evidence that combining global planning, local reactive planner and predictive optimization is a significant improvement in enhancing the reliability, stability and energy efficiency of autonomous navigation. TriLayer-Nav is a generalizable and computationally efficient navigation solution that can be applicable to a wide range of ground robotic systems used in dynamic and uncertain environments.

The accuracy of user position primarily depends on the behaviour of on-board clock. Any anomaly in clock behaviour would lead to inaccuracy in user position. Hence, the behaviour of on-board clock needs to be monitored continuously to ensure reliability in user position. This paper describes a methodology employed in NavIC constellation for anomaly detection using one way carrier phase measurement data. This methodology employs fractional frequency of on-board clock and addresses the presence of any integer ambiguity in the carrier phase measurement. Further, this methodology involves a novel algorithm that detects clock anomalies such as outliers, phase as well as frequency jumps. Following the detection of anomaly, the methodology would instantly initiate the process of estimating and updating the clock correction parameters which would subsequently broadcasted to the user.

This paper presents a Fast Block Kalman Filter (FBKF) for visual-inertial navigation. The filter recursively estimates the state vector describing the navigation parameters of a moving object and the coordinates of N visual features with reduced computational complexity, O(N), achieved through the decomposition of the estimation algorithm. It is shown that through applying the principal component analysis, the estimates of the block filter remain close to those of the Extended Kalman Filter (EKF), which, as shown previously, provides high estimation accuracy when consistent error models are used. The O(N) complexity is maintained even when all N features are observed simultaneously for an arbitrary time interval. The trade-off between computational time and FBKF accuracy is achieved by using a special procedure based on expanding the original state vector; negligible deviations from the EKF estimates are obtained for expansion dimensions that have only a minor effect on the computational burden of the proposed filter. A comparison with the EKF in terms of computational time and produced estimates is carried out by simulation of a visual aided INS. The results demonstrate the possibility of processing hundreds of features in real time in single-threaded mode.

In the paper, positioning of receivers of a bottom linear sonar array with a known method is simulated. The conditions under which the maximum RMS error of 1 m is provided are determined by the simulation. Simulation results are experimentally confirmed.

The article proposes an algorithm for determining the coordinates and motion parameters of an underwater target during multistatic sonar detection, which does not require any data on the attitude of the receiving antenna freely rotating around a vertical axis. The novelty of the algorithm is that the target’s bearing and distance are determined using the measured angle between the direction of the projector’s direct signal arriving at the receiving antenna, and the direction of the same signal reflected from the target; the difference in the time of direct and reflected signals detection is used as well. For the algorithm application, it is essential that the receiving antenna is equipped with an angular velocity sensor. The algorithm performance has been verified by simulation, and the accuracy of estimating the target’s coordinates and motion parameters has been confirmed, considering various effects.