Global Navigation Satellite System (GNSS) measurements provide important data for airborne gravimetry [1, 2]. While commercial GNSS software focuses primarily on determination of coordinates, the problem of determining acceleration specifically using Doppler/carrier phase measurements has never been addressed. The paper presents a postprocessing solution to this problem using GNSS raw measurements and ephemeris data. The basic idea is to differentiate single differences of Doppler and carrier 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.
This paper presents a Fast Block Kalman Filter (FBKF) for visual-inertial navigation. The filter recursively estimates the state vector that includes the navigation parameters of a moving object and the coordinates of N visual features, with computational complexity reduced to O(N) by decomposing the estimation algorithm. Moreover, the O(N) complexity is maintained even when all N features are observed simultaneously for an arbitrary time interval. Through applying the special procedure for expanding the original state vector, based on the principal component analysis, the estimates of the block filter are made close to those of the Extended Kalman Filter (EKF), which, as shown previously, provides high estimation accuracy when the error models are consistent. A comparison with the EKF in terms of computational time and produced estimates is carried out by simulation of a visual aided INS. The obtained results show that negligible deviations from the EKF estimates are obtained when the dimensionality of the expansion of the original state vector has only a minor effect on the computational burden. The ability of processing hundreds of features in real time in single-threaded mode is also demonstrated.
In the paper, we simulated the positioning of receivers of a bottom linear sonar array. The conditions for achieving the maximum positioning RMS error of 1 m are determined by the simulation. Simulation results have been experimentally confirmed.
The accuracy of user position primarily depends on the behavior of on-board clock. Any anomaly in clock behavior would lead to inaccuracy in user position. Hence, the behavior 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 and frequency jumps. Following the detection of anomaly, the methodology would instantly initiate the process of estimating and updating the clock correction parameters which would be subsequently broadcasted to the user.
The article proposes an algorithm for determining the coordinates and motion parameters of an underwater target during bistatic sonar detection, which does not use any data on the spatial orientation of the receiving antenna freely rotating about a vertical axis. The novelty of the algorithm is that the bearing and distance to the target are determined using the angle between the directions of the projector’s direct signal and the same signal reflected from the target, measured by the receiving antenna. In addition, the time difference of the direct and reflected signals arrival is used. For the algorithm application, 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 considering various factors has been confirmed.
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 ex-tended 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 collision-free global path, the dynamic window approach (DWA) for reactive obstacle avoidance, and model predictive control (MPC) for refining the DWA commands and at the same time satisfying non-holonomic and actuator constraints. The different layers function in cascade (A* at a low frequency, DWA in real time, and MPC continuously refining the 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 real-time 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
The paper considers the extended Rao−Blackwellization method (ERB) and the ERB-based suboptimal algorithm designed to solve the problem of estimating a time-varying state vector described by a linear time-invariant equation using nonlinear measurements within the Bayesian approach. The ERB features and the main stages of designing the proposed algorithm are explained by the example of solving a problem of estimating a Markov process frequently used in processing of navigation information, generated by multiple integration of an input signal in the form of white noise. The efficiency and advantages of the proposed algorithm in comparison with the conventional sequential Monte Carlo-based algorithm are illustrated by solving the map-aided navigation problem in its simplest formulation. Prospects for further studies, in particular, using ERB in solving applied problems of navigation and trajectory information processing are discussed.
The paper proposes a cost-effective method of navigation support for a group of autonomous underwater vehicles (AUV) which do not have expensive precision timing systems and absolute speed sensors. The approach is based on the use of a short-baseline acoustic positioning system (SBL APS). A method has been developed for AUV positioning with the coordinates being estimated according to their motion model. A short-baseline version of the APS is presented, in which the received signal processing unit for generating the time delay measurements on the AUV additionally measures the received signal frequency. A location estimation algorithm based on a particle filter has been developed for this purpose; it is designed to work on each AUV in real time. The descriptions of the proposed algorithms and the results of computer simulation of their operation are presented.
Fiber optic gyroscopes are one of the main categories of optical gyroscopes, finding wider applications in inertial sensing and navigation through the measurement of angular velocity. Over the past few decades, the research on fiber optic gyroscopes competes with the state-of-the-art technologies in every aspect of its design based on different applications. Though the concept of optical gyroscopes started to unfold a century back, research on this is blooming to find the alternative strategy in structure for one another, with a range of classified designs and performance improvement techniques to work on. Among the types of fiber optic gyroscopes, the interferometric type finds place in most of the navigation applications in land, military, avionics and marine. The design perspectives include the choice of source, the fiber coiling pattern, the phase modulators, the signal processing techniques and, most prominently, the integrated optics at different levels. This paper presents an exhaustive review on the fiber optic gyroscopes design and development techniques.
Calibration of a strapdown inertial navigation system (SINS) on a simple turntable is considered. SINS calibration is carried out in the autonomous mode, relying only on the SINS sensors. One of the well-known algorithms for calibration in this scenario is the algorithm based on the extended Kalman filter proposed by N.A. Parusnikov. The algorithm is accurate enough, so that under certain assumptions, it is close to optimal. Some difficulties in its application are due to linearization of the problem, which requires the knowledge of initial approximation of the parameters to be calibrated. As an alternative, an algorithm based on the Fourier transform and subsequent transition to data spectrum analysis is proposed, after which the calibration algorithm becomes purely algebraic and does not involve any convergence problems. The accuracy of the proposed algorithm, as well as its nonoptimality are discussed through the comparison with the theoretical Cramer Rao bound.
A comparative analysis has been performed for the algorithms correcting the navigation system data of an autonomous underwater vehicle (AUV) using the ranges to acoustic beacons in the case of their unfavorable location relative to the AUV. The paper studies a recursive iterative Kalman filter, an iterative batch linearized smoother, an algorithm based on their combined use, and an algorithm based on factor graph optimization methods.
The paper discusses the data fusion of SINS accelerometers and gyroscopes and an odometer. The solution is based on three-dimensional (3D) inertial and 3D kinematic odometer dead-reckoning and continuous SINS updates by the computed 3D odometer coordinates. Important factors for the integration accuracy are highlighted: misalignments between the SINS instrument frame and the body frame; linear displacements of the SINS center relative to the odometer reference point; odometer scale factor error, and possible timing skews between raw SINS and odometer data. These parameters are included in the estimated variables in the data fusion algorithms. We demonstrate the necessity and efficiency of the proposed algorithmic solutions with experimental data analysis.
Development in the field of automated astrogeodetic optoelectronic systems started in the late 1980s and continues successfully to this day due to the progress in optoelectronic equipment and acquisition of new technologies based on the use of CCD and CMOS image sensors. The article traces the development of astrogeodetic systems, discusses their design and operation principles, composition and main performance characteristics.
Active development of mass-produced devices with dual-frequency microcircuits capable of processing the code and phase signals from global navigation satellite systems (GNSS) opens up new opportunities for high-precision positioning in geodesy. However, the use of smartphones for such tasks is limited by the lack of data on the position of their antenna phase centers. This paper presents the results of an experimental study to determine the location of the average phase center of the GNSS antenna for a Huawei P40 Pro smartphone at the reference point and to evaluate its effect on positioning accuracy using the Precise Point Positioning (PPP) method. The study showed that the antenna’s average phase center is displaced relative to the geometric center of the device by 2.7 cm towards the left edge of the screen, by 1.3 cm deep into the body (from the screen towards the back panel), and by 5.8 cm down from its upper edge. Based on these data, it is possible to correct the systematic positioning errors.
Currently, the field of underwater robotics is actively developing. The scope of tasks performed by autonomous underwater vehicles (AUVs) is expanding, and hence, the requirements for their autonomy are growing. These factors inevitably increase the time and cost of designing control and navigation systems for AUVs; in this connection, mathematical simulation begins to play an increasingly important role. This paper proposes a method for designing a control system for AUVs based on reduced-order models formed as a result of numerical simulation. The novelty of the work is that the AUV motion dynamics is represented as modified nonlinear transfer functions with nonlinear time-varying parameters, which are supposed to be determined from the results of numerical simulation. The proposed approach makes it possible to decompose the problem of control algorithm design, reducing it to an optimization problem, taking into consideration the cross-effect of the control loops. This can cause difficulties in the case that traditional analytical models are used. The implementation of the proposed approach is described on the example of designing an algorithm for AUV control in the vertical plane when it moves at a specified distance from the seabed. The effectiveness of the method is con-firmed in the course of similar mathematical experiments conducted on numerical models.
The paper considers the vehicle’s velocity determination using raw carrier phase measurements of GNSS receiver in autonomous mode. This problem is topical for airborne gravimetry [1], because GNSS-derived navigation solutions are essential for it. The idea of the solution is based on differentiating the single differences of carrier phase measurements. The two-step LSM procedure is described. Results from processing the experimental data are analyzed. The paper continues and extends publications [2, 3].
The global characteristics of the Earth’s gravitational field are refined to the fifth approximation of Molodensky’s theory. The computations were based on analytic continuation of free-air gravity anomalies from the Earth’s physical surface to the reference spherical surface passing through the calculated point, using a Taylor series. Schematic maps of digital global models of gravity anomaly vertical gradients to the fifth order are presented, and global correction terms are obtained for quasigeoid heights and deflections of the vertical (DOV) for the first to fifth approximations of Molodensky’s theory. It is shown that the gradient solution helps refine the global characteristics of the Earth’s gravitational field. When refining quasigeoid heights using the Stokes’ formula, the second approximation of Molodensky’s theory is sufficient for plain areas, while the fourth one is sufficient for mountainous areas. When refining the DOV components in meridian and the first vertical planes, the standard error increases from the second approximation.
Accurate attitude determination of unmanned aerial vehicles (UAVs) is crucial for autonomous navigation, particularly when relying solely on gyroscope, accelerometer, and magnetometer measurements without utilizing the Global Positioning System (GPS). Reinforcement learning (RL) has emerged as a promising artificial intelligence technique applicable across various domains. This research introduces a novel approach that leverages RL to enhance the performance of the extended Kalman filter (EKF) in attitude estimation. The proposed method depends of RL which uses the Q-learning model and policy to find best solution to adjust autonomously the measurement noise covariance matrix within the EKF. By establishing a reward mechanism that incentivizes actions minimizing the prediction error relative to true measurements, the RL dynamically optimizes the measurement noise covariance matrix. This innovative integration of RL and EKF, referred to as RL-EKF, has been implemented and tested. Results demonstrate that RL-EKF significantly outperforms the traditional EKF, yielding marked improvements in attitude estimation accuracy. The improvement ratios showed that selected method is very effective in the field of attitude estimation.
The scale factor stability of a fiber-optic gyroscope (FOG) directly depends on the stability of the central wavelength of its optical source. Although broadband highly stable light sources used in high-precision FOGs provide excellent wavelength stability, they are typically bulky, which complicates the miniaturization of FOGs and FOG-based systems. This work investigates the use of a semiconductor laser diode with pulse-frequency current modulation in a navigation-grade FOG. It is shown that this approach provides superior central-wavelength stability (better than 1.6 ppm), resulting in a FOG angle random walk and bias instability of 0.002°/√h and 0.009°/h, respectively. Comparable performance can be obtained in FOGs employing broadband highly stable light sources.
