Traditional Inertial Navigation Research Articles

Visual Heading-Aided Pedestrian Navigation Method Based on Factor Graph in Indoor Environment

The main error in traditional inertial pedestrian navigation is caused by unobservable heading errors with only zero velocity constraints. In this paper, a human posture model aided optimized fusion framework based on dominant events for pedestrian navigation is proposed to precisely locate pedestrians. Optimization is performed only at the moment of dominant events to reduce computational effort and improve the robustness. This model can fit the preferred heading information between two dominant events for fusion to improve accuracy. The optimization-based framework can also reduce the non-linear error by re-linearizing the state and associating constraints to the historical state to estimate the biases better. Besides, a hybrid visual heading angle estimation method is proposed by leveraging the Manhattan World hypothesis to obtain a drift-free heading angle with a monocular camera. The hypothesis is searched in the designated region based on histogram quality assessment that can improve the robustness and significantly reduce the computational amount. With low-cost devices, the multiple indoor experiments show that the proposed method can achieve 0.11% positioning accuracy comparable to traditional algorithms which require numerous conditions to be met in good environments. And achieving over 70% improvement in accuracy over traditional methods in complex environments.

Image Processing Method of a Visual Communication System Based on Convolutional Neural Network

Unmanned motion platforms are being used in a wide range of industries. Unmanned motion platforms must have an autonomous and intelligent navigation procedure in order to carry out their system functions. Traditional inertial navigation and radio navigation have poor accuracy and autonomy when not dependent on satellite circumstances. The accuracy of image recognition algorithms must meet strict standards. This study and exploration of the high-precision scene image recognition system is based on convolutional neural network structure optimization. To demonstrate the viability of the approach, simulation experiments are carried out on the NUC dataset using the recognition technique based on a convolutional neural network that is proposed. The fundamental network architecture of a convolutional neural network is optimized using the L2 regularization technique. The experimental findings demonstrate that the NUC dataset now has better recognition accuracy. In terms of recognition accuracy, the suggested method satisfies the predetermined requirements.

Research on Inertial Navigation Technology of Unmanned Aerial Vehicles with Integrated Reinforcement Learning Algorithm

With the continuous expansion of unmanned aerial vehicle (UAV) applications, traditional inertial navigation technology exhibits significant limitations in complex environments. In this study, we integrate improved reinforcement learning (RL) algorithms to enhance existing unmanned aerial vehicle inertial navigation technology and introduce a modulated mechanism (MM) for adjusting the state of the intelligent agent in an innovative manner [1,2]. Through interaction with the environment, the intelligent machine can learn more effective navigation strategies [3]. The ultimate goal is to provide a foundation for autonomous navigation of unmanned aerial vehicles during flight and improve navigation accuracy and robustness. We first define appropriate state representation and action space, and then design an adjustment mechanism based on the actions selected by the intelligent agent. The adjustment mechanism outputs the next state and reward value of the agent. Additionally, the adjustment mechanism calculates the error between the adjusted state and the unadjusted state. Furthermore, the intelligent agent stores the acquired experience samples containing states and reward values in a buffer and replays the experiences during each iteration to learn the dynamic characteristics of the environment. We name the improved algorithm as the DQM algorithm. Experimental results demonstrate that the intelligent agent using our proposed algorithm effectively reduces the accumulated errors of inertial navigation in dynamic environments. Although our research provides a basis for achieving autonomous navigation of unmanned aerial vehicles, there is still room for significant optimization. Further research can include testing unmanned aerial vehicles in simulated environments, testing unmanned aerial vehicles in realworld environments, optimizing the design of reward functions, improving the algorithm workflow to enhance convergence speed and performance, and enhancing the algorithm's generalization ability. It has been proven that by integrating reinforcement learning algorithms, unmanned aerial vehicles can achieve autonomous navigation, thereby improving navigation accuracy and robustness in dynamic and changing environments [4]. Therefore, this research plays an important role in promoting the development and application of unmanned aerial vehicle technology.

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model.

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

Shearer-Positioning Method Based on Non-Holonomic Constraints

In the traditional shearer-positioning method, an odometer is used to assist the forward velocity correction of the inertial navigation system, but it cannot restrain the system’s error divergence. For this reason, this paper proposes a shearer positioning method based on non-holonomic constraints. In this method, an inertial measurement unit and odometer are installed in the middle of the shearer body and on the traction gear, respectively, the shearer attitude, speed, and position information are calculated through the inertial measurement-unit mechanization, and the shearer’s instantaneous velocity is calculated through the output of the odometer. The mechanization and error transfer process of the inertial navigation system are used to establish a Kalman filtering-state equation. The Kalman filtering observation equation is established through the difference between the projected velocity of the inertial navigation system at the joint and the output velocity of the odometer as the observation vector, and the non-holonomic constraint is introduced. Finally, the error feedback is derived from the results processed by the Kalman filtering algorithm, and the output of the inertial navigation system is corrected to obtain the optimal estimation of the shearer’s attitude, speed and position. The experiment shows that compared with the traditional inertial navigation and odometer combined positioning method, the degree of divergence in the positioning results over time is significantly reduced after adding the non-holonomic constraint. The positioning method has good tracking ability for the trajectory of the shearer. The error of the positioning results in the forward direction is reduced by 66%, the lateral direction is reduced by 62%, and the vertical direction is reduced by 67%.

INS/CNS/DNS/XNAV deep integrated navigation in a highly dynamic environment

PurposeThis study aims to address the problem of the divergence of traditional inertial navigation system (INS)/celestial navigation system (CNS)-integrated navigation for ballistic missiles. The authors introduce Doppler navigation system (DNS) and X-ray pulsar navigation (XNAV) to the traditional INS/CNS-integrated navigation system and then propose an INS/CNS/DNS/XNAV deep integrated navigation system.Design/methodology/approachDNS and XNAV can provide velocity and position information, respectively. In addition to providing velocity information directly, DNS suppresses the impact of the Doppler effect on pulsar time of arrival (TOA). A pulsar TOA with drift bias is observed during the short navigation process. To solve this problem, the pulsar TOA drift bias model is established. And the parameters of the navigation filter are optimised based on this model.FindingsThe experimental results show that the INS/CNS/DNS/XNAV deep integrated navigation can suppress the drift of the accelerometer to a certain extent to improve the precision of position and velocity determination. In addition, this integrated navigation method can reduce the required accuracy of inertial navigation, thereby reducing the cost of missile manufacturing and realising low-cost and high-precision navigation.Originality/valueThe velocity information provided by the DNS can suppress the pulsar TOA drift, thereby improving the positioning accuracy of the XNAV. This reflects the “deep” integration of these two navigation methods.

SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

AbstractTo achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

Kamera termowizyjna wspomagająca nawigację pojazdów UAV

The topic of this paper is an evaluation of developed sensor intended for navigation aid of unmanned aerial vehicles (UAVs). Its operation is based on processing images acquired with a thermal camera operating in the long-wave infrared band (LWIR) placed underneath a vehicle’s chassis. The vehicle’s spatial displacement is determined by analyzing movement of characteristic thermal radiation points (ground, forest, buildings, etc.) in pictures acquired by the thermal camera. Magnitude and direction of displacement is obtained by processing the stream of consecutive pictures with optical-flow based algorithm in real time. Radiation distribution analysis allows to calculate camera’s self-translation vector. Advantages of measuring translation based on thermal image analysis is lack of drift effect, resistance to magnetic field variations, low susceptibility to electromagnetic interference and change in weather conditions as compared to traditional inertial navigation sensors. As opposed to visible light situational awareness sensors, it offers operation in complete darkness (harsh weather, nights and indoors).The topic of this paper is an evaluation of developed sensor intended for navigation aid of unmanned aerial vehicles (UAVs). Its operation is based on processing images acquired from a thermal camera operating in the long wave infrared band (LWIR) placed underneath a vehicle’s chassis. The vehicle’s spatial displacement is determined by analyzing movement of characteristic thermal radiation points (ground, forest, buildings, etc.) in pictures acquired by the thermal camera. Magnitude and direction of displacement is obtained by processing the stream of consecutive pictures with optical-flow based algorithm in real time. Radiation distribution analysis allows to calculate camera’s self-translation vector. Advantages of measuring translation based on thermal image analysis is lack of drift effect, resistance to magnetic field variations, low susceptibility to electromagnetic interference and change in weather conditions as compared to traditional inertial navigation sensors. As opposed to visible light situational awareness sensors, it offers operation in complete darkness (harsh weather, nights and indoors).

Navigator Notes

Navigator Notes

Sensitivity Analysis of Precision Inertial Sensor‐based Navigation System (SAPIENS)

The future of deep space exploration depends upon technological advancement towards improving spacecraft's autonomy and versatility. This study aims to examine the feasibility of autonomous orbit determination using advanced accelerometer measurements. The objective of this research is to ascertain specific sensor requirements to meet pre-defined mission navigation error budgets. Traditional inertial navigation (dead reckoning and external aiding) is not considered. Instead, measurements from pairs of advanced, highly sensitive accelerometers (e.g., cold atom accelerometers) are used onboard to determine gravity field gradients, which are then correlated to onboard gravity maps and used to determine orbital information. Linear Covariance Theory helps to efficiently conduct an error budget analysis of the system. This error budget analysis helps to determine the effect of specific error sources in the sensor measurements, thereby providing information to rank and compare relevant sensor parameters and determine an optimal sensor configuration for a given space mission. The procedure is repeated to evaluate different accelerometer configurations and sensor parameters.

Inertial Navigation Method of Spacecraft Based on Geodesic Deviation Equation

A new method of spacecraft inertial navigation is proposed in paper. The method uses the earth gravitational field model and the on-board accelerometer to measure data, so the real-time deviation of the spacecraft relative to the geodetic orbit is calculated. Based on the post-Newtonian approximate gravity theory of general relativity, the high precision geodesic deviation equation around the earth is derived, and the equation of the spacecraft relative to the geodesic orbit is transformed into orbital element space. The position of the spacecraft is determined by calculating the deviation between the spacecraft and the nearest geodetic orbit. Because the reference points are selected different in the adjacent following inertia system during navigation, the accumulation error of the traditional inertial navigation is avoided, and the navigation precision can be effectively improved.

Novel Drift Reduction Methods in Foot-Mounted PDR System.

The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations ( $>$ 1 h), where neither frequent surfacing nor consistent bottom tracking is available. We instantiate this concept through mid-depth underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller scale turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved small-scale flow perturbations are present. For a 6-h navigation with an automotive-grade inertial navigation system, the current-aided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent double-gyre flow field, and under 7.3% UDT in a turbulent meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-h mission, positioning result with under 25% UDT for a 24-h navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

Pedestrian Dead Reckoning With Smartphone Mode Recognition

Smartphone mode recognition is becoming a key aspect of many applications, such as daily life monitoring, health care, and indoor positioning. In the later, two approaches exist to perform positioning using smartphone inertial sensors: traditional inertial navigation algorithms and pedestrian dead reckoning (PDR). Usually, PDR is preferred since it requires less integrations on the noisy sensory data. Step length estimation is a critical stage in PDR. It requires a calibration phase to determine appropriate gains, prior to PDR application. Using an incorrect gain will result in a position error. Such gains are very sensitive to different smartphone modes, such as talking, texting, swing, or pocket. Therefore, each smartphone mode requires a different gain. In this paper, the effect of the smartphone mode on PDR positioning accuracy is highlighted. To circumvent this error source, we employ machine learning classification algorithms to recognize the smartphone modes and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. To that end, a methodology of training on a single user and testing on multiple users, as well as unique features for the classification process, is implemented. Experimental results obtained using 13 participates walking in different indoor conditions and smartphone modes, show successes of more than 95% in classifying the smartphone modes and as a consequence may improve PDR positioning performance.

Improved Transversal Polar Navigation Mechanism for Strapdown INS using Ellipsoidal Earth Model

As the geographical meridians converge rapidly, traditional inertial navigation methods fail in the polar regions. Classic transversal navigation methods can address the problem by transversal rotation of the original north and south poles, but this can introduce errors based on the spherical Earth model. To reduce the principle errors, some fruitful research work using an ellipsoidal Earth model has been done. Under the ellipsoid Earth model, transversal navigation for polar region becomes a complex coupling problem. Considering the coupling of the three-dimensional motion, a more rigorous mechanism for transversal navigation using an ellipsoidal Earth model is proposed. Starting from the relationship between Euclidean coordinates and spherical coordinates, the main equations of transversal polar navigation based on an ellipsoidal Earth model are derived in detail. Complete mechanical arrangements of attitude, position and velocity calculation are presented. The new derivation in this paper completely avoids solving the ellipsoidal radius. Numerical results indicate that the proposed transversal navigation mechanism can outperform the traditional method, especially in the condition of vertical motion.

Integrated Navigation, Guidance and Control System and Validation

The navigation, guidance and control (NGC) system of Reusable Launch Vehicle – Technology Demonstrator (RLV-TD) is the most complex system ever flown in ISRO’s launches. It includes a navigation system employing traditional inertial navigation system along with global positioning system aiding and radar altimeter, ceramic servo accelerometer package, angle of attack-based control, different control systems, including secondary injection thrust vector control, reaction control system and aero control surfaces. Closed loop simulations of integrated NGC system of RLV-TD are of utmost importance to ensure mission success by assessment of system performance in nominal and off-nominal flight conditions. Validation is achieved through progressive evaluations in different levels of integrated simulation, namely autonomous simulations, software in loop simulation, on-board processor in loop simulation, hardware in loop simulation and actuator in loop simulation using Iron Bird test facility. This article discusses the overall NGC system, challenges faced during the realization of the simulation test bed, including system design to validate the new on-board elements, commissioning of Iron Bird facility and validation through various phases of simulation. The outcome of these simulations played a crucial role in bringing about improvements and helping in the evolution of the on-board system to the final configuration, ensuring the success of the mission.

Sensor to sensor calibration of the integrated INS/vision navigation system

Due to the essential limitation of optical sensors, its integration with traditional Inertial Navigation System (INS) tends to be the focus in indoor navigation applications. In a low-cost INS/vision integrated navigation system, the relative position and gestures between slave systems are important coefficients. In order to solve the initial bias estimation problems involved in INS/vision integrated system, this article proposes a novel alignment approach based on the time-domain constraints. At first, on the basis of traditional initial alignment model, the time-domain constrained model is deduced, in which the time-related states and measurements are all modeled. In order to verify the advantage of state evaluability, both the traditional alignment model and the corresponding time-domain constrained model are analyzed via the nonlinear observability analysis method. At the end of this article, two groups of numerical simulations are implemented, and the corresponding results validate the effectiveness ...

Two-mode navigation method for low-cost inertial measurement unit-based indoor pedestrian navigation

The traditional inertial navigation system-based indoor pedestrian navigation method is not suitable to all the conditions, since its data fusion model is designed only for a single condition. In order to achieve better performance, a two-mode navigation method for indoor pedestrian navigation is proposed in this work, which includes the stance mode and the swing mode. When the person’s foot is in a stance phase, the stance mode is used to correct the velocity error and yaw error. When the person’s foot is in a swing phase, the swing mode works and only the yaw error is able to be corrected. For verification, a real indoor test has been done to assess the performance of the proposed two-mode method. The position root-mean-square error (RMSE) value is 1.9265 m during a 176-m traverse, and the RMSE of yaw is 0.20734 rad. These results demonstrate that our method is effective to reduce the error compared with the conventional schemes.

Underwater terrain-aided navigation based on multibeam bathymetric sonar images

Underwater terrain-aided navigation is used to complement traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map. This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge corner pixels are then defined and used to construct an edge corner histogram, which it employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.

Transverse Navigation under the Ellipsoidal Earth Model and its Performance in both Polar and Non-polar areas

The transverse navigation system has been designed and developed to solve the challenges of navigation in polar regions. However, considerable theoretical errors are introduced into the system when the spherical Earth model is adopted. To tackle this problem, a transverse navigation mechanism under the ellipsoidal Earth model has been proposed in this research and the application regions of the proposed algorithm are specified and evaluated through error analysis. The analysis shows the presented transverse navigation system works in both polar and part of the non-polar regions. Field tests were conducted to evaluate the navigation performance in Nanjing, a non-polar region. A novel experimental method, where the field test data in mid-latitude areas was used to simulate the real Inertial Measurement Unit (IMU) data and the reference information in polar regions, was adopted to investigate the performance of the proposed algorithm in polar areas. The results show: that in the mid-latitude areas, the presented transverse navigation system achieves the same accuracy as the traditional inertial navigation system and that in polar regions, the proposed transverse mechanism outperforms the traditional method with a much lower error in longitude and yaw.