Collaborative Navigation - Synchronizing and Aiding the Absolute Navigation of Swarm Members using Relative Inter-swarm Navigation Information

This paper is devoted to understanding relative navigation models that are used for space vehicles. The relative navigations models and approaches which are based on different systems (Inertial Navigation Systems (INS)&Global Navigation Satellite System (GNSS) , Laser&INS, Vision- Based, etc.) are compared. These models and approaches can be used individually or combined for solving relative navigation problems. Advantages and disadvantages of the models vary according to the usage area, platform type, and environment. Different methods and approaches exist in addition to different estimation and optimization algorithms for adaptation, control, and sensor fusion. Most of the models assume perfect attitude conditions. This study considers satellites' position estimates according to each other within formation on the Low Earth Orbit (LEO). Also, the aim of this article is to understand correlation between the relative navigation systems and the effectiveness of the algorithms which are used for estimating states during constellation or formation flight.

First responders and other emergency services personnel must often enter buildings which prevent the use of GPS or other satellite navigation signals for positioning. Loss of navigation capability combined with the fact that the buildings are often unknown to the personnel in question makes it more difficult for individual team members to coordinate with one another, and difficult or impossible for the team leader to monitor and direct the actions of each team member. While inertial navigation or pedestrian dead reckoning provide for some degree of navigation in GPS signal denied environments, these solutions degrade with time and may require prohibitively large and expensive inertial solutions to navigate over extended periods, while also allowing each individual user to accumulate independent positioning errors and thereby appearing to 'drift away' from one another. This paper presents an implementation of a collaborative navigation system utilizing each of user-to-user radio links, Global Navigation Satellite Systems (GNSS) when available, inertial navigation, pedestrian dead reckoning, as well as camera based Simultaneous Location and Mapping (SLAM) to provide a team of users with absolute and relative situational awareness for themselves and their team. The application of collaborative navigation to such a team provides the triple benefits of providing improved absolute navigation accuracy, improved relative navigation accuracy, and greatly enhanced situational awareness for all cooperating team members.

Accurate train positioning is crucial for ensuring the safety and operational efficiency of intelligent railway systems. Traditional Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration often performs reliably only under ideal conditions. However, in GNSS-denied environments such as tunnels, signal blockage can cause significant positioning errors. To overcome these limitations, this paper proposes a seamless train positioning framework that dynamically switches between Visual Place Recognition (VPR) /INS and GNSS/INS integration. The framework operates adaptively: in tunnel or signal-denied scenarios, where GNSS is unavailable, VPR/INS integration maintains accurate localization; in open-sky environments, GNSS/INS integration ensures continuous positioning. To enable VPR-based localization, a reference image database of the railway line is pre-constructed, and the NetVLAD model is employed to extract robust global image descriptors. A location-guided image retrieval strategy is then introduced to accelerate global feature matching, providing a coarse estimation of the train’s position based on retrieval results. To ensure the accuracy of the matching process, we have implemented a double-check mechanism that accepts matching results only when validated, thereby enhancing overall railway positioning safety. Field experiments conducted on the Lhasa–Nyingchi Railway show that during GNSS signal blockage, the proposed switching system effectively reduces positioning errors, achieving an overall RMS error of approximately 1.03m.

The Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime land and autonomous vehicles navigation information source. However, urban canyons high rise buildings block the GPS signal. Therefore, the Inertial Navigation System (INS) or the Reduced Inertial Sensor System (RISS) are utilized as an alternative source of the navigation information. The RISS is able to produce a full navigation solution using less number of sensors and calculations. Dramatically, the RISS solution drifts over time as the INS system. The integration between the GPS and the RISS is mitigating each system drawbacks. However, during the GPS outage periods, the navigation system is only depending on the inertial measurements. The RISS is utilizing the odometer/speedometer to measure the vehicle's forward speed. Many types of error affect the odometer measurements. These errors are either due to the vehicle's specifications as the differences in wheel diameters, and/or inefficient wheelbase or the road nature as wheel slips, uneven road surfaces, and/or skidding. In this paper, a Radar-based RISS system is introduced to take place the traditional RISS or INS systems. The radar unit is an essential part of the adaptive cruise control (ACC) system. Furthermore, it is capable of measuring the relative velocity and distance between the carrying and the in front vehicle to reduce collisions and increase the safety of driving. In addition, the radar measurements are not affected by the error sources that affect the odometer. The idea is to invest the ground reflection and derive the forward speed of the onboard vehicle as the ACC keep a safe distance between the cars. A novel method based on extracting the ground reflection features is introduced to obtain the onboard vehicle's speed. The obtained speed is utilized with two accelerometers and one vertical gyroscope to produce the Radar-based RISS system. The proposed system has been integrated with the GPS producing a Radar-based RISS/GPS integrated navigation system. The system has been tested on a real road trajectory in a downtown area and involved several GPS signal outages. The results show the significant capabilities of the proposed system in keeping the navigation solution drift to a minimum especially when the GPS signal is in outage compared with the traditional RISS/GPS system.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, ‘Collaborative Navigation’ uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

The primary relative navigation system of the PRISMA technology demonstration mission is based on GPS. The PRISMA satellites have been launched in June 2010 and, since their separation in August, have been successfully conducting a multitude of autonomous formation flying and on-orbit servicing experiments. The onboard navigation system relies on GPS measurements to provide in real-time accurate absolute and relative navigation. The output of the GPS-based navigation system is used to feed the numerous on-board feedback controllers and the safety monitoring and collision avoidance functions. As a consequence the navigation system has to provide the utmost accuracy to the experimental payloads and, at the same time, be robust and reliable to support fault detection isolation and recovery tasks. The commissioning of the GPS navigation system is therefore a crucial milestone for the mission. It has to be ensured that the onboard navigation solution is always reliable even in the presence of hardware anomalies and that the navigation performance satisfies the mission requirements. The performance assessment is done using precise orbit products generated by a ground facility for GPS-based precise orbit determination which needs, in turn, to be also commissioned. The paper summarizes the commissioning activities performed to validate both precise orbit products and onboard navigation and presents key flight results to illustrate the overall system performance.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

The starlight navigation system needs the horizontal attitude information provided by the inertial navigation system as its horizontal reference. When the zero position of the accelerometer changes in the undammed inertial navigation system in the critical condition, it is easy to cause the error oscillation and divergence, which directly affects the accuracy of the horizontal reference information of the auxiliary star sensor. Therefore, under the condition of ship-borne use, consider the combination of strapdown inertial navigation system, starlight navigation system and electronic log. The Kalman filter with velocity damping compensates the dynamic error of inertial navigation. The calibration and readjustment technology of the ship borne starlight/inertial combined system is based on the horizontal velocity damping, and the position error and misalignment angle are completed with the help of accurate reference information of starlight navigation.

A relative navigation system based on both the Inertial Navigation System (INS) and the Global Positioning System (GPS) is developed to support situational awareness during formation flight. The architecture of the system requires an INS/GPS integration across two aircraft via a data link. A fault-tolerant federated filter is used to estimate the relative INS errors based on relative GPS measurements and a range measurement obtained from the data link. The filter is constructed based on a reduced-order model of the relative INS error process. A method for analyzing the filter performance is presented. A case involving two helicopters in formation flight is studied under three different night trajectories to account for the effect of vehicle motion on the INS state transition matrix. The results of the covariance analysis are compared with actual night results over an instrumented test range.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

In this paper a multi-sensor data fusion technique is applied to aerosonde unmanned aerial vehicle (UAV) model to enhance inertial navigation system. An inertial measuring unit (IMU) error model is built with different error parameters (biasing, scale factor, and noise). Each IMU output is applied to strap down inertial navigation system (INS) algorithm to obtain the navigation information: position, velocity, and attitude (PVA). Multi-sensor data fusion algorithm based on fuzzy c-means clustering (FCM) is used to fuse the IMUs data. The fused output is applied to the INS algorithm to obtain the PVA. The simulation results show the effectiveness of the proposed method in reducing the error in navigation information PVA than using a single IMU.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.