Integrated Navigation Method Research Articles

Comparison of Nonlinear Filtering Techniques for Inertial Sensors Error Identification in INS/GPS Integration

Nonlinear filtering techniques are used to fuse the global positioning system (GPS) and the inertial navigation system (INS) together to provide a robust and reliable navigation system with a performance superior to that of either INS and GPS alone. Prominent nonlinear estimators in this field are the Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF and the PF based on EKF and UKF in INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation and bias/scale factor estimation are investigated by using these four filtering algorithms. Both ground-vehicle experimental test and flight simulation test have been utilized to evaluate the filters performance

Multi-Sensor, Adjustable-Period Integrated Navigation Method Based on Multi-Stage Signal Trigger for Underwater Vehicles

For underwater vehicle navigation sensors, the output signal periods are different and time-varying. This would result in the decline of precision, and even wrong results. To deal with the problem, this paper puts forward a multi-sensor, adjustable-period integrated navigation method based on multi-stage signal trigger. This method considers the valid signals of a Doppler Velocity Log (DVL) as the trigger signals of a Dead-Reckoning (DR) navigation program. It also considers the valid signals of an acoustic positioning sensor as the trigger signals of the integrated navigation program. In this method, it can adjust the filtering period in real time. According to the time label of signals, this method actualises the time-space alignment of sensors. Then it conducts DR navigation and integrated navigation. The method can not only utilise the valid signals of each sensor sufficiently but also fuses the data based on time-space alignment efficiently. Sea trial data shows that when the output signal periods are certain, navigation precision of the method in this paper is better than a non-adjustable-period filtering method. Moreover, in poor conditions, it can also attain a high precision.

Drag derived altitude aided navigation method

The navigation problem of the lifting reentry vehicles has attracted much research interest in the past decade. This paper researches the navigation in the blackout zone during the reentry phase of the aircraft, when the communication signals are attenuated and even interrupted by the blackout zone. However, when calculating altitude, a pure classic inertial navigation algorithm appears imprecise and divergent. In order to obtain a more precise aircraft altitude, this paper applies an integrated navigation method based on inertial navigation algorithms, which uses drag derived altitude to aid the inertial navigation during the blackout zone. This method can overcome the shortcomings of the inertial navigation system and improve the navigation accuracy. To further improve the navigation accuracy, the applicable condition and the main error factors, such as the atmospheric coefficient error and drag coefficient error are analyzed in detail. Then the damping circuit design of the navigation control system and the damping coefficients determination is introduced. The feasibility of the method is verified by the typical reentry trajectory simulation, and the influence of the iterative times on the accuracy is analyzed. Simulation results show that iterative three times achieves the best effect.

A novel SINS/CNS Integrated Navigation Method Using Model Constraints for Ballistic Vehicle Applications

The major challenge of current Strapdown Inertial Naviagtion System/Celestial Navigation System (SINS/CNS) integrated systems is navigation accuracy degradation due to the failure to accurately estimate the accelerometer bias even when using stellar refraction information (e.g. the number of refraction stars is less than three). To solve this problem, this paper presents a new method for improving the accuracy of the traditional inertial-based integrated systems installed on ballistic vehicles. In an analogy with nonholonomic constraints in land navigation, this algorithm exploits the constraints that govern the motion of a ballistic vehicle in the free flight phase to obtain accelerometer bias observations. Improvements in dynamic equations are used to reduce the propagation of navigation errors, and high-rate virtual constraints are used to reduce the impact of bias errors. An information filter is devised to fuse the multi-rate observations from multiple sources, i.e. SINS, CNS and model constraints. The proposed method is also evaluated by long-range ballistic vehicle navigation simulations. The results indicate that the proposed constrained algorithm can address the degradation problem with remarkable accuracy improvements without adding extra sensors, enhancing the SINS-based navigation performance for ballistic applications.

Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method

In this paper, a low-cost, high-performance ultra-tightly coupled GPS/INS integrated navigation method is proposed. The measurement of the integration Kalman filter is generated from a moving average filter in the proposed method. A moving average filter is inserted between the signal tracking error estimator and the integration Kalman filter to avoid other measurement noise reduction methods’ limitations and the error of the MEMS inertial sensor is modeled as bias repeatability and bias stability in the proposed method. A new integration Kalman filter is derived to compensate the time delay caused by the moving average filter. The proposed method improves signal tracking performance of the GPS receiver and resolves the problem caused by the difference between correlator output rate and integration Kalman filter update rate. In addition to these, the proposed method resolves the performance degradation problem of the MEMS inertial sensors. In order to verify the proposed method, an experimental platform is constructed, which consists of a GPS/IMU signal generator, a GPS SDR S/W module and a GPS/INS S/W module. The results in weak signal environment show that the proposed method gives better navigation performance than other methods without the moving average filter and/or the model of bias stability of MEMS IMU.

Ballistic missile SINS/CNS integrated navigation method

Ballistic missile SINS/CNS integrated navigation method

X-ray Pulsar/Starlight Doppler Deeply-integrated Navigation Method

An X-ray pulsar/starlight Doppler deeply-integrated navigation method is proposed in this paper. A starlight Doppler measurement-aided phase propagation model, which can remove the orbital effect within the recorded photon Time Of Arrivals (TOAs), is derived, and guarantees that the pulse phase can be extracted from the converted photon TOAs using computationally efficient methods. Some simulations are performed by a hardware-in-loop system to verify the performance of the integrated pulse phase estimation method as well as of the integrated navigation method. The integrated pulse phase estimation method could achieve an estimation performance similar to the existing method for orbiting vehicles at the cost of much less computational complexity, is capable of handling the signals of millisecond pulsars, and is applicable to various vehicles. In addition, the proposed integrated navigation method could provide reliable positioning results for various vehicles.

An autonomous underwater vehicle positioning matching method based on iterative closest contour point algorithm and affine transformation

An improved autonomous underwater vehicle federal integrated navigation method based on two-stage position matching technology is proposed in this study to solve the problem of positioning divergence caused by the defects of error accumulation in an inertial navigation system. The improved two-stage position matching method includes the acquisition and preprocessing of the strap-down inertial navigation system and indicates track sequences and elevation measurement sequences. The first-stage position matching is based on iterative closest contour point, while the second-stage position matching is based on an affine algorithm. Simulation results indicate that the proposed algorithm solves the problem of the limitation of the rigid transformation of traditional iterative closest contour point and effectively resolves the matching failure caused by the large initial position error of long-time navigation.

Augmented Cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise

Abstract In order to enhance the capability of autonomous operation for small unmanned aerial vehicles (UAV), a MEMS-based inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation method is proposed. An augmented Cubature Kalman filter is derived to fulfil the data fusion of precise GNSS real-time kinematic (RTK) solution and noisy inertial measurements. In the filter, Cubature Kalman filtering is adopted to handle the strong INS model nonlinearity caused by sudden and large UAV maneuvers, and the technique of state-augmentation is used to capture meaningful odd-order moment information and reduce the adverse impacts of non-additive noise in inertial measurements. It is analyzed that the basic difference between the augmented and non-augmented CKFs generally favors the augmented CKF, which is supported by a representative example and flight test. The results of flight test have also shown that the proposed augmented Cubature Kalman filtering method can complete more accurate navigation compared with the conventional EKF/UKF-based approaches.

A dimension reduced INS/VNS integrated navigation method for planetary rovers

Inertial navigation system/visual navigation system (INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors (position and attitude) of planetary rovers, the performance of the Kalman filter (KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter (EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.

Vision/INS Integrated Navigation System for Poor Vision Navigation Environments.

In order to improve the performance of an inertial navigation system, many aiding sensors can be used. Among these aiding sensors, a vision sensor is of particular note due to its benefits in terms of weight, cost, and power consumption. This paper proposes an inertial and vision integrated navigation method for poor vision navigation environments. The proposed method uses focal plane measurements of landmarks in order to provide position, velocity and attitude outputs even when the number of landmarks on the focal plane is not enough for navigation. In order to verify the proposed method, computer simulations and van tests are carried out. The results show that the proposed method gives accurate and reliable position, velocity and attitude outputs when the number of landmarks is insufficient.

Performance enhancement of X-ray pulsar navigation using autonomous optical sensor

This paper develops an integrated navigation method based on the X-ray pulsar navigation (XNAV) system and an autonomous optical navigation system for spacecrafts. The X-ray pulsar navigation is implemented by using the difference between the measured and predicated pulse arrival time, which is calculated by comparing an observed pulse profile with a standard pulse profile. A problem arises from the X-ray signal processing in that the spacecraft’s orbit information, which may be unknown, is required to construct the observed pulse profile. The effect of the spacecraft orbit error on the accuracy of the pulse TOA (time of arrival) difference determination is analyzed. It is specified that the performance of the XNAV system may be degraded in the presence of large orbit error. In order to improve the navigation accuracy, an integrated navigation scheme is presented by fusing the measurement information of a X-ray detector and an ultraviolet optical sensor. The XNAV/optical integrated navigation system is effective to mitigate the effect of the spacecraft orbit error. The superiority of the presented scheme is illustrated through numerical simulations.

Autonomous Navigation of Mars Probes by Single X-ray Pulsar Measurement and Optical Data of Viewing Martian Moons

In order to achieve high accuracy of autonomous navigation for Mars probes, an integrated navigation method using X-ray pulsar measurement and optical data of viewing Martian moons is proposed. For single X-ray pulsar measurement on board a Mars probe, navigation accuracy is low due to its poor observability. On the other hand, Phobos and Deimos, two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, the Martian moons ephemeris bias and the differences between barycentre and centre of brightness of Martian moons will result in low navigation accuracy. The method of integrated navigation using X-ray pulsar measurement and optical data of viewing Martian moons can overcome the defect and achieve accurate navigation. Two sequential orbit determination algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared. The simulation results show this method can obtain high autonomous navigation accuracy during the phase of a probe orbiting Mars.

3D Multi-Copter Navigation and Mapping Using GPS, Inertial, and LiDAR

As the use of unmanned aerial vehicles has become more prevalent, the need for a reliable three-dimensional positioning and navigation capability is required to enable operation in challenging environments where the Global Positioning System (GPS) may not be available. For many of these environments, there may not be one particular method to solve the positioning navigation problem. Therefore, we have selected a set of dissimilar sensor technologies and implemented an integrated navigation method that can support reliable operation in an outdoor and structured indoor environment. The integrated navigation design is based on three types of sensors: a GPS receiver, an inertial measurement unit, and three laser scanners. This paper will show that decimeter-level relative positioning accuracies can be achieved for structured indoor operations and that when segments are included where GPS is available, the platform's trajectory is globally anchored with meter-level accuracy. A secondary goal of the proposed method is the generation of a three-dimensional map of the environment. Copyright © 2016 Institute of Navigation Copyright © 2016 Institute of Navigation

Integrated navigation method based on inertial navigation system and Lidar

An integrated navigation method based on the inertial navigational system (INS) and Lidar was proposed for land navigation. Compared with the traditional integrated navigational method and dead reckoning (DR) method, the influence of the inertial measurement unit (IMU) scale factor and misalignment was considered in the new method. First, the influence of the IMU scale factor and misalignment on navigation accuracy was analyzed. Based on the analysis, the integrated system error model of INS and Lidar was established, in which the IMU scale factor and misalignment error states were included. Then the observability of IMU error states was analyzed. According to the results of the observability analysis, the integrated system was optimized. Finally, numerical simulation and a vehicle test were carried out to validate the availability and utility of the proposed INS/Lidar integrated navigational method. Compared with the test result of a traditional integrated navigation method and DR method, the proposed integrated navigational method could result in a higher navigation precision. Consequently, the IMU scale factor and misalignment error were effectively compensated by the proposed method and the new integrated navigational method is valid.

A Radio/Optical Integrated Navigation Method Based on Ephemeris Correction for an Interplanetary Probe to approach a Target Planet

To obtain accurate navigation results with respect to Earth simultaneously with those with respect to the target for an interplanetary probe to approach the target planet, this paper proposes a Radio/Optical integrated navigation method based on ephemeris correction, which deeply affects the fusion accuracy. In this paper, the model of the ephemeris error is established, and taking the analytical solution of the ephemeris uncertainty as measurement, the target ephemeris error and its covariance are estimated by Kalman filter and fed back to modify the force models. By correcting the target ephemeris and using information fusion, the Radio/Optical integrated navigation prevents the ephemeris uncertainty polluting the fusion accuracy, and efficiently combines the radio and optical navigation results. The results show the influence of the ephemeris error can be removed, and the Radio/Optical integrated navigation is capable of providing accurate navigation results with respect to Earth and the target. The results demonstrate the proposed method yields an accuracy superior to the conventional method, which proves its effectiveness.

INS/VNS/CNS integrated navigation method for planetary rovers

Planetary rovers play an important role in deep space exploration missions. Inertial navigation system (INS), visual navigation system (VNS) and celestial navigation system (CNS) are three commonly used autonomous navigation systems for rovers. Although INS and VNS have high precision, their navigation errors especially the yaw angle error accumulate over time. CNS can make up for this shortage and provide accurate yaw angle information. Since INS, VNS and CNS have complementary characteristics, an integrated navigation system composed of them helps to improve the navigation accuracy and reliability. In this paper, an INS/VNS/CNS integrated navigation method for planetary rovers is presented. The INS error equation expressed in the navigation frame is used as state model. The relative motion error obtained by INS and VNS and the starlight vector provided by CNS are used as measurements. The unscented Kalman filter (UKF) is utilized to perform state estimation. Hardware-in-the-loop simulations show that the proposed method can remarkably improve the estimation accuracy of position and attitude compared with the INS, VNS, INS/VNS and INS/CNS navigation methods.

Integrated navigation method of a marine strapdown inertial navigation system using a star sensor

This paper presents an integrated navigation method of the strapdown inertial navigation system (SINS) using a star sensor. According to the principle of SINS, its own navigation information contains an error that increases with time. Hence, the inertial attitude matrix from the star sensor is introduced as the reference information to correct the SINS increases error. For the integrated navigation method, the vehicle’s attitude can be obtained in two ways: one is calculated from SINS; the other, which we have called star sensor attitude, is obtained as the product between the SINS position and the inertial attitude matrix from the star sensor. Therefore, the SINS position error is introduced in the star sensor attitude error. Based on the characteristics of star sensor attitude error and the mathematical derivation, the SINS navigation errors can be obtained by the coupling calculation between the SINS attitude and the star sensor attitude. Unlike several current techniques, the navigation process of this method is non-radiating and invulnerable to jamming. The effectiveness of this approach was demonstrated by simulation and experimental study. The results show that this integrated navigation method can estimate the attitude error and the position error of SINS. Therefore, the SINS navigation accuracy is improved.

Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network

For the surface ship integrated navigation system of INS/CNS, the Star Sensor may invalidity due to the cloud weather, which leads to the integrated system cannot work anymore. To resolve the problem, an INS/CNS integrated navigation method based on particle swarm optimization back propagation neural network (PSO BPNN) is proposed in this paper. During the effective Star Sensor navigation process, the INS positioning error can be obtained and used for training PSO BPNN; when the Star Sensor is in invalid state, the already trained BPNN is used for forecasting the INS positioning error. The effectiveness of this approach was demonstrates by simulation and experimental study. The results showed that the INS/CNS integrated navigation method based on PSO BPNN can effectively estimate and compensate the INS navigation error under the star senor invalid state.

An AUV Integrated Navigation Method Based on Multi-sensor Data Fusion

Autonomous Navigation Vehicle (AUV) navigates to the specified longitude and latitude on water surface by means of Global Positioning System (GPS), Inertial Navigation System (INS) and Electronic Compass (EC), then dives to the specified depth according to the measurements of Depth Gauge (DG). To overcome the low location accuracy of existing navigation technologies in AUV, an integrated navigation method based on multi-sensor data fusion of GPS/INS/EC/DG has been proposed. Since the systematic stability and high precision in short-term measurement of INS, it takes the INS as common reference system. The problem which measurement error accumulates with time is solved by the combination of four sub-filters and a main filter. The sub-filters output the measured velocity, position, orientation and depth error to the main filter, which fuse the data to obtain the global optimal estimation and feedback to the INS system. Realistic environment parameters are simulated in MATLAB to verify the rationality of the navigation algorithm. The feasibility of method is proved by the comparison of position errors with and without combined navigation.