A New Technique for INS/GNSS Attitude and Parameter Estimation Using Online Optimization

The US Army has a requirement for a highly accurate inertial/TACAN hybrid navigation system. This paper describes the navigation system architecture developed to meet this requirement, the specific hardware selected and the development status. At the beginning of the Integrated Inertial Navigation System (IINS) development program it was anticipated that significant technological advances would take place in its life cycle. In addition, mission requirements were expected to change as the system evolved. Particular attention was therefore given to the IINS system architecture, specifically the partitioning of hardware and software functions as well as the means of data transmittal between the subsystem elements. The paper describes in detail the design trade-offs made as well as hardware selections. The system is configured using several different manufacturers' equipment making the data interface and its configuration control of great importance. The above considerations lead to the conclusion that the appropriate data bus interface standard would be MIL-STD-1553. This interface standard not only facilitates configuration control during system development, but will allow rapid integration with future aircraft systems that will use it as a primary means of data transmittal. The paper describes in detail the signal interface between the subsystem element, onboard mission equipment and flight instruments. The most significant change anticipated in the course of the IINS life cycle is the substitution of Global Positioning System User Equipment for the external update function currently provided by TACAN. Another area where technology insertion may be deemed appropriate is the substitution of a strapdown inertial navigation unit (INU) employing ring laser gyros (RLG) for the currently employed conventional gimballed INU. The INU employed in the IINS is built to a form, fit and function specification, which will allow substitution of a RLG INU when such a unit is qualified and in production. The system hardware and software is structured so that these changes can be accomplished with a minimum of effort. The paper also describes the federated processing structure which allows this flexibility with particular attention to the inertial/TACAN update algorithm and data bus control functions. The paper concludes with a brief summary of the development status of the IINS, currently in full scale engineering development.

Advanced land‐vehicle navigation commonly uses integrated systems to counteract global navigation satellite system (GNSS) solution degradation. This occurs mainly in urban environments due to blockage of the satellite signals. This paper presents a loosely coupled inertial navigation system/GNSS navigation system that combines an attitude and heading reference system (AHRS) device with a dual‐frequency dual‐antenna GNSS heading receiver. The integrated navigation system is aided by a low‐cost odometer which replaces external wheel speed sensors usually installed in autonomous vehicles. The proposed odometer extracts the anti‐lock braking system‐generated pulses of rear wheels from vehicle controller area network messages. Following, it converts them into software‐generated signal pulses which are sent to the AHRS device through the serial port. The system platform uses a mobile internet data link to get differential GNSS corrections in real‐time from a public Ntrip—networked transport of Radio Technical Commission for Maritime Services via internet protocol—broadcaster in order to allow the GNSS receiver to operate in differential global positioning system/real‐time kinematic (RTK) modes. Thus, the integrated navigation system provides centimeter‐level positioning accuracy at 100 Hz. Since the positioning accuracy is severely affected by numerous factors, this work proposes a replicated 24 full factorial design with the purpose of evaluating the in‐field obtained positioning performance under different factors combinations. The experimental design chosen allows to know under which conditions it is feasible to replace the available GNSS velocity by the proposed odometry solution, when they are used as navigation aids, and knowing that the proposed odometry has a low resolution. The analysis of 32‐runs factorial design results, using a significance level of .05, demonstrated that the proposed odometry can overcome GNSS/RTK velocity.

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.

With the advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDV) is expected to replace the odometer to be combined with a strapdown inertial navigation system (SINS) to form a higher precision integrated navigation system. However, LDV scale factor error and misalignment angles between LDV and inertial measurement unit will affect the accuracy of navigation. Considering that not all global navigation satellite system (GNSS) receivers can directly provide velocity information and current mainstream calibration methods are sensitive to the measurement noise and outliers of velocity and position information, a robust calibration method aided by GNSS is proposed in this paper, which is based on position observation. Different from current popular calibration methods, the attitude information of the GNSS/SINS integrated navigation system obtained by an adaptive Kalman filter is used to construct the observation vector together with LDV velocity outputs and GNSS position outputs in this method. The LDV scale factor error and the misalignment angle are determined by the ratio of two observation vector modulus and the Davenport's q-method method, respectively. The accuracy and robustness of the calibration method are verified by one vehicle test with normal GNSS signals and one vehicle test with GNSS signals with outliers. And the horizontal position error of dead reckoning of the calibrated LDV/SINS integrated system are less than 0.0314% and 0.1033% of the mileage, respectively.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

In the integrated inertial navigation system (INS) and celestial navigation system (CNS), the attitudes and positions of probes are estimated based on the auxiliary measurement of an infrared Earth sensor. The poor accuracy of the infrared Earth sensor compared to that of the star sensor significantly limits the navigation accuracy of the integrated navigation system. This paper proposes a novel INS/CNS integrated navigation system that uses the specific force with noncumulative error to correct infrared Earth measurements. The probe azimuth angle and elevation angle relative to the Earth's inertial frame are first estimated according to the specific force provided by the INS to filter the infrared Earth sensor's measurement error. Then, the corrected infrared Earth measurement assists the CNS to obtain accurate celestial measurements. The integrated INS/CNS navigation system in the tightly coupled mode finally estimates the accurate probe attitude and position. Compared to the integrated navigation system, which directly uses the uncorrected infrared Earth measurement, the yaw, pitch, and roll angle are improved under the proposed method by 76.73%, 77.64%, and 75.39% on average, respectively; the latitude and longitude estimation errors improve under the proposed method by 70.44% and 61.92% on average, respectively. In short, the proposed method is a feasible and highly accurate approach to the INS/CNS integrated navigation system.

With the rapid development of high-speed trains in the world, how to quickly collect train operation data becomes a significant issue. The paper uses Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and Driver Machine Interface (DMI) information fusion to obtain the position and speed information of the train more accurately. Among these information sources, GNSS and INS can carry out the collection of train position and speed information with and without obstructions, respectively. When the train is running in the open and unobstructed, GNSS can directly obtain train operation information. When the train passes through an environment with obstructions, GNSS and INS work simultaneously to obtain train operation information. The DMI on the train has the timely speed and kilometer mark information of the train. During the measurement, as we can not access to the onboard equipment. The camera is applied to collect the DMI interface. The image is processed to obtain the train data information. In this paper, the train data collected by GNSS/INS equipment is corrected by the Kalman filtering method. The data collected by DMI is filtered by the kinematics theorem and the least square method. What is more, a centralized fusion structure is used to process the DMI, GNSS, and INS data. This result can provide more accurate train time, speed, and kilometer mark information for the train during the operation test.

In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.

Global Navigation Satellite Systems (GNSS) has been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). To ensure the continuity and protection of UAV operations, using redundant navigation systems is essential. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of an much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS which switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth.

A new method for compensating the errors of integrated navigation systems using artificial neural networks

As the Global Satellite Navigation Systems (GNSS) signals are received and processed by the GNSS receivers with an extremely low power, they are vulnerable to non-intentional and intentional interference. In particular, the intentional transmission of Radio Frequency interference, known as jamming, can completely prevent the operation of GNSS receivers. The integration of measurements from multiple sensors with complementary error characteristics offers a possible countermeasure against jamming and, therefore, it can provide a position solution that has sufficient accuracy for the applications. This paper shows how a Deeply Coupled (DC) integration of GNSS with Inertial Navigation Systems (INS) and visual sensors enhances the robustness of the navigation system in presence of jamming. In particular, the effect of DC integration for jamming mitigation is investigated for a dynamic scenario where jamming signals have been added to a pre-recorded GNSS scenario by using the record and replay approach, emulating different interference levels. The results presented in this paper show that the deep coupling of GNSS with INS and visual sensors can sustain the navigation in presence of high power jamming, although such a strong interference completely masks the GNSS signal denying the navigation of a GNSS stand-alone system. Moreover, the test procedures suitability of the record and replay approach for the jamming scenario generation and combination with real datasets are discussed and analyzed.

This paper investigates into the feasibility of a combined use of Ultra-wideband (UWB) and Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) for integrated navigation in GNSS challenged environments. The performance of the original integrated GNSS/INS navigation system deteriorated rapidly due to the GNSS signal outages. The position information provided by UWB is used as the observation quantity of Extend Kalman Filter (EKF) to make up for the influence of GNSS signal loss. Considering the positioning service performance provided by UWB is closely related to the geometric distribution of base station of UWB, an optimization searching based on maximum convex-hull (OSMC) method is adopted to establish the optimal base station layout scheme. The features of navigation have been investigated in vehicle test with the results showing clear evidence that the method can resist the impact of GNSS outages effectively and the positioning information are stable and reliable.

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.