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14 pages, 9227 KiB

Open AccessArticle

In-Motion Alignment with MEMS-IMU Using Multilocal Linearization Detection

by Yulu Zhong, Xiyuan Chen, Ning Gao and Zhiyuan Jiao

Sensors 2025, 25(9), 2645; https://doi.org/10.3390/s25092645 - 22 Apr 2025

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to [...] Read more.

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to estimate the initial state of the Kalman filter, the proposed method utilizes the designed quasi-uniform quaternion generation method to estimate several possible initial states. Then, the proposed method selects the most probable result based on the generalized Schweppe likelihood ratios among multiple hypotheses. The experiment result of the proposed method demonstrates the advantage of estimation performance within poor-quality measurement conditions for the long-duration coarse alignment using MEMS-IMU compared with the OBA-based method. The proposed method has potential applications in alignment tasks for low-cost, small-scale vehicle navigation systems. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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14 pages, 16597 KiB

Open AccessArticle

An Enhanced, Real-Time, Low-Cost GNSS/INS Integrated Navigation Algorithm and Its Platform Design

by Pengcheng Wang, Yuting Gao, Qingzhi Zhao, Yalong Wang, Feng Zhou and Dengxiong Zhang

Sensors 2025, 25(7), 2119; https://doi.org/10.3390/s25072119 - 27 Mar 2025

Viewed by 190

Abstract

The integration of the global navigation satellite system (GNSS) and the inertial navigation system (INS) is a well-established method for achieving accurate positioning, especially in applications involving unmanned aerial vehicles (UAVs). UAVs are increasingly used across various fields, yet they face challenges such [...] Read more.

The integration of the global navigation satellite system (GNSS) and the inertial navigation system (INS) is a well-established method for achieving accurate positioning, especially in applications involving unmanned aerial vehicles (UAVs). UAVs are increasingly used across various fields, yet they face challenges such as the need for real-time processing and the impact of low-quality measurements from cost-effective devices. To address these challenges, we propose a velocity-constrained, enhanced, real-time, low-cost, GNSS/INS integrated navigation algorithm and design an algorithmic platform based on the open-source software KF_GINS. The algorithm supports loosely coupled integration of GNSS position data and raw inertial measurement unit (IMU) data, utilizing a 4G data transmission unit (DTU) for real-time data transmission and performing loosely coupled computations on the received data. Subsequently, we successfully applied this algorithm to low-cost integrated navigation devices, such as UAVs. We tested the algorithm platform using one set of vehicle-mounted data and six UAV datasets. Experimental results indicate that the algorithm platform effectively performs computations under various conditions, improving single-point positioning (SPP) accuracy by up to 15.38% horizontally and 6.78% vertically. These findings demonstrate the algorithm platform’s capability to significantly enhance the accuracy and stability of integrated navigation positioning for UAVs. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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21 pages, 11171 KiB

Open AccessArticle

A Beam Steering Vector Tracking GNSS Software-Defined Receiver for Robust Positioning

by Scott Burchfield, Charles Givhan and Scott Martin

Sensors 2025, 25(6), 1951; https://doi.org/10.3390/s25061951 - 20 Mar 2025

Viewed by 219

Abstract

Global navigation satellite systems are the best means of navigation for dynamic platforms. However, interference, line-of-sight blockages, and multipath are destructive to receiver operations. Advanced receiver architectures like vector tracking loops have been shown to be more resilient in tracking during degraded signal [...] Read more.

Global navigation satellite systems are the best means of navigation for dynamic platforms. However, interference, line-of-sight blockages, and multipath are destructive to receiver operations. Advanced receiver architectures like vector tracking loops have been shown to be more resilient in tracking during degraded signal environments and dynamic scenarios. Additionally, controlled reception pattern antennas can be used to steer the effective antenna gain pattern to resist interference. This work introduces algorithms for a software-defined radio that combines vector tracking loops with a phased antenna array to digitally steer beams for the amplification of signals of interest so that the effects of signal degradation and multipath can be reduced. The proposed receiver design was tested on dynamic live sky data in multipath-rich environments and compared against traditional scalar receivers with and without beamforming as well as robust commercial receivers. The results showed that beam steering receivers were obtaining the expected amplification and that the vector tracking with beam steering was able to provide better positioning and signal tracking performance than the other implemented software receivers and provide continuous measurements where the commercial receiver failed to track degraded signals. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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19 pages, 11821 KiB

Open AccessArticle

Bias Estimation for Low-Cost IMU Including X- and Y-Axis Accelerometers in INS/GPS/Gyrocompass

by Gen Fukuda and Nobuaki Kubo

Sensors 2025, 25(5), 1315; https://doi.org/10.3390/s25051315 - 21 Feb 2025

Viewed by 635

Abstract

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a [...] Read more.

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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16 pages, 5789 KiB

Open AccessArticle

Research on EV Crawler-Type Soil Sample Robot Using GNSS Information

by Liangliang Yang, Chiaki Tomioka, Yohei Hoshino, Sota Kamata and Shunsuke Kikuchi

Sensors 2025, 25(3), 604; https://doi.org/10.3390/s25030604 - 21 Jan 2025

Viewed by 678

Abstract

In Japan, the decline in the number of agricultural workers and the aging of the workforce are problems, and there is a demand for more efficient and labor-saving work. Furthermore, in order to correct the rising price of fertilizer and the increasing burden [...] Read more.

In Japan, the decline in the number of agricultural workers and the aging of the workforce are problems, and there is a demand for more efficient and labor-saving work. Furthermore, in order to correct the rising price of fertilizer and the increasing burden on the environment caused by fertilizer, there is a demand for more efficient fertilization. Therefore, we aim to develop an electric soil sampling robot that can run autonomously using Global Navigation Satellite System (GNSS) information. GNSS and the Inertial Measurement Unit (IMU) are used as navigation sensors. The work machine is a crawler type that reduces soil compaction. In addition, a route map was generated in advance using the coordinate values of the field, with soil sampling positions set at 10 m intervals. In the experiment, the robot traveled along the route map and stopped automatically. The standard deviation of the standard deviation of lateral error was about 0.032 m, and the standard deviation of the interval between soil sampling positions was also less than 0.05 m. Therefore, it can be said that the accuracy is sufficient for soil sampling. It can also be said that even higher density sampling is possible by setting the intervals for soil sampling at finer intervals. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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19 pages, 7501 KiB

Open AccessArticle

Multi-Antenna GNSS–Accelerometer Fusion Attitude Correction Algorithm for Offshore Floating Platform Displacement Monitoring

by Xingguo Gao, Junyi Jiang, Guoyu Xu, Zengliang Chang and Jichao Yang

Sensors 2024, 24(23), 7804; https://doi.org/10.3390/s24237804 - 6 Dec 2024

Viewed by 790

Abstract

In order to solve the problem of fixed ambiguity and decreased accuracy in GNSS displacement monitoring of the offshore floating platforms, an attitude correction algorithm based on the fusion of a multi-antenna GNSS and an accelerometer was proposed using the Kalman filtering method. [...] Read more.

In order to solve the problem of fixed ambiguity and decreased accuracy in GNSS displacement monitoring of the offshore floating platforms, an attitude correction algorithm based on the fusion of a multi-antenna GNSS and an accelerometer was proposed using the Kalman filtering method. The algorithm was validated on a physical simulation platform and a real offshore floating platform. The results indicate that this fusion method effectively compensates for the loss of high-frequency displacement information caused by low GNSS sampling rates, improves situations in which the fusion effect deteriorates due to attitude changes, and enhances the accuracy of GNSS and accelerometer fusion monitoring through offshore buoy testing. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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27 pages, 2578 KiB

Open AccessArticle

A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration

by Adalberto J. A. Tavares Jr. and Neusa M. F. Oliveira

Sensors 2024, 24(22), 7331; https://doi.org/10.3390/s24227331 - 16 Nov 2024

Cited by 2 | Viewed by 1986

Abstract

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, [...] Read more.

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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17 pages, 1181 KiB

Open AccessArticle

A Switched Approach for Smartphone-Based Pedestrian Navigation

by Shenglun Yi, Mattia Zorzi, Xuebo Jin and Tingli Su

Sensors 2024, 24(16), 5247; https://doi.org/10.3390/s24165247 - 14 Aug 2024

Viewed by 3870

Abstract

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements [...] Read more.

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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17 pages, 4444 KiB

Open AccessArticle

A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints

by Haiyang Qiu, Yun Zhao, Hui Wang and Lei Wang

Sensors 2024, 24(13), 4419; https://doi.org/10.3390/s24134419 - 8 Jul 2024

Viewed by 1810

Abstract

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. [...] Read more.

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite’s position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method’s potential for practical applications. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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