An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

Cao, Yu; Bai, Hongyang; Jin, Kong; Zou, Guanyu

doi:10.3390/electronics12132905

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…For

, as time accumulates, cumulative errors will occur. The longer the time, the greater the cumulative error, which requires timely correction by other navigation equipment [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Zhang,

Cui,

Zhang

2024

Sensors

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There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.

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“…For

, as time accumulates, cumulative errors will occur. The longer the time, the greater the cumulative error, which requires timely correction by other navigation equipment [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Zhang,

Cui,

Zhang

2024

Sensors

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“…In recent years, with the rapid development of artificial intelligence algorithms, its applicability in solving nonlinear problems has gradually improved, so it has begun to be used to assist navigation systems. Researchers have used methods such as support vector regression (SVR) [29], extreme learning machine (ELM) [30], Back propagation (BP) network [31], Long Short-Term Memory (LSTM) network [32], gated recurrent unit (GRU) network [33] and LightGBM regression [34] to conduct research on navigation assistance for vehicles, ships, and unmanned underwater vehicles. Meanwhile, when using artificial intelligence algorithms to predict GNSS navigation results using INS navigation results, historical data based on a certain step size can effectively improve the prediction accuracy [35].…”

Section: Introductionmentioning

confidence: 99%

A robust integrated navigation optimization method for USV in signal occlusion environment

Lou,

Liu,

et al. 2024

Phys. Scr.

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Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

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“…To address these limitations, various solutions have been proposed. As shown in Table 1, many researchers have sought to address the issue of positioning loss due to GPSdenied areas by employing inertial sensor data to predict trajectories [6][7][8][9]. This approach involves continuously tracking a vehicle's position based on acceleration, direction, and attitude data provided by inertial sensors, assuming an initial position is available.…”

Section: Introductionmentioning

confidence: 99%

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

Huang,

Song,

Choi

et al. 2023

Sensors

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Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial sensor and electronic map data. ConGPS estimates the speed and heading direction of a moving container based on the inertial sensor data, the container trajectory, and the speed limit information provided by an electronic map. The directional information from magnetometers, coupled with map-matching algorithms, is employed to compute container trajectories and current positions. ConGPS significantly reduces the frequency of GPS calls required to maintain an accurate current position. To evaluate the accuracy of the system, 280 min of driving data, covering a distance of 360 km, are collected. The results demonstrate that ConGPS can maintain positioning accuracy within a GPS-call interval of 15 min, even if using low-cost inertial sensors in GPS-denied environments.

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An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

Cited by 4 publications

References 57 publications

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

A robust integrated navigation optimization method for USV in signal occlusion environment

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

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