GPS World

Tag: Shanghai

  • GNSS, INS and neural networks combine for Arctic navigation

    GNSS, INS and neural networks combine for Arctic navigation

    GNSS receivers combined with inertial navigation systems (INS) have been widely applied to various mobile platforms.

    However, in Arctic regions, GNSS positioning accuracy is severely degraded from low satellite elevation angles, frequent ionospheric disturbances, and insufficient visible satellites.

    Moreover, the limited validation of existing onboard navigation systems further exacerbates the challenges of Arctic navigation.

    To address these issues, a new research paper describes a hybrid neural network model based on temporal convolutional networks (TCN) and long short-term memory (LSTM) networks. The hybrid solution has been tested in the Artic with successful results.

    The paper, “Robust GNSS/INS Integrated Navigation in Arctic GNSS-Challenged Environments Based on TCN-LSTM and MDAREKF,” is authored by Wei Liu, Tengfei Qi, Yuan Hu, Kaiwei Zhu, Tsung-Hsuan Hsieh and Shengzheng Wang of Shanghai Maritime University (DOI 10.1088/1361-6501/ae5279).

    The proposal combines the pseudo-measurement information of GNSS predicted by the model with INS for integrated navigation to compensate for the interruption of GNSS and correct the error of INS.

    Considering the potential bias in predicted pseudomeasurements, an adaptive robust extended Kalman filter (AREKF) algorithm based on Mahalanobis distance is further developed to dynamically adjust the innovation covariance matrix, thereby enhancing filter robustness.

    Field experiments conducted on an Arctic survey vessel demonstrate that the proposed TCN-LSTM combined with AREKF significantly improves both the robustness and accuracy of integrated navigation under GNSS-constrained environments. In particular, during GNSS outages of 50 seconds, 140 seconds and 400 seconds, the proposed method reduces the horizontal root mean square error (RMSE) by 47%, 38% and 76% respectively.

  • Multi-platform lidar enables digital twin cities

    Multi-platform lidar enables digital twin cities

    Digital twin technology emerged a decade ago to provide 3D virtual replicas of physical assets. Today, with Big Data and internet of things (IoT) capabilities, it is a complex and comprehensive method to support the construction of smart cities.

    Mapping Shanghai with the AlphaUni 900. (Image: CHC Navigation)
    Mapping Shanghai with the AlphaUni 900. (Image: CHC Navigation)

    As a virtual model, a digital city can be an indispensable tool to visualize the life of a city in real time. It provides layered data about buildings, urban infrastructure, utilities, businesses, and the movement of people and vehicles. By providing this information, digital twins enable intelligent urban development and modernization.

    Traditional methods of collecting and representing 2D spatial data, such as maps and images, are insufficient to meet the requirements for digital twin city models, where digital data provides the foundation for large-scale projects.

    For example, the derived 3D models must have a high capacity to be merged and correlated with social or economic spatial data from IoT and Big Data. Because of this, a high demand exists for global, accurate, real-time geospatial data that provides high-precision 2D and 3D information.

    Proof-of-concept

    To illustrate a typical digital cities project, CHC Navigation (CHCNAV) carried out a proof-of-concept demonstration in the Jinshan district of Shanghai for one month in March and April.

    The total area of the Jinshan district is approximately 600 km2. This area contains rich terrain features and typical characteristics of large, modern cities, such as high buildings, power lines, rivers and vegetation.

    Extracted 3D mesh created from the data. (Image: CHC Navigation)
    Extracted 3D mesh created from the data. (Image: CHC Navigation)

    The traditional method of capturing with a single-platform lidar system may leave some areas blank in the point-cloud data. CHCNAV’s AlphaUni 900 lidar solution, with its multi-platform capability, was able to capture complete data with four different platforms: an unmanned aerial vehicle (UAV), a car, a backpack and a boat or unmanned surface vehicle (USV).

    The AlphaUni series provides optimized data sets powered by advanced GNSS/inertial navigation system (INS) sensors and long-range scanners.

    Point cloud from aboard an Apache6 USV mapping a water channel. (Image: CHC Navigation)
    Point cloud from aboard an Apache6 USV mapping a water channel. (Image: CHC Navigation)

    During the project, the CHCNAV AlphaUni 900 seamlessly integrated the district’s buildings in the data sets and provided a sophisticated 3D image from both indoor and outdoor environments. Its high-accuracy capability and multi-platform design can improve the way high-precision data is collected. It successfully provides an innovative solution for the problems of 3D geospatial data acquisition required for the development of smart cities.

    Table Data: CHC Navigation
    Table data: CHC Navigation

     

  • Rohde & Schwarz and Huawei conduct field trials for 5G and V2X precision

    Rohde & Schwarz and Huawei conduct field trials for 5G and V2X precision

    Rohde & Schwarz and Huawei have successfully conducted cellular-based 5G V2X latency measurements in vehicular environments in field tests in Munich and Shanghai.

    In a joint project between Huawei and Rohde & Schwarz, a precision end-to-end delay measurement system for over-the-air IP transmissions was applied to 5G V2X communication for cooperative driving applications in field tests in a moving car.

    The precision absolute time standards on both ends were derived from two independent GPS receivers.

    URLLC will enable automated driving. (Image: Rohde & Schwarz)
    URLLC will enable automated driving. (Image: Rohde & Schwarz)

    The initial measurements show that it is possible to achieve delays in the millisecond regime in a 5G network, demonstrating superior latency performance in comparison to LTE.

    One of the key use cases of 5G is ultra-reliable low-latency communication (URLLC). Important for advanced vehicle-to-X communication use cases, URLLC will enable automated driving in the future.

    A measurement accuracy below 2 µs for each transmitted IP packet was demonstrated. The transmitted data contained various IP traffic streams including video, lidar and control data (ITS messages) for a tele-operated vehicle.

    While the trial in Munich was related to a tele-operated driving project, the tests in Shanghai were related to a platoon V2X testing site, where a number of vehicles traveling together are electronically connected via wireless communication.

    The delay for transmission of one IP packet from source over-the-air to a (moving) receiver (sink) needs to be measured, spanning all delays introduced by the radio transmitter, propagation delay and radio receiver from/to IP packet level.

    As latency is one of the key performance indicators of 5G and crucial for safety applications, such measurements could become an important criterion for future certification testing.

    “We are delighted to collaborate with Huawei to contribute with our test and measurement expertise to 5G technology development,” said Andreas Pauly, executive vice president,  Test & Measurement at Rohde & Schwarz. “With a strong global footprint in the telco ecosystem and close cooperation with partners, Rohde & Schwarz is committed to further expanding our innovative test and measurement solutions to new automotive applications.”