Zhaoliang Liu

Wuhan University

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Co-Authors

Liang Chen

Wuhan University

Zhenhang Jiao

Shenyang Ligong University

Xin Zhou

Chinese Research Academy of Environmental Sciences

Yanlin Ruan

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Xiangchen Lu

Wuhan University

Ruizhi Chen

Wuhan University

Guangyi Guo

Wuhan University

Lei Tian

Shenzhen Technology University

Xiaoyan Liu

Sichuan University

Nan Shen

Nanjing Tech University

Cooperative Institutions

Wuhan University

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

University of Science and Technology of China

Chinese Academy of Sciences

Shanghai Electric (China)

University of Chinese Academy of Sciences

State Grid Corporation of China (China)

Harbin Engineering University

Shanghai Jiao Tong University

Jiangsu Maritime Institute

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A Neuro-Inspired Positioning System Integrating MEMS Sensors and DTMB Signals

IEEE Transactions on Broadcasting (2023)

Xiaoyan Liu Liang Chen Zhenhang Jiao Fangwen Yu Xiangchen Lu

Digital terrestrial multimedia broadcasting (DTMB) signals have been proven as good signals of opportunity (SoP) for positioning due to their stronger transmission power, while micro-electro-mechanical systems (MEMS) sensors are feasible to couple with wireless signals to achieve good performance of positioning. Studies have found that neurons in the hippocampus and the entorhinal cortex are the "GPS" system in the brain, which can help animals navigate in large and complex environments. To achieve stable and intelligent positioning, a neuro-inspired positioning system named MM-NeuroPos is proposed to integrate MEMS sensors and DTMB signals. In specific, MEMS sensors in the smartphone are used to acquire self-motion cues, and a DTMB signals receiver is developed to perceive external ranging cues. Then, the self-motion cues and external ranging cues are integrated into the multi-type and multi-scale navigation cells model, including grid cells, head direction cells, speed cells, and distance cells. Finally, the optimal position is decoded from these different types of multi-scale cells by maximum probability optimization to realize the construction of the experience map. Field test results showed that the 68% positioning errors of the MM-NeuroPos system are 1.19 m, while errors of the self-motion cues only are 1.91 m, and the errors of the classical particle filter (PF) system are 1.65 m. This proves that the MM-NeuroPos system based on multi-type and multi-scale neuron model has superior positioning performance than the PF system.

Ranging

Navigation System

10.1109/tbc.2023.3284409

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Citations (3)

Research on Energy Saving and Cost Saving Optimization Strategy of HVAC System

Zhaoliang Liu

Source

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Machine Learning for Time-of-Arrival Estimation With 5G Signals in Indoor Positioning

IEEE Internet of Things Journal (2023)

Zhaoliang Liu Liang Chen Xin Zhou Zhenhang Jiao Guangyi Guo

Location-based service in the indoor environment is playing a crucial role in different application scenarios. The introduction of technologies, such as ultradense network and massive multiple-input multiple-output enables fifth-generation (5G) cellular signals, as a new generation of cellular network signals, to show unique advantages in indoor positioning. This article describes 5G reference signal structures that can be used for navigation. A high-precision time-of-arrival estimation method based on 5G downlink signal is proposed that can be realized by edge computing. A software-defined receiver (SDR) based on machine learning to extract navigation observations from 5G signals is then developed. In simulation, the error sources of SDR in additive white gaussian noise channel and multipath channel were analyzed, and the possible ranging accuracy achieved by 5G signals in the developed SDR was evaluated. In field experiments, commercial 5G signals deployed by operators were collected, and the performance of SDR in practical applications was evaluated. The feasibility in practical applications of the proposed SDR is demonstrated, and high pseudorange measurement accuracy can be achieved.

Non-line-of-sight propagation

Time of arrival

10.1109/jiot.2023.3234123

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Citations (36)

THE TOA ESTIMATION OF CELLULAR NETWORK SIGNALS BASED ON MACHINE LEARNING IN COMPLEX URBAN ENVIRONMENTS

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (2023)

Zhaoliang Liu Liang Chen Zhenhang Jiao Xiangchen Lu Yanlin Ruan

Abstract. The precision location-based services in complex environment is a challenge in the field of navigation and positioning. With the continuous development of wireless communication technology in recent years, cellular network signals such as LTE and 5G have emerged as unique advantages in navigation and positioning applications. This paper presents a time-of-arrival (TOA) estimation method based on machine learning, which can use cellular network signals to obtain accurate ranging results in low signal-to-noise ratio conditions. For this purpose, we first present the cellular network signals that can be applied in navigation and positioning. Then, we describe in detail the process of TOA estimation based on machine learning. Finally, we carried out vehicular experiments in an urban environment to test the performance of the proposed method. The test results demonstrate the feasibility of the proposed method and achieve metre-level ranging accuracy.

Ranging

Time of arrival

SIGNAL (programming language)

10.5194/isprs-annals-x-1-w1-2023-673-2023

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Using Artificial Intelligence for Intelligent Ideological and Political Education Teaching

Zhaoliang Liu Li Luo

10.1109/iist62526.2024.00087

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Online displacement extraction and vibration detection based on interactive multiple model algorithm

Mechanical Systems and Signal Processing (2021)

Nan Shen Liang Chen Xiangchen Lu Hao Hu Yuanjin Pan

Structural Health Monitoring

10.1016/j.ymssp.2020.107581

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MLLoc: Machine Learning Location System based on RAIM in Mobile Networks

IEEE Sensors Journal (2024)

Zhaoliang Liu Liang Chen Zhenhang Jiao Xiangchen Lu Yanlin Ruan

Location-based service (LBS) has been playing an essential role in various sectors of society. As the most important solution in LBS, the Global Navigation Satellite System (GNSS) has been limited in availability in areas such as city and canyon. With the development of wireless communication technology, the widely distributed mobile networks can provide numerous quality line-of-sight path signals in GNSS-denied environments. Therefore, this paper develops a machine learning location (MLLoc) system based on receiver autonomous integrity monitoring (RAIM) to fuse mobile network signals with GNSS, in which a machine learning (ML) method is used to obtain stable time of arrival (TOA) estimation from the downlink broadcast signal of mobile networks. The field tests carried out in Long Term Evolution networks have verified the performance of the method. Specifically, the range accuracy of the TOA estimation based on ML and the positioning performance of the MLLoc system were evaluated through field tests. Our results verified that the developed MLLoc system is highly available in GNSS-denied environments and achieves meter-level positioning accuracy.

10.1109/jsen.2024.3383055

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Study of local faults in cables based on input impedance spectra

Jingpeng Li Lei Tian Chi Ma Zhaoliang Liu Guangjie Li

The large-scale laying of cables has accelerated the development of power systems, and the voltage level is increasing, while the traditional cable fault detection-pulse current experimentation (ICE), time domain reflectometry (TDR) and other methods in fault diagnosis have the problems of insufficient accuracy and limited fault type identification. Insufficient accuracy and limited fault type identification, so a cable local fault diagnosis method based on cable input impedance spectrum is proposed. Firstly, we analyse the shortcomings of traditional detection methods, and clarify the basic principle of the input impedance method and the fault identification method on the basis of the power cable distribution model. Then, we analyse the changes of the input impedance spectrum during different types of faults (open circuit, short circuit, high resistance, low resistance) in power cables, and judge them by combining with the frequency-domain diagrams, based on which, we verify the characteristics of the input impedance spectrum under the fault conditions through simulation. Finally, the error compensation is carried out for the simulation experiment to improve the accuracy of fault diagnosis. The final results show that the method used in this paper has good accuracy and better fault identification performance.

Reflectometry

Fault indicator

Identification

10.1117/12.3024335

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Experimental Study on Heat Transfer Enhancement of Modified CuO Nanofluid in Helical Grooved Tube

Journal of Physics Conference Series (2023)

Shanguo Zhao Guangmei Hai Hongtao Ma Long Huang Zhaoliang Liu

Abstract This study proposes a novel technology to enhance heat transfer by combining nanofluid and helical grooved tubes. The researchers used a two-step method to obtain a stable modified CuO nanofluid. They then constructed an experimental setup to investigate the single-phase heat transfer of the nanofluid in a helical grooved tube, and compared its performance to traditional methods under varying volume fractions and Reynolds numbers. The experimental results indicate that the use of nanofluid in the helical grooved tube significantly improves the convective heat transfer coefficient. Furthermore, the heat transfer coefficient increases gradually with the addition of nanoparticle volume fraction and Reynolds number. The helical grooved tube’s internal structure causes local turbulence at low Reynolds numbers, intensifying the nano-scale eddies and turbulence inside the nanofluid, enhancing fluid mixing and thermal diffusion rates, and thus improving the heat transfer efficiency.

Heat transfer enhancement

Volume fraction

10.1088/1742-6596/2584/1/012060

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Ku-band Self-Packaged Balanced Magic-T and Filtering Power Divider Based on Substrate-Integrated Suspended Sandwich Slotline With High Isolation

IEEE Transactions on Components Packaging and Manufacturing Technology (2024)

Xi‐Bei Zhao Feng Wei Ruizhi Liu Zhaoliang Liu Zhian Deng

Isolation

10.1109/tcpmt.2024.3482416

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