Wireless Positioning:Technologies,Applications,Challenges,and Future Development Trends

The development of the fifth-generation(5G)mobile communication systems has entered the commercialization stage.5G has a high data rate,low latency,and high reliability that can meet the basic demands of most industries and daily life,such as the Internet of Things(IoT),intelligent transportation systems,positioning,and navigation.The continuous progress and development of society have aroused wide concern.Positioning accuracy is the core demand for the applications,especially in complex environments such as airports,warehouses,supermarkets,and basements.However,many factors also affect the accuracy of positioning in those environments,for example,multipath effects,non-line-of-sight,and clock synchronization errors.This paper provides a comprehensive review of the existing works about positioning for the future wireless network and discusses its key techniques and algorithms,as well as the current development and future directions.We first outline the current traditional positioning technologies and algorithms,which are discussed and analyzed with the relevant literature.In addition,we also discuss application scenarios for wireless localization.By comparing different positioning systems,the challenges and future development directions of existing wireless positioning systems are prospected.

SNWPM:A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable

Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.

Wireless Positioning Technologies in CDMA System

Location services not only provide address information, but also locate, monitor and track terminals on a real-time basis. To deliver fast and accurate location services, it is necessary to select an appropriate positioning method. Currently, 3 methods are available for CDMA wireless positioning: network based, Mobile Station (MS) based, and GpsOne positioning.As these methods are different in location time, accuracy, availability, privacy, and operation cost, they shall be selected according to the actual network conditions. Network structure, information bearer protocols, and transport mode make the basis of a wireless positioning system. They can be implemented in different ways, and some details shall be specified by the operators.

A PHEMT Based Wideband LNA for Wireless Applications

This work is about the development of a super low noise amplifier with minimum power consumption and high gain for several wireless applications.The amplifier operates at frequency bands of 0.9-2.4 GHz and can be used in many applications like Wireless local area network(WLAN),WiFi,Bluetooth,ZigBee and Global System for mobile communications(GSM).This new design can be employed for the IEEE 802.15.4 standard in industrial,scientific and medical(ISM) Band.The enhancement mode pseudomorphic high electron mobility transistor PHEMT is used here due to its high linearity,better performance and less noisy operation.The common source inductive degeneration method is employed here to enhance the gain of amplifier.The amplifier produces a gain of more than 17 dB and noise figure of about 0.5 dB.The lower values of S11 and S22 reflect the accuracy of impedance matching network placed at the input and output sides of amplifier.Agilent Advance Design System(ADS) is used for the design and simulation purpose.Further the layout of design is developed on the FR4 substrate.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

Wireless positioning and path tracking for a mobile platform in greenhouse

In order to realize intelligent greenhouse,an automatic navigation method for a mobile platform based on ultra-wideband(UWB)positioning technology was proposed and validated in this study.The time difference of arrival(TDOA)approach was used to monitor and track the UWB positioning to obtain the localization information of the mobile platform working in a greenhouse.After applying polynomial fitting for positioning error correction,the system accuracy was within 5 mm.A fuzzy controller model was constructed by incorporating the lateral and heading deviations as input variables and the steering angle of front wheel as the output variable.A fuzzy rule was established based on domain knowledge,as well as the steering angle of front wheel offline query table,which was applied to alleviate the calculative load of the controller.Experimental results confirmed that the automatic navigation method proposed in this study performed satisfactorily,with a steady-state error ranging from 41 mm to 79 mm when tracking straight line,and an average error of 185 mm and an average maximum error of 532 mm when tracking polygon.In addition,the maximum error occurred at the polygonal corner which could meet the needs of driving on the narrow road in the greenhouse.The method proposed in this study provides a new systematic approach for the research of greenhouse automatic navigation.