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.

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability.

China developed its Digital Television (DTV) standard in 2006, known as Digital Television Terrestrial Multimedia Broadcasting (DTMB), which employs time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) as the modulation method. In contrast to Global Navigation Satellite Systems (GNSSs), DTV signals have higher transmitting power, wider coverage, larger bandwidth, and fixed transmitter location. This paper explores the positioning performance of DTMB signals, and the potential to improve GNSS positioning accuracy in urban environments. Specifically, a solution is proposed, and a software-defined radio receiver is developed for wireless localization. Without changing the current signal structure, the pseudorandom noise (PN) sequences in the signal are used for signal acquisition and carrier phase ranging. The carrier phase of the first arrived path is extracted by the least squares matching pursuit method. Both static and dynamic field tests were conducted to verify the proposed ranging and positioning method. Centimeter-level ranging accuracy was achieved in the static scenario, while meter-level ranging accuracy was achieved in the dynamic scenario. As one possible application, the proposed ranging method was combined with GPS pseudorange measurements to achieve higher accuracy position results in an urban pedestrian scenario, especially when there is only a limited number of visible satellites.

Pedestrian Dead Reckoning (PDR) is a good alternative positioning method in Global Navigation Satellite Systems (GNSS)-challenged environments. It has the advantage in terms of infrastructure-independent, while the disadvantage is that, the estimated errors grow rapidly with time. The Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong transmission power, which contributes to better urban propagation and building penetration than GNSS signals. So, in this paper, an enhanced PDR algorithm aided with DTMB signals is proposed. In the study, a complete software-defined radio receiver is developed to estimate the Doppler speed and range information from DTMB signals. To reduce the accumulated errors of the PDR, the Extended Kalman Filter (EKF) algorithm is carried out to fuse the information of Doppler speed and ranging from the DTMB signals, as well as the information of walking speed and heading from the PDR. The results of the field tests showed that the 95% positioning error of the proposed algorithm is less than 3.94 m, while the PDR only is less than 9.19 m, which suggests the effectiveness of the proposed method to provide continuous positioning by fusing DTMB signals with PDR.

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.

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.