Location-Based Services have become an indispensable part of our daily life, the sparsity of location finding makes it possible to estimate specific position by Compressive Sensing (CS). Using public Frequency Modulation (FM) broadcasting and Digital Television Terrestrial Multimedia Broadcasting (DTMB) signals, this paper presents an indoor positioning scheme, which is consisted of an offline stage and an online stage. In the offline stage, the Received Signal Strength (RSS) at the Reference Points (RPs) is measured, including the average and variance of public FM broadcasting and DTMB signals. In the online stage, the K-Weighted Nearest Neighbor algorithm is used to fulfill coarse positioning, which is able to narrow the selection scope of locations and choose partial RPs for accurate positioning. Then, the accurate positioning is implemented through CS. Experiment shows that the average positioning error of the proposed scheme is 1.63m. What's more, a CS-based method has been proposed in this paper to reduce the labor cost when collecting data. Experiment shows the average positioning error is 2.04m, with the advantage of a 34% labor cost reduction. Experiment results indicate that the proposed system is a practical indoor positioning scheme.

The secondary modulation with the Neumann-Hoffman code increases the possibility of bit sign transition. Unlike other GNSS signals, there is no pilot component for synchronization in BeiDou B1/B3 signals, which increases the complexity in acquisition. A previous study has shown that the delay and multiplication (DAM) method is able to eliminate the bit sign transition problem, but it only applies to pretty strong signals. In this paper, a DAM-based BeiDou signal acquisition approach, called variable length data accumulation (VLDA), is proposed to acquire weak satellite signals. Firstly, the performance of DAM method versus the different delays is analyzed. The DAM operation not only eliminates bit sign transition, but it also increases noise power. Secondly, long-term signal is periodically accumulated to improve signal intensity in order to acquire weak signals. While considering the Doppler frequency shift of ranging codes, the signal length must be compensated before accumulating long-term signal. Finally, the fast-Fourier-transform based parallel code phase algorithm are used for acquisition. The simulation results indicate that the proposed VLDA method has better acquisition sensitivity than traditional non-coherent integration method under the same calculation amount. The VLDA method only requires approximately 27.5% of calculations to achieve the same acquisition sensitivity (35 dBHz). What is more, the actual experimental results verify the feasibility of the VLDA method. It can be concluded that the proposed approach is an effective and feasible method for solving the bit sign transition problem.

Indoor localization systems based on Wi‐Fi signal strength fingerprinting techniques are widely used in office buildings. However, a general problem of these systems pertains to Wi‐Fi signal degradation due to the environmental factors. And also, these systems cannot be used in the environments not covered with Wi‐Fi signals or the environments with only a single Wi‐Fi access point. In this paper, a new indoor location fingerprinting system using both FM radio and Digital Television Terrestrial Multimedia Broadcasting (DTMB) signals is proposed. First, the indoor location fingerprinting using FM radio and DTMB signals is theoretically analyzed to confirm its feasibility. Then, a specially designed combined strength fingerprinting location algorithm is proposed for the location system, which is achieved on the USRP2 platform. Finally, the system is tested in a typical indoor environment. The theoretical analysis and the tests show that the indoor location fingerprinting system using FM radio and DTMB signals has a similar localization accuracy to the Wi‐Fi signal strength fingerprinting location system, while it has a wider coverage area, a lower maintenance cost, and more stable signal strength, which makes it a practical indoor positioning method.

The device‐free localisation, which neither requires the targets to carry any electronic device nor participate in the position process proactively, is an innovated and prospective technique. Traditional device‐free techniques include computer vision, infrared sensor, indoor radar etc. Since there is a rapid development of the internet of things (IoT) brings massive wireless radio links, it is possible to achieve the device‐free localisation by investigating the link quality indicator of wireless links. In this Letter, the authors propose a device‐free localisation procedure to position the target by acquiring all the wireless link indictors in the IoT network, utilising deep learning to train the deep neural network. The experiment shows the average positioning error can be <0.2 m in an experimental environment of 160 square metres.