Magnetic map matching localization has various strategic applications, such as drone reconnaissance, submarine navigation, cruise missile guidance, etc. However, the complexity of matching localization architectures based on magnetic map often fails to meet the fast position update frequency demands for high-speed moving targets. To address this issue, this article proposes a lightweight matching localization architecture, i.e., an adaptive integrated particle filter (AIPF) method based on magnetic map and inertial sensors. The AIPF method includes the prediction phase, correction phase, normalization phase, integration phase, and estimation phase. By pre-establishing a Bayesian probability integral table (BPIT) based on standard normal probability distribution functions and expectation functions, the integration results can be directly queried from the BPIT adaptively, avoiding the time consuming integration operation when matching the potential magnetic fingerprint. The heading and step length can be obtained by dead reckoning based on inertial sensors. Extensive experiments show that when the heading uncertainty is less than 1.2°, or the step length uncertainty is less than 30% of the precision of reference magnetic map, the AIPF method has comparable positioning accuracy while the computational complexity is reduced to one-tenth of that in the compared methods, such as particle filter, the adaptive optimization firefly algorithm, extended Kalman particle filter, etc. Moreover, when the experimental condition is more challenging, the AIPF method demonstrates better robustness than the compared methods. This work provides a new solution for lightweight matching localization algorithms using magnetic maps and inertial sensors, suitable for various applications requiring high position update frequencies or fast target localization and tracking.

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.

Pseudolites can overcome the shortcomings of Global Navigation Satellite System(GNSS), and simulate similar navigation satellite signals to be used as a stable and reliable positioning signal source in the indoor environment, which has gradually become a research hotspot in the field of indoor positioning. Due to the influence of factors such as multipath propagation, it is impossible to directly replicate the existing outdoor positioning method. To solve the above problems, we designed a BDS/GPS array pseudolite indoor high-precision positioning system PIP's (Pseudolite Indoor Positioning system). Relying on this system, we proposed a pseudo-satellite carrier phase differential positioning algorithm based on particle filtering, which avoids the problem of solving the integer ambiguity by calculating the pseudorange similarity. Then, aiming at the problem of divergence of positioning results caused by different carrier moving speeds, a method of dynamically estimating particle velocity based on Doppler frequency shift is proposed, which improves the accuracy of particle weight distribution, thereby improving the positioning accuracy and continuity of the positioning system. Finally, in order to verify the positioning performance of the proposed system, we conducted a large number of experiments in the microwave anechoic chamber and the test field environment. The results show that in the microwave anechoic chamber, the average positioning error in the X direction is 0.09m, and the average positioning error in the Y direction is 0.12m. In the test field environment, we analyzed the influence of different transmitting powers and different types of antennas on the positioning performance. At the same time, the positioning accuracy heat map is drawn, and the maximum positioning error is 0.67m.

Aiming at the problem that the traditional Pedestrian Dead Reckoning (PDR) cannot adapt to the reliable positioning of the target in different motion states, this paper designs and proposes a positioning method based on deep learning for Human Activity Recognition (HAR) assisted PDR. First, the Wavelet-CNN deep learning network is used in the offline stage to preprocess and train the data of the built-in MEMS sensor of the smartphone to obtain the HAR model. Then, in the online real-time positioning stage, the different motion states of the target are identified based on the HAR model, and the pedestrian step detection and step size estimation algorithms are adaptively adjusted. Finally, the HAR-assisted PDR algorithm is implemented on the smart-phone, and a large number of tests and verifications are carried out in the experimental environment. The proposed localization method for HAR-assisted PDR based on deep learning can accurately identify a variety of complex human motion states, and the recognition accuracy is as high as 99.50%. At the same time, the accuracy rate of the step recognition algorithm of state-of-the-art PDR is increased by 10.94 %, and the maximum positioning error is reduced by about 16.2%, which verifies the effectiveness of the proposed algorithm.

With the increasing demand for location‐based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless‐based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image‐based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective‐n‐point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image‐based indoor localization methods do not require infrastructure and have a wide range of applications that include self‐driving, robot navigation, and augmented reality.

Due to the influence of indoor signal multipath effect and human disturbance, the indoor positioning technology of WiFi fingerprint based on deep learning is poor stability. The large sample and accurate data in the room is very difficult to collect for weights training of deep learning, so it is difficult to be widely used. Firstly, the innovative algorithm with multi-sensor fingerprint and deep learning for indoor position (DL-IMPS) is puts forward, and used the statistical model and the ray tracing method to construct a large sample data for weights training, the experiment is proved that the model data of WiFi-RSSI is a subset of the actual measurement data. Secondly, 10.9m×7.4m indoor location test environment is set up in the room, through 9700 groups of modeling data and 1300 groups of measurement data to train DBN's weights, it get more optimal weights matrix and speed up the convergence rate. Finally, The performance of WKNN and DL-IMPS is compared under four different paths, The results prove that the average error of DL-IMPS is 0.52 m, the probability of error less than 1 m is 92.3%, but the average error of WKNN is 1.39 m, the probability of error of less than 1m is 45%, Location accuracy and stability of DL-IMPS are superior to WKNN. The other experiment is the comparison between one-sensor indoor location and DL-IMPS, Locating error probability of DL-IMPS is 1%, and the convergence speed is fast, that of WiFi-only is 24%, iBeacon-only is 25%, Geomagnetic-only is 15%, DL-IMPS have better positioning accuracy and robustness.