As the number of wireless applications and devices grows, higher standards for the quality of service and navigation performance of mobile networks are required. Numerous critical applications, including unmanned aerial vehicles, internet of things, digital twin, and military systems, require reliable communication and accurate navigation services. To meet these requirements, the development of the sixth generation (6G) network is necessary. 6G networks provide seamless 3-dimensional coverage in space–air–ground–sea area, as well as deep coupling of communication, sensing, and computation. 6G networks will be combined with low Earth orbit (LEO) satellites to construct a universal and intelligent integrated system of communication, sensing, and computing by utilizing the benefits of LEO satellites, such as miniaturization, modularity, large bandwidth, low latency, and wide area coverage. One of the critical construction tasks in this system is the integration of communication and navigation (ICAN), which can break the limitations of the global navigation satellite system, provide high-precision, robust navigation capability, and enable high-quality communication services. In this article, a comprehensive survey is presented for ICAN technologies toward LEO-enabled 6G networks (LEO-ICAN), including the framework design, system implementation, and key technologies. We also highlight the challenges and opportunities ahead faced by the LEO-ICAN system. Finally, the prospect development and future research trends are discussed, and a few ideas for practical and effective LEO-ICAN solutions are provided. This survey provides a reference for the theoretical design, technological innovation, and system implementation of LEO-ICAN, which is capable of coping with the demand of massive access and global seamless coverage in the upcoming 6G network era. 

Integration of Communication and Navigation Technologies Towards LEO-Enabled 6G Networks: A Survey

The pedestrian positioning algorithm based on inertial measurement unit (IMU) has been widely applied in scenarios such as firefighting and counterterrorism where global navigation satellite system (GNSS) performance is limited. However, most existing systems do not take into account crawling-type motions. Additionally, the zero velocity update error constraint method often used in pedestrian navigation is not applicable to crawling-type motions. As a result, errors introduced by crawling-type motions become the primary source of error in complex motion scenarios. In order to enhance pedestrian positioning accuracy during complex motion modes, this study analyzed data across various motion modes and developed specific positioning algorithms based on a hierarchical classification. First, we observe that data patterns for standing mode and crawling-type mode are distinct based on the analysis of motion mode. Therefore, to improve step detection accuracy, support vector machine (SVM) is employed to classify motion modes into three coarse categories: standing mode (forward/lateral/backward walking, and running), crawling mode (baby/military crawling), and one side crawling mode (one side military crawling). Specific step detection algorithms are designed for each category. Furthermore, the step lengths of standing motions and crawling-type motions exhibit relatively strong correlations with data characteristics and height, respectively. Hence, different step length models are designed. Fine classification of motion modes is accomplished using the minimally random convolutional kernel transform (MINIROCKET), optimizing step length model parameters and heading correction for various fine motion modes. Finally, precise position estimation is achieved. Experimental results demonstrate that this algorithm exhibits stronger adaptability and superior performance in complex motion scenarios.

Pedestrian Dead Reckoning Based on Complex Motion Mode Recognition Using Hierarchical Classification

For unmanned aerial vehicles (UAVs), high-precision measurement and high-speed communication are necessary to realize flight and operational missions. In this paper, we propose an integrated communication and measurement system in a Doppler frequency offset environment. The system combines orthogonal frequency division multiplexing (OFDM) modulation with binary offset carrier (BOC) modulation to formulate an OFDM+BOC composite signal through power control. High-precision measurement is achieved through BOC modulation, and high data transmission is achieved through OFDM modulation. Furthermore, the high-precision Doppler frequency offset tracked by the BOC signal is adopted to assist in the demodulation of the OFDM signal. This substantially decreases the impact of the Doppler frequency offset on the OFDM signal. Moreover, the ranging error is within 10−2, and the maximum Doppler frequency error is within 2 Hz.

/pdf/integrated-communication-and-measurement-system-with-boc-1wz5azrd.pdf

Integrated Communication and Measurement System with BOC-Assisted OFDM

Satellite Navigation Signal Quality Monitoring Algorithm Based on DBO-SVM

With the rapid development of intelligent applications, the mission execution mode of aircraft has gradually expanded from a single real-time control mode to a hybrid mode of real-time system and non-real-time system, which brings new challenges to the software deployment architecture of aircraft. The centralized architecture relies on the processing power of local hardware, and it is difficult to support the operation of complex intelligent algorithms, while the distributed architecture is affected by the communication delay and data synchronization uncertainty, and it is also difficult to meet the complex application scenarios of aircraft. On the basis of summarizing the development trend of aircraft technology, this study analyzed the requirements of real-time and non-real-time software architecture systems brought about by intelligent applications of aircraft, and designed a hybrid deployment architecture based on autonomous systems OpenEuler and UniProton, and then verified the performance of autonomous systems under the hybrid deployment architecture through deployment tests. The results show that the hybrid deployment architecture proposed in this scheme has a good applicability to the intelligent application of aircraft.

Design of Hybrid Deployment Architecture for Aircraft Software for Intelligent Applications

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.

Positioning Method of Pedestrian Dead Reckoning Based on Human Activity Recognition Assistance

ABSTRACT Driven by improvements in satellite internet and Low Earth Orbit (LEO) navigation augmentation, the integration of communication and navigation has become increasingly common, and further improving navigation capabilities based on communication constellations has become a significant challenge. In the context of the existing Orthogonal Frequency Division Multiplexing (OFDM) communication systems, this paper proposes a new ranging signal design method based on an LEO satellite communication constellation. The LEO Satellite Communication Constellation Block-type Pilot (LSCC-BPR) signal is superimposed on the communication signal in a block-type form and occupies some of the subcarriers of the OFDM signal for transmission, thus ensuring the continuity of the ranging pilot signal in the time and frequency domains. Joint estimation in the time and frequency domains is performed to obtain the relevant distance value, and the ranging accuracy and communication resource utilization rate are determined. To characterize the ranging performance, the Root Mean Square Error (RMSE) is selected as an evaluation criterion. Simulations show that when the number of pilots is 2048 and the Signal-to-Noise Ratio (SNR) is 0 dB, the ranging accuracy can reach 0.8 m, and the pilot occupies only 50% of the communication subcarriers, thus improving the utilization of communication resources and meeting the public demand for communication and location services.

https://doi.org/10.1080/10095020.2022.2121229

Design and performance evaluation of a novel ranging signal based on an LEO satellite communication constellation

High-precision systems such as civil aviation have put forward higher requirements for navigation systems, including indicators such as accuracy and integrity. Signal distortions and evil waveforms (EWF) generated by the signal-generating hardware on the satellite can severely affect the cross-correlation function of the signal, thereby affecting the integrity of the navigation system. With the further development of the BeiDou Navigation System (BDS), the types of signal distortion are subdivided into three types: analog distortion, subcarrier distortion, and PN code distortion. Traditional multi-correlator methods are no longer applicable under the requirements of modern navigation systems. In this paper, a machine learning-based BeiDou B1C signal anomaly monitoring algorithm is proposed. We detected and classified the signals using a quadratic discriminant analysis (QDA) method. The results show that our method can accurately classify the distortion types under the condition that the accuracy of distortion detection can be greatly improved. Meanwhile, our method is also highly effective and robust.

/pdf/research-on-beidou-b1c-signal-abnormal-monitoring-algorithm-2bojzq0u.pdf

Research on BeiDou B1C Signal Abnormal Monitoring Algorithm Based on Machine Learning

Aiming at the issue that the navigation augmentation signal interferes with the normal received signal of the LEO-NA (Low Earth Orbit Navigation Augmentation) payload in the LEO-NA system, a spatial self-interference cancellation algorithm based on WIMUSIC (Weighted Improved Multiple Signal Classification) and LCMV (Linearly Constrained Minimum Variance) is proposed. First, a new covariance matrix is obtained by reconstructing the covariance matrix of the received data. The corresponding noise subspace is constructed by decomposing the new covariance matrix with singular value decomposition. Then, the weighted value is built using the eigenvalue, and the DOA (Direction of Arrival) estimation of the received signal is realized. Finally, the LCMV adaptive beamforming algorithm is used to form null at the self-interference signal direction and gain at the interest signal direction. Simulation results indicate that compared with the existing improved MUSIC (Multiple Signal Classification) methods, our algorithm can achieve accurate DOA estimation of the received signal when SNR (Signal-to-Noise Ratio) is low, and the signal angle interval is small. Compared with the existing variable step size LMS (Least Mean Square) algorithms, ICR (Interference Cancellation Ratio) is improved by 7.3dB.

A Spatial Self-Interference Cancellation Algorithm of LEO Navigation Augmentation System Based on WIMUSIC and LCMV

Integer Aperture Bootstrapping (IAB) is a crucial method for testing ambiguity acceptance in carrier-phase differential global navigation satellite system (CDGNSS) positioning. It has the advantage that integrity parameters, such as the failure rate, can be analytically calculated, which is essential in safety-of-life applications. Although the IAB methods have been extensively studied, their threshold-determining method is still not well explained, theoretically. In this study, a new method, named Analytical Integer Aperture Bootstrapping (AIAB), is theoretically derived to determine the optimal IAB threshold. AIAB is novel in that: (1) a precise and easy-to-compute expression has been developed to describe the relationship between the IAB threshold and the failure rate, (2) an analytical function model has been derived from the expression to determine the IAB threshold; moreover, the function model is simplified, and (3) a data-constraint approach has been proposed to reduce the complexity of IAB. In the global CDGNSS simulations, AIAB is shown to outperform the existing IAB methods under both strong and weak models, particularly at low fix rates, which are 23% to 40% higher than the basic IAB method. The Monte Carlo simulation results show that AIAB can obtain almost theoretically the same performance as Optimal Integer Aperture (OIA).

Optimized Integer Aperture Bootstrapping for High-Integrity CDGNSS Applications

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