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The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes.

This paper presents a novel technique for the mass flow rate determination of particulate solids called the “Sliding Mass Technique”. The mass flow rate is a measure of the mass of a substance that passes through a given cross-sectional area per unit time. Its calculation requires simultaneous detection of the concentration and velocity of the Material Under Test. A novel measurement technique is designed for determining the concentration of the mass flow without the necessity for density evaluation. The mass flow rate is determined by fusing the established concentration results with velocity results obtained from “Microwave Spatial Filtering Velocimetry”. A new metamaterial-based mass flow sensor for particulate solids was designed, realized and measured in an industrial environment. A Software-Defined Radio (Ettus Research™’s USRP B210) was utilized as a sensor electronic system for DAQ purposes. A MATLAB app was developed to operate the SDR. Measurements were carried out on-site using a state-of-the-art wood pellet heating system with wood pellets with different moisture contents. The measurement results were found to be in very good agreement with the expected results, which strengthens the feasibility of this newly proposed measurement technique.

The pipe-jacking inertial guidance method is a key technology to solve the guidance problems of complex pipe-jacking projects, such as long distances and curves. However, since its guidance information is obtained by gyroscope integration, the accrued error is significant, which limits its application. The pipe-jacking construction process has two working modes of static and jacking. The accumulated errors of static state can be corrected with zero velocity to suppress the system position dispersion. Therefore, zero-velocity detection is required. However, the pipe-jacking velocity is so slow (1–2 m/h) that the traditional threshold-based zero-velocity detection method cannot accurately detect the zero-velocity interval (ZVI). Bidirectional long short term memory (Bi-LSTM) can effectively extract the features of repetitive and regular movements during the long-time pipe-jacking construction process. Therefore, this research proposes a working pattern detection model for pipe-jacking based on Bi-LSTM deep learning framework. Through establishing a data collection system to construct the data set and training the model, the accuracy of the test set reaches 98.54%. In addition, a zero-velocity correction model is established. According to the zero-velocity detection results of the Bi-LSTM model, zero-velocity correction is performed. Subsequently, an experimental platform is established to simulate a curve pipe-jacking and attitude experiments. The attitude experiment proves that the proposed model detects the ZVI accurately. The inclination error is corrected by 0.06 °, and the azimuth error is corrected by 0.18 °. Finally, the proposed model is validated by the crossing project of the China-Russia Eastern Natural Gas Pipeline, and the results show that the proposed model effectively detects the working pattern of pipe-jacking machine with strong robustness and adaptability. In summary, the method can effectively improve the detection accuracy of the pipe-jacking working pattern. It lays the foundation for the application of the inertial guidance system in complex pipe-jacking, such as long-distance and curved projects.

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx‐based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing. A auditory sensory system with motion perception is demonstrated by using a WOx‐based memristive synapse. Due to the coexistence of the volatile mode and semi‐nonvolatile mode in the Ar‐plasma‐treated (APT) WOx memristor, the functions of azimuth detection and velocity detection are realized via implementing the high‐pass filtering and processing the spike trains with frequency shift in both modes, respectively.

To address the problem of degraded positioning accuracy in traditional visual–inertial navigation systems (VINS) due to interference from moving objects in dynamic scenarios, this paper proposes an improved algorithm based on the VINS-Fusion framework, which resolves this issue through a synergistic combination of multi-scale feature optimization and real-time dynamic feature elimination. First, at the feature extraction front-end, the SuperPoint encoder structure is reconstructed. By integrating dual-branch multi-scale feature fusion and 1 × 1 convolutional channel compression, it simultaneously captures shallow texture details and deep semantic information, enhances the discriminative ability of static background features, and reduces mis-elimination near dynamic–static boundaries. Second, in the dynamic processing module, the ASORT (Adaptive Simple Online and Realtime Tracking) algorithm is designed. This algorithm combines an object detection network, adaptive Kalman filter-based trajectory prediction, and a Hungarian algorithm-based matching mechanism to identify moving objects in images in real time, filter out their associated dynamic feature points from the optimized feature point set, and ensure that only reliable static features are input to the backend optimization, thereby minimizing pose estimation errors caused by dynamic interference. Experiments on the KITTI dataset demonstrate that, compared with the original VINS-Fusion algorithm, the proposed method achieves an average improvement of approximately 14.8% in absolute trajectory accuracy, with an average single-frame processing time of 23.9 milliseconds. This validates that the proposed approach provides an efficient and robust solution for visual–inertial navigation in highly dynamic environments.

The rapid development of mass-market sensors built in portable devices has inspired a variety of ubiquitous pedestrian navigation applications. However, seamless positioning of consumer applications is still a challenge, especially in GNSS-denied environments and complex pedestrian dynamics. We present an enhanced foot-mounted pedestrian dead-reckoning (PDR) method to achieve continuous indoor and outdoor positioning for walking pedestrians. In order to improve the availability and stability of PDR under real pedestrian conditions, an adaptive zero-velocity detection method is given, and then, more accurate magnetic heading changes and pressure-based altitude changes are extracted. During stable pedestrian motions, the heuristic drift reduction models are also applied to constrain the cumulative heading and altitude errors. The field test results demonstrate that the adaptive detector performs well under different walking speeds and stair climbing, and the cumulative distance error of enhanced PDR is only 0.23%. After integrating with smartphone GNSS position, the root mean square value of PDR horizontal position error is about 1.34 m, and the multi-source information-enhanced PDR has the capability to continuously positioning in open sky, building occlusion and indoor situations under complex pedestrian walking conditions.

The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects.

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.

Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems.