Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System

The BeiDou navigation satellite system (BDS-3) has been deployed and provides positioning, navigation, and timing (PNT) services for users all over the world. On the basis of BDS-2 system signals, BDS-3 adds B1C, B2a, B2b, and other signals to realize compatibility and interoperability with other global navigation satellite systems (GNSS). Network real-time kinematic (RTK) technology is an important real-time regional high-precision GNSS positioning technology. Combined with the network RTK high-precision service platform software developed by the author’s research group and the measured data of a provincial continuously operating reference station (CORS) in Hubei, this paper preliminarily evaluates the network RTK service performance under the new signal system of BDS-3. The results show that single BDS-3 adopts the new signal combination (B1C+B2a) and transition signal combination (B1I+B3I) when providing virtual reference station (VRS) services, the RTK fixation rate of the terminal is above 95%, and the horizontal and elevation accuracies are within 1cm and 2 cm, respectively, which meets the requirements of providing high-precision network RTK services by a single BDS-3 system. In addition, the positioning accuracy of BDS-2 is relatively poor, while the accuracy of BDS-3 is better than global positioning systems (GPS) and BDS-2. The combined processing effect of the B1I+B3I transition signal of BDS-2/3 is optimal, the accuracy of E and N directions is better than 0.5 cm, and the accuracy of U direction is better than 1.5 cm. It can be seen from the atmosphere correction accuracy, regional error modeling accuracy, and network RTK terminal positioning accuracy that the service effect of the B1C+B2a combination is slightly better than that of the B1I+B3I combination. When a single BDS-3 constellation provides network RTK services, it is recommended to use the B1C+B2a combination as the main frequency solution, and when the BDS-2/3 constellation provides service, it is recommended to use the B1I+B3I combination as the main frequency solution.

Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination

The intensity of the reflected measuring beam is greatly reduced for laser-Doppler vibrometer (LDV) measurements on rough surfaces since a considerable part of the light is scattered and cannot reach the photodetector (laser speckle effect). The low intensity of the reflected laser beam leads to a so-called signal dropout, which manifests as noise peaks in the demodulated velocity signal. In such cases, no light reaches the detector at a specific time and, therefore, no signal can be detected. Consequently, the overall quality of the signal decreases significantly. In the literature, first attempts and a practical implementation to reduce this effect by signal diversity can be found. In this article, a practical implementation with four measuring heads of a Multipoint Vibrometer (MPV) and an evaluation and optimization of an algorithm from the literature is presented. The limitations of the algorithm, which combines velocity signals, are shown by evaluating our measurements. We present a modified algorithm, which generates a combined detector signal from the raw signals of the individual channels, reducing the mean noise level in our measurement by more than 10 dB. By comparing the results of our new algorithm with the algorithms of the state-of-the-art, we can show an improvement of the noise reduction with our approach.

Three-dimensional acoustic emission source localisation in concrete based on sparse least-squares support vector regression

In order to improve the accuracy of concrete damage localisation based on acoustic emission (AE) monitoring, a multi-output sparse least-squares support vector regression (S-LS-SVR) method is attempted for AE source localisation in concrete. The AE events are produced by pencil lead breaks and the response wave is received by piezoelectric sensors. A Newton iterative method, an improved exhaustive method and two S-LS-SVR approaches (S-LS-SVR(A) and S-LS-SVR(B)) are used to locate the AE source, then the positioning accuracies of the methods in the three coordinate directions are compared and analysed. The results show that the accuracy of AE source localisation using the S-LS-SVR(B) model is higher than that of the other methods. The accuracy of the S-LS-SVR model using the time difference of arrival (TDOA) and the sequential number of sensors that arrive successively as input parameters is higher than that of the other AE signal combination trialled as the input. This shows that the S-LS-SVR(B) model is better than the S-LS-SVR(A) model. The intelligent S-LS-SVR(B)-based localisation method provides a basis for application in actual damage detection.

Using Orthogonal Combined Signals in Broadband ADCP for Improving Velocity Measurement

Broadband Acoustic Doppler Current Profiler (BBADCP) is a widely used technology in velocity measurements. To adapt to the varied water environment and different measurement requirements, flexible tuning of transmitted signal parameters will improve the feasibility and accuracy of velocity measurement. Compared with the conventional signal, the orthogonal combined signal designed in this paper can generate a wealth of signal combination examples and improve the accuracy of the velocity measurement under the same conditions. The proposed orthogonal combined signal consists of two orthogonal sub-signals with a symmetrical spectrum. Each is designed based on time delay to eliminate or weaken the current velocity ambiguity. Then, the processing method of the received signal when the pulse signals are the same or different coded signal is discussed. The numerical simulation results show that, when using the proposed method, the standard deviation of the estimated current velocity has different degrees of reduction at different current velocities. Our simulation also shows that, compared to the convention method, the proposed method can improve the SNR by 10 dB. This can help significantly increase the scope of the configuration.

On the Choice of the Third-Frequency Galileo Signals in Accelerating PPP Ambiguity Resolution in Case of Receiver Antenna Phase Center Errors

Rapid precise point positioning ambiguity resolution (PPP-AR) is of great importance to improving precise positioning efficiency. There is an expectation that Galileo multi-frequency (three or more frequencies) data processing will offer a promising way to accelerate PPP-AR. However, the performance of different combination observables out of raw Galileo multi-frequency data is still unclear, and the adverse impacts of missing receiver antenna phase center corrections have not been quantified in detail. We therefore studied uncombined Galileo PPP-AR by contrasting three typical triple-frequency combinations, which are E1/E5a/E5b, E1/E5a/E6, and E1/E5/E6 signals, using 30 days of data from 15 stations across Australia. We carried out triple-frequency PPP-AR by separately applying the official GPS receiver antenna phase centers, as currently employed in most relevant literatures, as well as the pilot Galileo receiver antenna phase centers preliminarily measured by the International GNSS Service. We found that, compared to dual-frequency (E1/E5a) PPP-AR, triple-frequency PPP-AR based on E1/E5a/E5b signals shortened the convergence time by only 7.6%, while those based on E1/E5a/E6 and E1/E5/E6 increased unexpectedly the convergence time by 17.6% and 12.7%, respectively, if the GPS receiver antenna corrections were presumed for Galileo signals. However, after using the pilot Galileo phase center corrections, triple-frequency PPP-AR based on E1/E5a/E5b, E1/E5a/E6, and E1/E5/E6 signals could speed up the convergence on average by about 16.2%, 30.3%, and 17.7%, respectively. Therefore, we demonstrate the critical impact of correct Galileo receiver antenna phase centers on multi-frequency PPP-AR convergences. Moreover, the triple-frequency signal combination E1/E5a/E6 is advantageous over others in achieving rapid triple-frequency Galileo PPP-AR.

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.