Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

The 24 GHz continuous-wave (CW) Doppler radar sensor (DRS) is widely used for measuring the instantaneous speed of moving objects by using a non-contact approach, and has begun to be used in train-borne movable speed measurements in recent years in China because of its advanced performance. The architecture and working principle of train-borne DRSs with different structures including single-channel DRSs used for freight train speed measurements in railway freight dedicated lines and dual-channel DRSs used for speed measurements of high-speed and urban rail trains in railway passenger dedicated lines, are first introduced. Then, the disadvantages of two traditional speed calibration methods for train-borne DRS are described, and a new speed calibration method based on the Doppler shift signal simulation by imposing a signal modulation on the incident CW microwave signal is proposed. A 24 GHz CW radar target simulation system for a train-borne DRS was specifically realized to verify the proposed speed calibration method for a train-borne DRS, and traceability and performance evaluation on simulated speed were taken into account. The simulated speed range of the simulation system was up to (5~500) km/h when the simulated incident angle range was within the range of (45 ± 8)°, and the maximum permissible error (MPE) of the simulated speed was ±0.05 km/h. Finally, the calibration and uncertainty evaluation results of two typical train-borne dual-channel DRS samples validated the effectiveness and feasibility of the proposed speed calibration approach for a train-borne DRS with full range in the laboratory as well as in the field.

Section: Introductionmentioning

confidence: 99%

Speed Calibration and Traceability for Train-Borne 24 GHz Continuous-Wave Doppler Radar Sensor

Sun

Bai

et al. 2020

“…Based on the Doppler effect, vehicle-borne Doppler radar acquires vehicle speed information using the frequency difference between the emitted electromagnetic waves and the return echoes. This method provides distinct advantages, including high-accuracy, no accumulated errors, continuous output, good anti-interference, and strong autonomy [17,18,19,20]. In addition to normal environmental conditions, Doppler radar can also be applied to rain and snow weather, sand and gravel pavement, and night environment.…”

Section: Introductionmentioning

confidence: 99%

“…Its working effect is not affected by the condition of reflective pavement and vehicle sloshing. For this reason, we innovatively proposed that the alignment on the moving base be achieved for the strapdown inertial navigation system assisted by the vehicle-borne Doppler radar, which can effectively avoid measurement errors caused by wheel-slip or vehicle-sliding [18]. We investigated the alignment scheme and the filter algorithm to achieve not only fast and accurate initial alignment on the moving base but also strong interference resistance and high autonomy.…”

Section: Introductionmentioning

confidence: 99%

An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base

Yang

et al. 2019

In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a strapdown inertial navigation system and Doppler radar, we calculated the dead reckoning, analyzed the error sources of the dead reckoning system, and established an error model. Then the errors of the strapdown inertial navigation system and dead reckoning system were treated as the states. Besides velocity information, attitude information was cleverly introduced into the alignment measurement to improve alignment accuracy and reduce alignment time. Therefore, the first measurement was the difference between the output attitude and velocity of the strapdown inertial navigation system and the corresponding signals from the dead reckoning system. In order to further improve the alignment accuracy, more measurement information was introduced by using the vehicle motion constraint, that is, the velocity output projection of strapdown inertial navigation system along the transverse and vertical direction of the vehicle body was also used as the second measurement. Then the corresponding state and measurement equations were established, and the Kalman filter algorithm was used for assisted alignment filtering. The simulation results showed that, with a moving base, the misalignment angle estimation accuracy was better than 0.5’ in the east direction, 0.4’ in the north direction, and 3.2’ in the vertical direction.

“…With the development of spectral sensor technology, the application of the near-infrared (NIR) sensor has been widely used in many fields [ 1 , 2 , 3 , 4 , 5 , 6 ]. The spectra obtained from the NIR sensor have the potential to extract corresponding feature information for the samples.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Wang

Xie

Yang

et al. 2018

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.