Speed Estimation Research Articles

Multi-model Deep Learning Approaches for Vehicle Speed Estimation

Accurate vehicle speed estimates are critical for traffic monitoring and management. Traditional speed estimation approaches, such as radar guns or pixel displacement methods, are resource-intensive and frequently struggle in real-time or dynamic traffic situations. This paper describes a combination of YOLO for real-time object identification and Long Short-Term Memory (LSTM) networks for vehicle speed prediction that overcomes these constraints. The ability of YOLO to recognize cars with high precision, along with the capability of LSTM in processing sequential data, makes this approach extremely good for calculating speed from video data. Using the VS13 dataset of 400 single-vehicle videos, YOLOV5 detects vehicles and generates bounding box areas for LSTM to estimate speed. The performance across 13 car models averaged 5.79 km/hr RMSE. The accuracy and generalizability of the model can be improved in the future by adding outside variables like weather, road conditions, and driver behavior.

A Model-Based Method Applying Sliding Mode Methodology for SynRM Sensorless Control

In this paper, a new sensorless approach is proposed to address the speed and position estimation of the Synchronous Reluctance Machine (SynRM). The design of the sensorless control algorithm is developed on the basis of the modified SynRM mathematical model employing a simple sliding mode observer (SMO) and a modified EMF observer that are connected in series. All variables of the modified SynRM model are expressed in the arbitrary rotating frame, which is the so-called estimated γδ reference frame. The derived modified rotor flux terms contain angle error information in the form of trigonometric functions. Initially, the modified rotor flux is expressed as a function of saliency and the stator current id, including the angular deviation between the dq and γδ reference frames, which are rotating at synchronous and estimated speeds, respectively. A suitably designed SMO is utilized to estimate the modified stator flux components in the γδ reference frame. Once the SMO operates in sliding mode, the derived equivalent control inputs of the flux/current observer are used to obtain the required angular position and speed information of rotor by means of the modified EMF and Speed/Position observer. Only measures of stator voltages and currents are required for the speed and position estimation. In addition, Lyapunov Candidate Functions (LCFs) have been applied to determine the sliding mode existence conditions and the gains of the modified EMF observer. The SynRM observer–controller system is tested and evaluated in a wide speed range, even at very low speeds, in the presence of torque load disturbances. Simulation results demonstrate the overall efficacy and robustness of the proposed sensorless approach. Moreover, simulation tests verify the fast convergence and high performance of the modified EMF/speed/angle observer.

Coupled reaction-diffusion equations with degenerate diffusivity: wavefront analysis

Abstract We investigate traveling wave solutions for a nonlinear system of two coupled reaction-diffusion equations characterized by double degenerate diffusivity: n t = − f ( n , b ) , b t = [ g ( n ) h ( b ) b x ] x + f ( n , b ) . These systems mainly appear in modeling spatio-temporal patterns during bacterial growth. Central to our study is the diffusion term g ( n ) h ( b ) , which degenerates at n = 0 and b = 0; and the reaction term f ( n , b ) , which is positive, except for n = 0 or b = 0. Specifically, the existence of traveling wave solutions composed by a couple of strictly monotone functions for every wave speed in a closed half-line is proved, and some threshold speed estimates are given. Moreover, the regularity of the traveling wave solutions is discussed in connection with the wave speed.

Drone Localization using Global Navigation Satellite System and Separated Feature Visual Odometry Data Fusion

The localization system is the most important part of the overall drone navigation system. The Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) is the main device commonly used in a drone. However, under certain conditions, GPS or GNSS may not function optimally, such as in situations of signal jamming or enclosed environments. This paper implemented a new approach to address this issue by combining GNSS data with Visual Odometry (VO) through Machine Learning (ML) methods. The followed process consists of three main stages. First, performing speed and orientation estimation using VO. Second, performing left and right feature separation on the images to generate a more stable and robust estimation of speed and rotation. Third, refining speed and orientation estimation by integrating GNSS data through ML-based data fusion. The proposed method strives to enhance drone localization accuracy, despite disruptions or unavailability of GNSS signals. The research results indicate that the introduced method significantly reduces Absolute Translation Error (ATE) compared to utilizing VO or GNSS separately. The average ATE produced reached 4.38 m and an orientation of 8.26°, indicating that this data fusion approach provides a significant improvement in drone localization accuracy, making it reliable in operational scenarios with limited GNSS signals.

Hybrid Reaching Law of Sliding Mode Position-Sensorless Speed Control for Switched Reluctance Motors

This study presents a method to reduce the chattering phenomenon and the reaching time of sliding mode speed control for switched reluctance motors (SRMs). In order to simultaneously reduce the chattering phenomenon and reaching time, we propose a hybrid reaching law (HRL) for sliding mode controller (SMC). Furthermore, to address the absence of a position sensor, a sliding mode observer (SMO) is integrated into the proposed HRL-based SMC, enabling accurate estimation of rotor angle and angular speed. Comprehensive simulation results are conducted to verify the effectiveness of the proposed method in terms of the reduced chattering phenomenon and reaching time.

Cardiac shear wave elastography: assessing the performance of different algorithms for shear wave speed estimation

Abstract Introduction Cardiac Shear Wave Elastography utilizes ultrafast echocardiographic imaging to assess myocardial stiffness through the propagation velocities of shear waves (SWs). Despite advancements, variability in SW speed measurements remains a challenge. Current assessment lacks a gold standard method and still relies on subjective visual assessment with manual measurements leading to inherent variability. Purpose Manual and nine semi-automatic algorithms for measuring SW speed were systematically tested, aiming to reduce variability and enhance reproducibility of the estimations. Methods High-frame-rate echocardiography images (1055±159 Hz) were acquired in parasternal long axis view. Anatomical M-modes were drawn along the septal wall. To visualize the SW, M-modes were color coded for tissue acceleration. SWs then appear as green bands, and their slopes correspond to tissue propagation velocity. SW slopes from healthy volunteers (n=67) and patients (n=53) with different cardiac conditions, covering a wide age range (19-89 years-old) were included to evaluate the performance of the methods in a clinical context. Measurements were performed manually and with 9 automatic methods: Time-frequency correlation (TFC), Regression window (RW), Radon Transform (RT), and 6 segmentation-based methods (e.g. maximum value). Automatic algorithms were initiated by indicating the position of SW by a mouse click. To assess reproducibility, this procedure was repeated 3 times for different sites along the SW. Failure of SW detection was counted to assess feasibility. Since the visualization of the SW’s and segmentation threshold depends on the selected color scale limits, 3 scales (0.5, 1, 2 m/s²) were tested (Figure 1). Finally, manual measurements served as reference, and agreement analysis was conducted. Results When accessing reproducibility across different starting points, only TFC exhibited variability (SD ±0.3 vs ±0.0 m/s for others). In terms of the color scale settings, RW and RT showed no variability (SD ±0.0 vs ± 0.3 m/s for manual), while Regression Weighted Maximum Value (RMW), emerged as the segmentation method with the least variability (SD ±0.1 m/s). RMW also showed highest feasibility [99%, (RW: 76% and RT: 90%)], correlation (r=0.77, r= 0.72, and r= 0.76 resp.), and agreement with a manual measurements (Figure 2). Conclusion Among the tested algorithms, Regression Weighted Maximum Value (RWM) demonstrated the highest feasibility, correlation and agreement with manual measurements. Our findings suggest this semi-automatic method provides an objective alternative to manual SW velocity estimations.

Comparison of International Methods to Estimate Maximum Speed Through Roundabouts

Roundabout operating speed estimation is a critical aspect of safe roundabout infrastructure design, yet existing international guidelines for speed evaluation exhibit inconsistencies and limited agreement with actual observed speeds at roundabouts. This study provides a comprehensive evaluation of five existing international guidelines and their implications for roundabout safety. This study focused on the discrepancies between estimated and observed fastest path speeds by conducting Passing–Bablok regression analyses of speed estimates evaluated from guidelines from Australia, the Netherlands, the United Kingdom, the USA, and South Africa, and speeds observed at 29 roundabouts, encompassing both single- and double-lane configurations. This study represents the most extensive comparison between methods used in various countries for fastest path analysis and is the first instance of empirical fastest path speed assessment. A widening of the confidence intervals around regression lines at higher speeds indicated poor agreement between estimated and observed speeds. All minimum observed speeds exceeded estimated design speeds for double-lane roundabouts signaling a failure of current design methodologies to sufficiently reduce speeds. The findings of the study uncover a systematic tendency for international guidelines to underestimate operating speeds lower than 60 km/h and overestimate higher speeds, posing significant safety concerns. This discrepancy not only challenges prevailing assumptions about roundabout safety but also highlights the potential for a false sense of security among designers. By shedding light on these discrepancies, insights are gained to inform more accurate speed estimation methods, thereby contributing to improved design practices and sufficient speed reduction at roundabouts.

Evaluating spatiotemporal speed metrics for traffic state estimation on complex urban roads

Conducting city-wide tests of intelligent transport systems (ITS) is a costly endeavor that requires extensive funding and planning. Combined traffic simulations can be utilized to alleviate initial costs, and allow easy parameterization to deduce real-world requirements. In this paper, we aim to contribute to two frontiers in the domain of traffic state estimation (TSE) within an urban environment. First, we introduce speed estimation and different sensor technologies, reiterating the foundations of traffic dynamics. Second, we present a simulation-based assessment utilizing the open-source co-simulation framework Eclipse MOSAIC, alongside a newly developed TSE application suite, executed on top of the BeST Berlin traffic scenario. This evaluation highlights the estimation capabilities of different sensor technologies and speed estimates in an urban scenario, addressing their effectiveness in various city areas.

Rotor Position Estimation Algorithm for Surface-Mounted Permanent Magnet Synchronous Motor Based on Improved Super-Twisting Sliding Mode Observer

In response to the chattering issue inherent in sliding mode observers during rotor position estimation and to enhance the stability and robustness of sensorless control systems for surface-mounted permanent magnet synchronous motors (SPMSM), this study proposes a rotor position estimation algorithm for SPMSM based on an improved super-twisting sliding mode observer (ISTSMO) and a second-order generalized integrator (SOGI) structure. Firstly, the super-twisting algorithm is introduced to design the observer, which effectively attenuates the sliding mode chattering by using continuous control signals. Secondly, SOGI is introduced in the filtering stage, which not only effectively addresses the time delay issues caused by traditional low-pass filters but also enables the observer to extract rotor position information by monitoring only the back electromotive force (back-EMF) signal of the α-phase, thereby simplifying the observer structure. Finally, the proposed scheme is experimentally compared with the traditional sliding mode observer on the YXMBD-TE1000 platform. The experimental results showed that during motor acceleration and deceleration tests, the average speed estimation error was reduced from 141 r/min to 40 r/min, and the maximum position estimation error was reduced from 0.74 rad to 0.29 rad. In load disturbance experiments, the speed variation decreased from 781 r/min to 451 r/min, and the steady-state speed fluctuation was significantly reduced. These results confirm that the proposed observer exhibits superior stability and robustness.

Research on Sensorless FOC Method of PMSM based on ADRC

This paper addresses the issues of low observer estimation accuracy, high speed overshoot, narrow speed range, and poor anti-disturbance performance in traditional sensorless Permanent Magnetic Synchronous Motor (PMSM) Field Oriented Control (FOC) systems. It proposes two control methods combining the Extended Kalman Filter (EKF) observer with Linear Active Disturbance Rejection Control (LADRC) and Nonlinear Active Disturbance Rejection Control (NLADRC). The EKF is used to estimate rotor position and speed, while LADRC and NLADRC controllers compensate for load disturbances in the speed loop. EKF outputs are used for feedforward compensation in the current loop, eliminating the coupling between d-axis and q-axis voltages. Simulation results show that both methods offer strong anti-disturbance performance, a wide speed range, strong speed overshoot suppression, and accurate estimation of motor speed and rotor position, thereby enhancing the operational stability of the PMSM system.

Optical Quantification of Wind‐Wave Breaking and Regional Variations in Different Offshore Seas Using Landsat‐8 OLI Images

AbstractOceanic whitecaps, as indicators of increased air–sea exchange, can be effectively captured by Landsat‐8 Operational Land Imager (OLI) images with a 30‐m spatial resolution. This study used a new adaptive iterative triangle algorithm, which was applied to 400 OLI images synchronized with National Data Buoy Center‐measured wind speeds, covering the different offshore areas of the United States over the last ten years to automatically extract whitecap‐affected pixels. By integrating radiative transfer equations, we calculated the whitecap coverage (W), determined that a 4 km window size is optimal for calculating W, and established a whitecap coverage‐sea surface wind speed model. A sliding window method was used to achieve high‐resolution (100 m) large‐area sea surface wind speed estimations. More importantly, this study provides new insights into the impact of different wind and wave conditions on W and regional variations in the whitecap coverage‐sea surface wind speed model from an optical satellite perspective, which can effectively quantify the wind‐wave breaking and distinguish the regional variations of them in different offshore seas. By comparing with 10‐m resolution data from the Sentinel‐2 Multi‐Spectral Instrument, we further clarified the impact of spatial resolution on the selection of methods for calculating W, revealing scale effects in optical remote sensing of whitecap detection and proposing corresponding W calculation strategies. These results demonstrate the potential of high‐resolution optical remote sensing for monitoring whitecaps, estimating sea surface winds and studying air–sea interactions.

Design of speed calibration and estimation method for UAV MEMS gyroscope

Design of speed calibration and estimation method for UAV MEMS gyroscope

Optimal Speed and Range Estimation Using Dual Linear Frequency Modulated Signals

Optimal Speed and Range Estimation Using Dual Linear Frequency Modulated Signals

Analysis Winds Data from ASCAT and SAR Backscatter using Statistical and Modeling Methods during Tropical Cyclone Anggrek (2024) in Indian Ocean

Tropical cyclones are extreme weather phenomena characterized by strong winds that can cause damage to coastal areas, so accurate measurement of wind speed during tropical cyclones is very important. This study aims to measure the intensity of wind speed during the occurrence of Tropical Cyclone Anggrek in 2024 using microwave data from Synthetic Aperture Radar (SAR) and Advanced Scatterometer (ASCAT), both of which have different wind speeds in each measurement product. The methods used in this study include statistical analysis of wind speed data obtained from both sources, and data adjustment using the CMOD7D-v2 model to achieve consistency between SAR and ASCAT wind speed estimates. The results of the analysis show that this adjustment can reduce the SAR and ASCAT wind errors and show lower bias values. This research is expected to help the use of CMOD7D adjustment for wind speed analysis during tropical cyclones. CMOD7 GMF adjustment can help eliminate wind speed differences between SAR and ASCAT data, the analysis results show that the wind speed bias is reduced by 25.07% on January 27, while on January 29 it is reduced by 4.39%.

Feed-Forward Neural Network-Based Prediction of Flutter Speed of 3 DOF 2D Wing Model

IntroductionThe application of artificial intelligence, particularly neural network models, has expanded throughout various fields of academic and industrial studies, recently. This research focuses to investigate aeroelastic phenomena and predict flutter speeds without expensive computational and experimental methods utilizing these algorithms, specifically the artificial neural network (ANN).MethodsFundamentally, the approach involves an ANN algorithm to navigate the complexities of aeroelastic systems, achieved by layering multiple ANNs. The network's neurons effectively interpret diverse flight data by this structure. The study also incorporates state-space representation and Theodorsen's unsteady aerodynamic theory to create a comprehensive dataset on aeroelastic flutter speed across different wing parameters. In order to forecast the flutter speeds, a feed-forward neural network (FFNN) model, which is an approach of ANN method, containing sigmoid hidden neurons and a linear output neuron has been proposed for the conditions above.ResultsThe proposed model accomplished a regression value of 0.92 for flutter speeds according to the experimental findings. As presented, the suggested FFNN model is successful and can be employed to predict the flutter speed estimation. The findings of the FFNN structure are validated with the previous work for aeroelastic flutter speed analysis in the literature.DiscussionFinally, the findings indicate that the FFNN model formulated in this study is highly accurate in predicting flutter speeds with the accordance of numerical results of aeroelastic structure modeled with unsteady aerodynamic theory. In addition, the correctness of the predicted flutter speeds demonstrates that analyzing without expensive tests and high computational costs is in the near future.

Joint Source Localization and Propagation Speed Estimation Using TDOA with Hypothesized Propagation Speed

Joint Source Localization and Propagation Speed Estimation Using TDOA with Hypothesized Propagation Speed

An Improved SOGI-Higher-Order Sliding Mode Observer-Based Induction Motor Speed Estimation

Abstract This article presents a novel adaptive gain tuning second-order generalised integrator (SOGI)-higher-order sliding mode (HOSM) observer for robust speed estimation for an induction motor’s entire speed range. This article introduces a hyperbolic tangent function and a varying gain exponent that ensures accurate speed estimation under noisy conditions and significantly reduces chattering observed in conventional sliding mode observers (SMOs). The robustness of the proposed speed estimation method is verified through simulations conducted on MATLAB/Simulink R2024a developed by MathWorks, demonstrating its capability to effectively track the motor’s actual speed even under varying load torque conditions, parameter variations and additional sensor noise. The proposed approach’s superiority and robustness were compared with the conventional SOGI-frequency locked loop (FLL) and super twisting algorithm (STA) SMO.

OTFS vs OFDM Waveforms Switching Use based on Mobile Speed Estimation

OTFS vs OFDM Waveforms Switching Use based on Mobile Speed Estimation

Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed.

Continuous-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses.

Community ambulation is essential for maintaining a healthy lifestyle, but it poses significant challenges for individuals with limb loss due to complex task demands. In wearable robotics, particularly powered prostheses, there is a critical need to accurately estimate environmental context, such as walking speed and slope, to offer intuitive and seamless assistance during varied ambulation tasks. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time on an open-source knee and ankle powered prosthesis (OSL). We recruited 11 individuals with transfemoral amputation, with 7 participants used for real-time validation. Our findings revealed two main conclusions: 1) the user-independent (IND) performance across speed and slope was not statistically different from user-dependent (DEP) models in real-time, and did not degrade compared to its offline counterparts, 2) IND walking speed estimates showed ~0.09 m/s mean absolute error (MAE) and slope estimates showed ~0.95° MAE across multi-context scenarios. Additionally, we provide an open-source dataset to facilitate further research in accurately estimating speed and slope using an IND approach in real-world walking tasks on a powered prosthesis.