scholarly journals Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 351 ◽  
Author(s):  
Jakob Pfeiffer ◽  
Xuyi Wu ◽  
Ahmed Ayadi
Keyword(s):  
Time Delay ◽  
Electronic Control Unit ◽  
Measurement Data ◽  
Control Unit ◽  
Real Data ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Distributed Sensor ◽  
Automotive Electronic ◽  
Interesting Approach

Deviations between High Voltage (HV) current measurements and the corresponding real values provoke serious problems in the power trains of Electric Vehicle (EVs). Examples for these problems have inaccurate performance coordinations and unnecessary power limitations during driving or charging. The main reason for the deviations are time delays. By correcting these delays with accurate Time Delay Estimation (TDE), our data shows that we can reduce the measurement deviations from 25% of the maximum current to below 5%. In this paper, we present three different approaches for TDE. We evaluate all approaches with real data from power trains of EVs. To enable an execution on automotive Electronic Control Unit (ECUs), the focus of our evaluation lies not only on the accuracy of the TDE, but also on the computational efficiency. The proposed Linear Regression (LR) approach suffers even from small noise and offsets in the measurement data and is unsuited for our purpose. A better alternative is the Variance Minimization (VM) approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filter (AFs), introduced by Emadzadeh et al. Unfortunately, AFs do not reach the accuracy and efficiency of VM in our experiments. Thus, we recommend VM for TDE of HV current signals in the power train of EVs and present an additional optimization to enable its execution on ECUs.

scholarly journals Detection of Direction-Of-Arrival in Time Domain Using Compressive Time Delay Estimation with Single and Multiple Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5431
Author(s):  
Youngmin Choo ◽  
Yongsung Park ◽  
Woojae Seong
Keyword(s):  
Time Delay ◽  
Time Domain ◽  
Time Delays ◽  
Matched Filter ◽  
Measurement Data ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Multiple Measurements

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity.

scholarly journals A Novel Adaptive Super-Twisting Sliding Mode Control Scheme with Time-Delay Estimation for a Single Ducted-Fan Unmanned Aerial Vehicle

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 54
Author(s):  
Minh-Thien Tran ◽  
Dong-Hun Lee ◽  
Soumayya Chakir ◽  
Young-Bok Kim
Keyword(s):  
Time Delay ◽  
Sliding Mode Control ◽  
Unmanned Aerial Vehicle ◽  
Sliding Mode ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Mode Control ◽  
Ducted Fan ◽  
Control Scheme ◽  
Aerial Vehicle

This article proposes a novel adaptive super-twisting sliding mode control scheme with a time-delay estimation technique (ASTSMC-TDE) to control the yaw angle of a single ducted-fan unmanned aerial vehicle system. Such systems are highly nonlinear; hence, the proposed control scheme is a combination of several control schemes; super-twisting sliding mode, TDE technique to estimate the nonlinear factors of the system, and an adaptive sliding mode. The tracking error of the ASTSMC-TDE is guaranteed to be uniformly ultimately bounded using Lyapunov stability theory. Moreover, to enhance the versatility and the practical feasibility of the proposed control scheme, a comparison study between the proposed controller and a proportional-integral-derivative controller (PID) is conducted. The comparison is achieved through two different scenarios: a normal mode and an abnormal mode. Simulation and experimental tests are carried out to provide an in-depth investigation of the performance of the proposed ASTSMC-TDE control system.

scholarly journals Methods for broadband signals mutual time delays estimating enhancement

2019 ◽  
Vol 30 ◽  
pp. 03012
Author(s):  
Ilya Grin ◽  
Oleg Morozov
Keyword(s):  
Time Delay ◽  
Computational Efficiency ◽  
Radiation Source ◽  
Time Delays ◽  
Time Delay Estimation ◽  
Ambiguity Function ◽  
Delay Estimation ◽  
Reliability Criterion ◽  
Optimal Processing ◽  
Narrowband Channels

This paper considers methods for estimating the mutual time delay of broadband signals recorded by satellites based multi-position systems for determining the location of a radiation source. All methods considered are based on modified algorithms for calculating the ambiguity function. The presented algorithms are based on the extraction of narrowband channels from the studied signals and their further optimal processing. The reliability criterion for mutual time delay estimation by the presented methods was evaluated. Based on the results and analysis of computational efficiency, viability of methods considered and their modifications was determined.

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