The Experts below are selected from a list of 44490 Experts worldwide ranked by ideXlab platform
Mario Terzo - One of the best experts on this subject based on the ideXlab platform.
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Constrained Nonlinear Filter for vehicle sideslip angle estimation withno a priori knowledge of tyre characteristics
Control Engineering Practice, 2018Co-Authors: Salvatore Strano, Mario TerzoAbstract:Abstract The vehicle sideslip angle is one of the most functional feedbacks for the actual control systems of vehicle dynamics. The measurement of the sideslip angle is expensive and unsuitable for common vehicles. Consequently, its estimation is nowadays an important task. This paper focuses on the vehicle sideslip angle estimation adopting a constrained unscented Kalman Filter (CUKF) that takes into account state constrains during the estimation process. State boundaries are useful in real-world applications to prevent unphysical results and to improve the estimator robustness. The proposed technique fully takes into account the measurement noise and Nonlinearities. A vehicle model with single track has been adopted for the design of the estimator. Simulations have been carried out and comparisons with the unscented Kalman Filter (UKF) are illustrated. Performance of the estimators have been checked through the application to experimental data. The results show the goodness of the CUKF, able to give an estimate fully in accordance with the measurement. Moreover, the results show that the CUKF, due to the presence of the boundaries, outperforms the UKF.
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Vehicle sideslip angle estimation via a Riccati equation based Nonlinear Filter
Meccanica, 2017Co-Authors: Salvatore Strano, Mario TerzoAbstract:The vehicle sideslip angle is an important variable that contains information concerning the directional behaviour and stability of vehicles. As a consequence, it represents a very functional feedback for all the actual vehicle dynamics control systems. Since the measurement of the sideslip angle is expensive and unsuitable for common vehicles, its estimation is nowadays an important task. To this aim, several approaches have been adopted and the limits due to the Nonlinear nature of the vehicle system are emerged. In order to overcome these limits, this paper focuses on an alternative Nonlinear estimation method based on the State-Dependent-Riccati-Equation (SDRE). The technique is able to fully take into account the system Nonlinearities and the measurement noise. A single track vehicle model has been employed for the synthesis of the estimator. Simulations have been conducted and comparisons with the largely used Extended Kalman Filter are illustrated. Performance of the estimator have subsequently been verified by means of experimental data acquired with an instrumented vehicle. The results show the effectiveness of the SDRE based technique, able to give an estimated sideslip angle fully in accordance with the measured one.
Salvatore Strano - One of the best experts on this subject based on the ideXlab platform.
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Constrained Nonlinear Filter for vehicle sideslip angle estimation withno a priori knowledge of tyre characteristics
Control Engineering Practice, 2018Co-Authors: Salvatore Strano, Mario TerzoAbstract:Abstract The vehicle sideslip angle is one of the most functional feedbacks for the actual control systems of vehicle dynamics. The measurement of the sideslip angle is expensive and unsuitable for common vehicles. Consequently, its estimation is nowadays an important task. This paper focuses on the vehicle sideslip angle estimation adopting a constrained unscented Kalman Filter (CUKF) that takes into account state constrains during the estimation process. State boundaries are useful in real-world applications to prevent unphysical results and to improve the estimator robustness. The proposed technique fully takes into account the measurement noise and Nonlinearities. A vehicle model with single track has been adopted for the design of the estimator. Simulations have been carried out and comparisons with the unscented Kalman Filter (UKF) are illustrated. Performance of the estimators have been checked through the application to experimental data. The results show the goodness of the CUKF, able to give an estimate fully in accordance with the measurement. Moreover, the results show that the CUKF, due to the presence of the boundaries, outperforms the UKF.
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Vehicle sideslip angle estimation via a Riccati equation based Nonlinear Filter
Meccanica, 2017Co-Authors: Salvatore Strano, Mario TerzoAbstract:The vehicle sideslip angle is an important variable that contains information concerning the directional behaviour and stability of vehicles. As a consequence, it represents a very functional feedback for all the actual vehicle dynamics control systems. Since the measurement of the sideslip angle is expensive and unsuitable for common vehicles, its estimation is nowadays an important task. To this aim, several approaches have been adopted and the limits due to the Nonlinear nature of the vehicle system are emerged. In order to overcome these limits, this paper focuses on an alternative Nonlinear estimation method based on the State-Dependent-Riccati-Equation (SDRE). The technique is able to fully take into account the system Nonlinearities and the measurement noise. A single track vehicle model has been employed for the synthesis of the estimator. Simulations have been conducted and comparisons with the largely used Extended Kalman Filter are illustrated. Performance of the estimator have subsequently been verified by means of experimental data acquired with an instrumented vehicle. The results show the effectiveness of the SDRE based technique, able to give an estimated sideslip angle fully in accordance with the measured one.
Solmaz Niknam - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of analog intermittently Nonlinear Filter in the presence of impulsive noise
IEEE Transactions on Vehicular Technology, 2019Co-Authors: Reza Barazideh, Balasubramaniam Natarajan, Alexei V Nikitin, Solmaz NiknamAbstract:In this paper, an adaptive Nonlinear differential limiter (ANDL) is proposed to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing Nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear Filter when there is no outlier, it exhibits intermittent Nonlinearity in response to IN. Therefore, the structure of the matched Filter in the receiver is modified to compensate the Filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio at the output of the Filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear Filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the Filter time parameter variations, we treat the ANDL as a set of linear Filters where the exact operating Filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified.
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performance analysis of analog intermittently Nonlinear Filter in the presence of impulsive noise
arXiv: Signal Processing, 2018Co-Authors: Reza Barazideh, Balasubramaniam Natarajan, Alexei V Nikitin, Solmaz NiknamAbstract:An Adaptive Nonlinear Differential Limiter (ANDL) is proposed in this paper to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing Nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear Filter when there is no outlier, it exhibits intermittent Nonlinearity in response to IN. Therefore, the structure of the matched Filter in the receiver is modified to compensate the Filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio (SNR) at the output of the Filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear Filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the Filter time parameter variations, we treat the ANDL as a set of linear Filters where the exact operating Filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate (BER) is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified.
Robert Schoelkopf - One of the best experts on this subject based on the ideXlab platform.
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Protocols for optimal readout of qubits using a continuous quantum nondemolition measurement
Physical Review A, 2007Co-Authors: Jay M Gambetta, William A Braff, Andreas Wallraff, S M Girvin, Robert SchoelkopfAbstract:We study how the spontaneous relaxation of a qubit affects a continuous quantum nondemolition measurement of the initial state of the qubit. Given some noisy measurement record $\ensuremath{\Psi}$, we seek an estimate of whether the qubit was initially in the ground or excited state. We investigate four different measurement protocols, three of which use a linear Filter (with different weighting factors) and a fourth which uses a full Nonlinear Filter that gives the theoretically optimal estimate of the initial state of the qubit. We find that relaxation of the qubit at rate $1∕{T}_{1}$ strongly influences the fidelity of any measurement protocol. To avoid errors due to this decay, the measurement must be completed in a time that decrease linearly with the desired fidelity while maintaining an adequate signal to noise ratio. We find that for the Nonlinear Filter the predicted fidelity, as expected, is always better than the linear Filters and that the fidelity is a monotone increasing function of the measurement time. For example, to achieve a fidelity of 90%, the box car linear Filter requires a signal to noise ratio of $\ensuremath{\sim}30$ in a time ${T}_{1}$, whereas the Nonlinear Filter only requires a signal to noise ratio of $\ensuremath{\sim}18$.
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protocols for optimal readout of qubits using a continuous quantum nondemolition measurement
Physical Review A, 2007Co-Authors: Jay M Gambetta, William A Braff, Andreas Wallraff, S M Girvin, Robert SchoelkopfAbstract:We study how the spontaneous relaxation of a qubit affects a continuous quantum nondemolition measurement of the initial state of the qubit. Given some noisy measurement record {psi}, we seek an estimate of whether the qubit was initially in the ground or excited state. We investigate four different measurement protocols, three of which use a linear Filter (with different weighting factors) and a fourth which uses a full Nonlinear Filter that gives the theoretically optimal estimate of the initial state of the qubit. We find that relaxation of the qubit at rate 1/T{sub 1} strongly influences the fidelity of any measurement protocol. To avoid errors due to this decay, the measurement must be completed in a time that decrease linearly with the desired fidelity while maintaining an adequate signal to noise ratio. We find that for the Nonlinear Filter the predicted fidelity, as expected, is always better than the linear Filters and that the fidelity is a monotone increasing function of the measurement time. For example, to achieve a fidelity of 90%, the box car linear Filter requires a signal to noise ratio of {approx}30 in a time T{sub 1}, whereas the Nonlinear Filter only requires a signal to noisemore » ratio of {approx}18.« less
Saeid Saryazdi - One of the best experts on this subject based on the ideXlab platform.
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Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence, 2011Co-Authors: Esmat Rashedi, Hossien Nezamabadipour, Saeid SaryazdiAbstract:This paper is devoted to the presentation of a new linear and Nonlinear Filter modeling based on a gravitational search algorithm (GSA). To do this, unknown Filter parameters are considered as a vector to be optimized. Examples of infinite impulse response (IIR) Filter design, as well as rational Nonlinear Filter, are given. To verify the effectiveness of the proposed GSA based Filter modeling, different sets of initial population with the presence of different measurable noises are given and tested in simulations. Genetic algorithm (GA) and particle swarm optimization (PSO) are also used to model the same examples and some simulation results are compared. Obtained results confirm the efficiency of the proposed method.
