Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter

Ghaleb, Fuad A.; Zainal, Anazida; Rassam, Murad A.; Abraham, Ajith

doi:10.1016/j.pmcj.2017.06.008

Cited by 54 publications

(61 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A robust acquisition algorithm that is aware of such an environment should be used to improve the accuracy and estimate the uncertainty of the acquired data. Although there are many acquisition algorithms in the literature, the improved innovation-based adaptive estimation Kalman filter algorithm proposed in Reference [70] has been found robust for heterogeneous and dynamic noises in vehicular environments. The algorithm in Reference [70], which is called Enhanced Innovation-based Adaptive Estimation Kalman Filter algorithm (EIAE-KF), estimates the measurement noise covariance according to the discrepancy between the prediction and measurement phases of the Kalman filter algorithm in a timely manner.…”

Section: Data Collection Phasementioning

confidence: 99%

“…Although there are many acquisition algorithms in the literature, the improved innovation-based adaptive estimation Kalman filter algorithm proposed in Reference [70] has been found robust for heterogeneous and dynamic noises in vehicular environments. The algorithm in Reference [70], which is called Enhanced Innovation-based Adaptive Estimation Kalman Filter algorithm (EIAE-KF), estimates the measurement noise covariance according to the discrepancy between the prediction and measurement phases of the Kalman filter algorithm in a timely manner. The estimated noise covariance was then used to adaptively adjust the Kalman gain to improve the estimation accuracy along with estimating the uncertainty of the data.…”

Section: Data Collection Phasementioning

confidence: 99%

“…The estimated noise covariance was then used to adaptively adjust the Kalman gain to improve the estimation accuracy along with estimating the uncertainty of the data. For more details about the EIAE-KF algorithm, the readers are referred to the study in Reference [70]. Kalman gain to improve the estimation accuracy along with estimating the uncertainty of the data.…”

Section: Data Collection Phasementioning

confidence: 99%

See 2 more Smart Citations

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

View full text Add to dashboard Cite

Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

show abstract

Section: Data Collection Phasementioning

confidence: 99%

Section: Data Collection Phasementioning

confidence: 99%

Section: Data Collection Phasementioning

confidence: 99%

See 1 more Smart Citation

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The problem of online noise covariance adaptation has been addressed in numerous studies . One of the first and better known methods, innovation‐based adaptive estimation (IAE), was developed by Mehra and later perfected by Mohamed and Schwarz .…”

Section: Introductionmentioning

confidence: 99%

“…The problem of online noise covariance adaptation has been addressed in numerous studies. 2,[5][6][7][8][9][10][11][12] One of the first and better known methods, innovation-based adaptive estimation (IAE), was developed by Mehra 5 and later perfected by Mohamed and Schwarz. 7 Such methods are based on subtracting the expected measurement from the actual one (which is known as the innovation vector), and testing if the innovation covariance resembles the covariance computed by the filter, and adjusting the noise matrices so that the observed and expected covariances match.…”

Section: Introductionmentioning

confidence: 99%

Online evaluation of the process noise covariance matrix for event‐based state estimators

Martinez-Rey

Santos

Nieto

et al. 2019

Numerical Meth Engineering

View full text Add to dashboard Cite

Summary In state estimation, adjusting the process noise covariance matrix is an important and often difficult task. Well‐known methods use the innovation vector to perform an adaptive adjustment, but when using event‐based sensors, the innovation vector is not available for the estimator. We propose an online method for adjusting the process noise covariance matrix using the expected and observed event rates, which is based on the golden section search optimization algorithm. Simulation results confirm the suitability and efficiency of our proposed method. The process noise covariance parameter converges to the actual covariance iteratively, reducing the sensor transmission rate and the estimation error.

show abstract

Q‐learning based adaptive Kalman filtering for partial model‐free dynamic systems

Tang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryIn this article, we propose an adaptive Kalman filtering based on Q‐learning for partial model‐free dynamic systems. First, a cost function is defined to iteratively update the prior state value when the model parameters are unknown. Then, the observations in a period of time are utilized to improve the accuracy and updating speed of the prior state estimation by means of the multi‐innovation least squares. Next, considering that the weight matrix in the cost function will change due to external noise noise and model mismatch, the innovation‐based adaptive estimation algorithm is presented to adjust the weight matrix by using the covariance of the information sequence. Finally, the proposed algorithms are applied to estimate the water level of a quadruple water tank system.

show abstract

Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter

Cited by 54 publications

References 51 publications

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Online evaluation of the process noise covariance matrix for event‐based state estimators

Q‐learning based adaptive Kalman filtering for partial model‐free dynamic systems

Contact Info

Product

Resources

About