Manasi Das

Jadavpur University, Electrical Engineering, Senior research fellow

Followers

Following

Co-authors

Public Views

Nuno Garrido

ISCTE - University Institute of Lisbon (ISCTE-IUL)

Armando Marques-Guedes

UNL - New University of Lisbon

Binoy Nair

Amrita Vishwa Vidyapeetham

naveen noronha

Visvesvaraya Technological University

Muhammad Fuady

Md Kafiul Islam

Independent University, Bangladesh

Leonardo Mostarda

Università degli studi di Camerino UNICAM

Yuriy S. Shmaliy

Universidad de Guanajuato

Didier Maquin

University of Lorraine

H.K. Lam

King's College London

InterestsView All (17)

Uploads

Papers by Manasi Das

Q-Adaptive Extended Kalman Filter for Nonlinear Systems with Unknown Noise Statistics

2018 15th IEEE India Council International Conference (INDICON), 2018

A ‘Q’-Adaptive Extended Kalman Filter (QAEKF) has been characterized in this work for nonlinear s... more A ‘Q’-Adaptive Extended Kalman Filter (QAEKF) has been characterized in this work for nonlinear state estimation when process noise covariance (Q) is unknown. Innovation based Direct Covariance Matching technique has been used here for adaptation. An important pragmatic regarding the initialization of Q matrix to start the adaptation process is obtained from this detailed study. Tradeoff of sliding window size, an important tuning parameter required for adaptation, has also been investigated. Nonlinear LEO satellite planar model is considered here to validate the algorithm. Simulation results clearly demonstrate the improved performance of Q-AEKF over traditional non-adaptive Extended Kalman Filter in terms of root mean square error, transient error peak and steady state error.

Adaptive unscented information filter for multiple sensor fusion

Michael Faraday IET International Summit 2015, 2015

An adaptive nonlinear information filter for multiple sensor fusion is proposed in situations wit... more An adaptive nonlinear information filter for multiple sensor fusion is proposed in situations with nonlinear signal models and unknown process noise covariance. The proposed derivative free information filter, based on Unscented Transformation, ensures satisfactory estimation performance by online adaptation of the unknown process noise covariance. Efficacy of the proposed estimator is demonstrated with a well known object tracking problem in a sensor fusion configuration. From the results of Monte Carlo simulation it has been revealed that in the situation when the prior knowledge of the process noise covariance is lacking the performance of the proposed filter is demonstrably superior to its non adaptive version in the context of joint estimation of parameters and states.

Fault Detection and Isolation using an Adaptive Unscented Kalman Filter

IFAC Proceedings Volumes, 2014

An enhanced Fault Detection and Isolation (FDI) technique based on 't' statistical test is presen... more An enhanced Fault Detection and Isolation (FDI) technique based on 't' statistical test is presented here employing a residual based R-Adaptive Unscented Kalman Filter (AUKF). Although the use of AUKF is common for estimation problem, employing AUKF for FDI duty is quite rare. Adaptive Unscented Kalman filtering framework is chosen here due to its derivative free calculations in the algorithm and reportedly better accuracy. Monitoring the 't' statistic of the innovation sequence, measurement faults are detected and corresponding alarm signals are generated here. The presented 't' statistic based FDI technique comprising of consistency checking and abruptness checking is straight forward, demands less computational burden and minimizes false alarms. The superiority of the proposed AUKF based FDI method compared to non adaptive, nonlinear filtering based FDI techniques is demonstrated here by an extensive simulation study executed on a nonlinear LEO satellite planar model. The use of AUKF has provided faster convergence speed and is also found to be advantageous for the situations where the measurement noise statistics are unknown.

Manasi Das

Uploads

Papers by Manasi Das

Log In