A Simple and Novel Algorithm for State Estimation of Continuous Time Linear Stochastic Dynamic Systems Excited by Random Inputs
Robinson P. Paul1, Vishvjit Thakar2, Hetal N. Patel3
1Robinson P. Paul, Department of Electronics & Communication, Gujarat Technological University, Ahmedabad, India.
2Vishvjit K. Thakar, Department of Information and Communications Technology, Sank Alchand Patel University, Visnagar, India.
3Hetal N. Patel, Department of Electronics & Communication, Gujarat Technological University, Ahmedabad, India.

Manuscript received on 14 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 3559-3563 | Volume-8 Issue-10, August 2019 | Retrieval Number: J97670881019/19©BEIESP | DOI: 10.35940/ijitee.J9767.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.
Index Terms: Kalman Filter; Particle Filter; state Estimation; Lossy Network; State- Measurement Update; Stochastic Stability Multiple Model.

Scope of the Article: Web Algorithms