• Robinson P. Paul
• Dr. Vishvjit K. Thakar
• Dr. Hetal Patel

The goal of this paper is to find the effective state estimation of system with lossy measurement without increasing the computational complexity of the algorithm. Existing methods of state estimation for provides effective estimation with computational complexity and time. Kalman Filter and Particle Filer are fundament algorithms for state estimation of any linear and non-linear system respectively but both have its limitation. To overcome the limitation a scheme of Data Loss Detection Kalman Filter (DLDKF) is proposed. The information obtained by the sensors of the aerial vehicle is processed by the DLDKF module to confirm the accuracy of the sensor measurement and if needed it is filtered with posterior error covariance matrix to get the accurate state estimation based on the threshold decided for the particular application. The results show that the proposed method can effectively estimate the position of the Aerial Vehicle compared to standard Kalman filter under the non-reliable sensor measurements scenario.

Kalman Filter; Particle Filter; state estimation; lossy network; state - measurement update; stochastic stability analysis; hybrid system; covariance matrices; Interactive Multiple Model.

"State Estimation of Stochastic Systems with Random Lossy measurement based on DLDKF Estimation Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.88-97, March-2019, Available :http://www.jetir.org/papers/JETIR1903314.pdf

"State Estimation of Stochastic Systems with Random Lossy measurement based on DLDKF Estimation Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp88-97, March-2019, Available at : http://www.jetir.org/papers/JETIR1903314.pdf