Optimal filters with multiple packet losses and its application in wireless sensor networks.

Area of Science:

• Control Systems Engineering
• Signal Processing
• Wireless Sensor Networks

Background:

• Filtering is crucial for discrete-time stochastic linear (DTSL) and nonlinear (DTSN) systems.
• Unreliable wireless sensor networks (WSNs) introduce challenges like multiple packet losses.
• Existing methods such as the Extended Kalman Filter (EKF) have limitations in these scenarios.

Purpose of the Study:

• To design an optimal linear filter for DTSL systems with packet losses.
• To derive an extended minimum variance filter for DTSN systems with packet losses.
• To evaluate the performance of the proposed filters in unreliable WSNs.

Main Methods:

• Developed a linear optimal filter for DTSL systems using orthogonal principle analysis.
• Derived an extended minimum variance filter for DTSN systems, employing first-order Taylor series approximation.
• Applied and tested the filters in simulated unreliable WSN environments.

Main Results:

• The proposed linear filter for DTSL systems proved feasible and effective.
• The extended minimum variance filter for DTSN systems demonstrated successful application in unreliable WSNs.
• Simulations showed the extended minimum variance filter outperformed the EKF in WSNs.

Conclusions:

• The developed filters effectively address filtering challenges in discrete-time stochastic systems with packet loss.
• The proposed methods offer a viable solution for reliable data processing in unreliable WSNs.
• The extended minimum variance filter presents a superior alternative to the EKF for nonlinear systems in WSNs.