Strengthening Lattice Kalman Filters: Introducing Strong Tracking Lattice Filtering for Enhanced Robustness

Abstract

This work develops a novel formulation of the lattice Kalman filter (LKF) for enhanced robustness. This novel approach initially integrates the concept of sliding innovation to refine the measurement update phase of the LKF, ensuring that the filter’s innovation is constrained within predetermined bounds; the resultant robust filter is designated as the Bounded Innovation Lattice Kalman Filter (BILF). To enhance its numerical stability and adaptive response to rapid changes in the process model or observational data, a Jacobian-free formulation of BILF with a time-varying bounded layer is first developed and then augmented with the adaptive fading factor strategy, leading to the establishment of a robust estimation method, termed as Strong Tracking LKF (ST-LKF). The developed estimation algorithm, in comparison with several renowned filters, is applied to the real-time estimation of states and output power of a single-machine infinite bus (SMIB) system under significantly noisy conditions. The effectiveness of ST-LKF is rigorously tested against a spectrum of operational conditions, including time-variant step and/or ramp inputs, measurement outliers, and short circuits, encompassing both stable and unstable states. Simulation results validate that the proposed filtering strategy excels in terms of accuracy and robustness when faced with model uncertainties and extreme noise levels, consistently maintaining its performance in estimating states across different designed scenarios.

Keywords

• Adaptive fading factors
• adaptive sliding innovation filter
• dynamic state estimation
• lattice Kalman filter
• robustness
• single machine infinity bus system