Adaptive Fuzzy Augmented H-Infinity Fusion Filter for Target Tracking Applications

Reshma Verma,Shreedarshan K,Lakshmi Shrinivasan

Accurate and uncertainty capable robust designs are required in real time tracking systems. Predominantly adopted Kalman Filter and its variants exhibit unstable performance in certain nonlinear systems and is affected by presence of outliers. Study presented here discusses adoption of modified H Infinity Filters for multi sensor data fusion based target tracking. Fuzzy Logic based H-Infinity Fuzzified Filter (FLHIFF) is presented where in initially fuzzy logic is used at filter level to eliminate local estimation errors and an additional fuzzy system is used to minimize outlier effects and estimation errors during data fusion. Adaptive H-Infinity Filter using Fuzzy Degree of Matching AHIFFDoM developed to limit filter divergence in presence of uncertainties. In AHIFFDoM Degree of Matching (DoM) is computed and in accordance to it process noise covariance matrix is tuned using fuzzy logic to achieve matching. Performance of FLHIFF is compared with classical H Infinity filters for nonmaneuvering targets through simulation results.

Volume 12 | Issue 6

Pages: 1873-1884

DOI: 10.5373/JARDCS/V12I2/S20201392