Multi-sensor-Multi target tracking is an emerging technology which is an essential building block of 4-D trajectory based operation in next-generation air transport system. This study proposes an adaptive algorithm for filtering and fusion of multiple heterogeneous sensors, which includes secondary surveillance radars at different geographical location and ADS-B, whose measurements and sensor characteristics are different from one another. Decentralized fusion architecture based on 4D-dimensional (3-D plus time) Earth-Centered Earth-Fixed (ECEF) common coordinate system is adapted to process the data received asynchronously from multiple heterogeneous sensors. The proposed algorithm, removes sensor bias, by proper sensor registration process using LMS (Least Mean Square) algorithm and thereby increasing the quality of the track. A decentralized Adaptive filter with Decentralized Kalman Filter Fusion (ADKFF) method based on Mahalanobis distance is proposed to carry out the fusion task. This study also makes use of the Down-linked Aircraft Parameters (DAP) which can be obtained from mode-S radar and ADS-B for the computation-ally efficient fusion process. The simulation results indicate that the proposed adaptive filtering algorithm with decentralized Kalman filtering can remove sensor registration error, better tracking performance, eliminating ghost and more accurate position information using different types of surveillance sensors.