Sequential Dual Filter Based Smoothing Framework for Integrated Navigation Systems

In this paper a computational Kalman smoothing algorithm framework -sequential dual filter (SDF) for the filtering of asynchronous measurements is studied. SDF having dual KFs constructs a sequential operating structure to compute the smoothed estimate of the state of an observed system. The performance comparison between SDF and classical R-T-S algorithm -forward backward filter (FBF) is researched with numerical experimentation. The smoother based on SDF can be suitably applied to an integrated navigation system (INS) to correct navigation information at every epoch using asynchronous measurements from digital TV broadcasting signals or other positioning sensors. The simulation results indicate the DTV/GPS integrated navigation system using SDF can be expected to achieve a high score in practical applications.