Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper,\nan adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of\nremote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs\nare the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been\nmodeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE)\nhas been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays.\nFirst, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies.\nSecond, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus\nrealized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more\nrobust performance than the FIR estimator using constant optimal horizon size.
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