Algorithms for prediction of INS estimated Doppler in Ultra-tight Integration

نویسندگان

  • Ravindra Babu
  • Jinling Wang
چکیده

In ultra-tight GPS/INS integration, a Doppler signal derived from INS navigation data and GPS satellite ephemeris is integrated with the receiver tracking loops to remove the dynamics from the GPS signal. It is well known that this integration architecture provides several advantages such as improved S/N ratio, better ‘immunity’ to interference and jamming signals, lower order tracking loops even in dynamic situations, etc. However, similar to loose and tight integration architectures, this system also requires continuous calibration of INS errors for the Doppler integration to be effective. The lower costs and compact sizes are making the MEMS INS devices attractive; however, these sensors can bridge the GPS outages for only about 10 seconds at most as the errors drift rapidly. This ‘run-away’ characteristic of the MEMS sensors is further exploited in high-dynamic situations where the receiver experiences higher Doppler rates due to the vehicle motion. In such dynamic conditions, the INS-derived Doppler can be used for less than 5 seconds; beyond this the INS data becomes so ineffective that they cannot be used for aiding the tracking loops. Therefore, tracking the GPS signals after the ‘loss-of-lock’ even in integrated environments requires higher order loops and higher tracking bandwidths which generally degrade the tracking performance and subsequently the navigation performance. This paper proposes to address this problem by extrapolating the INS-derived Doppler using the Levinson-Durbin (LD) linear prediction algorithm. The Doppler can be considered to be the output of an Autoregressive (AR) model which consists of only poles. The LD algorithm determines a set of coefficients for this model by solving linear Hermitian Toeplitz equations. The LD algorithm predicts the Doppler by computing the coefficients of higher order filters in terms of partial correlation or correlation coefficients. This algorithm is efficient for linear and relatively ‘less’ non-linear trajectories. Based on the trajectory, the Doppler signal derived from the MEMS INS can be predicted from several seconds to minutes. Thus, the GPS signals can be tracked much faster after the ‘loss-of-lock’ than in the unaided case. Simulation experiments have been conducted to study the ‘effectiveness’ of this Doppler prediction and it is found that the Doppler signal can be predicted to within about 10 to 30% of the expected trajectory.

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تاریخ انتشار 2008