GPS/INS Integration Based on Recursive Least Square Lattice

The integration of both the Global Positioning System (GPS) and Inertial Navigation System (INS) has several navigation and positioning applications. Both systems have their unique features and shortcomings. Therefore, their integration offers robust navigation solution. This paper introduces a novel multi-sensor system integration using Recursive Least Square Lattice (RLSL) filter. The proposed system has a similar structure to the widely used Kalman filter. However, it has the major advantage of working without the need of neither dynamic nor stochastic models. Furthermore, no prior information about the covariance information of INS and GPS is required. The RLSL process includes both the lattice predictor and the joint process estimator. The lattice predictor has a modular structure, which consists of a number of individual stages. Each stage has the appearance of a lattice. This lattice structure allows the simultaneous implementation of one forward and one backward prediction error filters. The forward prediction error is the difference between the input (e.g. INS velocity) and its one-step forward prediction value, which is obtained using a number of past tap inputs equivalent to the filter order. Similarly, the backward prediction error is the difference between the last tap input to the filter and its backward prediction value, which is based on the following tap inputs to the filter. The final forward and backward prediction errors can be determined by moving stage by stage through the lattice predictor. In this study, loosely coupled GPS/INS architecture is adopted and only GPS velocity updates are used. The INS input signal represented by the input sequence has a direct relationship with backward prediction errors. This allows estimating some desired response from a linear combination of the backward prediction errors. The backward prediction errors are uncorrelated and orthogonal random variables, with a diagonal correlation matrix. Therefore, the desired signal (corresponding to the GPS velocity) contained in the input sequence can be estimated throughout the joint process estimator from the backward prediction errors whose tap weights are the regression coefficients, similar to Kalman gain. These regression coefficients are tuned recursively in the update mode utilizing the GPS velocity components. The performance of the proposed RLSL module for GPS/INS integration is examined with a tactical grade system through a field test. The field test was conducted in Calgary (Alberta, Canada) in a land vehicle involving a NovAtel OEM4 GPS receiver and a tactical grade INS system (the Honeywell HG1700). The proposed system is examined during the availability of the GPS signal and with intentionally introduced GPS signal outages. The results indicate that the proposed RLSL system is indeed robust in providing reliable modeless INS/GPS integration module.

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