The architecture of the ultra-tight GPS/INS/PL integration is the key to its successful performance; the main feature of this architecture is the Doppler feedback to the GPS receiver tracking loops. This Doppler derived from INS, when integrated with the carrier tracking loops, removes the Doppler due to vehicle dynamics from the GPS/PL signal thereby achieving a significant reduction in the carrier tracking loop bandwidth. The bandwidth reduction provides several advantages such as: improvement in anti-jamming performance, and increase in post correlated signal strength which in turn increases the dynamic range and accuracy of measurements. Therefore, any degradation in the derived Doppler estimates will directly affect the tracking loop bandwidth and hence its performance. The quadrature signals from the receiver correlator, I (in-phase) and Q (quadrature), form the measurements, whereas the inertial sensor errors, position, velocity and attitude errors form the states of the complementary Kalman filter. To specify a reliable measurement model of the filter for this type of integrated system, a good understanding of GPS/PL signal characteristics is essential. It is shown in this paper that phase and frequency errors are the variables that relate the measurements and the states in the Kalman filter. The main focus of this paper is to establish the fundamental mathematical relationships that form the measurement model, and to show explicitly how the system error states are related to the GPS/PL signals.The derived mathematical relationships encapsulated in a Kalman filter, are tested by simulation and shown to be valid.

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Tracking dynamics on the GPS signal is still a big challenge to the receiver designer as the operating conditions are becoming more volatile. Optimizing the stand-alone system for dynamics generally degrades the accuracy of measurements. Therefore, an inertial navigation system (INS) is integrated with GPS to address this issue. Doppler derived from INS can be used to aid the carrier tracking loop for improving the performance under dynamic conditions. However, the derived doppler does not truly reflect the GPS signal doppler due to errors in inertial sensors. As the tracking loop bandwidth is reduced significantly in ultratightly integrated systems, any offsets in the aiding doppler creates undesired correlations in the tracking loop resulting in sub-optimal performance of the loop. The paper addresses this issue and also provides a mitigating mechanism to reduce the effects of incorrect estimates of the doppler. It is shown that doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed. Mathematical algorithms pertaining to this are provided and the results are summarized. Simulations show that the bias due to aiding doppler offsets could be effectively addressed by appropriate modelling. K E Y W O R D S 1. INS derived Doppler. 2. Correlations. 3. Stochastic Modelling. I N T R O D U C T I O N.Continuous tracking of GPS signals in dynamic scenarios pose a significant challenge for the design of the tracking loops. Optimizing a design to suit a particular scenario will degrade its performance in other scenarios. For instance, increasing the carrier tracking loop bandwidth to receive dynamic signals will inadvertently affect the accuracy of the raw measurements (Jwo, 2001 ;Cox, 1982). Therefore, in a stand-alone GPS receiver, a trade-off design is required to perform optimally in all the scenarios. External sensor integration with the GPS is considered as an alternative to improve upon this, and INS is the ideal choice as it is not only autonomous but also provides attitude at higher data rates. Traditionally, the integration of GPS and INS were carried out in loosely and tightly coupled configurations (Brown & Hwang, 1997). While these systems offer significant advantages over the stand-alone GPS, nevertheless it is imperative to improve the performance wherever possible. With this point of view, the development of integration presently culminated in ultra-tight systems. This type of integration, also called deep level tracking, integrates the I (in-phase) and Q (quadrature) signals

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COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.