Dynamic short-delay multipath in increasing dense urban canyons is a challenge to the global navigation satellite system (GNSS)-based positioning systems. However, the performance of existing multipath methods for a stand-alone receiver is limited on dealing with short-delay multipath. Vector tracking-based deep coupling with an inertial navigation system (INS) has the potential to suppress multipath errors by tracking direct signals using more accurate and robust positions. However, in the typical deep couplings reported, the unmodeled multipath error is generally regarded as noise with other errors, and consequently, their capacity for multipath suppression is not satisfying. To solve these problems, an improved deep coupling structure with a multipath error estimator (MPEE) is proposed and implemented in two schemes. Different from other methods, this modification creates nearly no extra complexity and computation cost. In this way, the inner correlation caused by multipath interference between channels can be decoupled, and the underlying requirements for modeling the errors accurately to construct vector feedback are fulfilled. Besides, for comparison analysis, a software-defined platform, including different tracking and integration approaches, is designed. Finally, the proposed methods are evaluated in a weak signal, short-delay multipath, dynamic multipath, and the real-life multipath scenarios, and their superiority in positioning, measurement improvement, and tracking robustness are validated.

The superiority of a global navigation satellite system (GNSS)/inertial navigation system (INS) ultra-tight integration navigation system has been widely verified. For those systems with centralized structure based on coherent-accumulation measurements (I/Q), the conversion from I/Q signals to navigation information is implemented by an observation equation. As a result, the model is highly complex and nonlinear, exerting essential influence on system performance. Based on the analysis of previous studies, a novel model and its linearization method are proposed, aiming at the integrity, stability and implicit nonlinear factors. Unlike the one-order precision in the common Jacobian matrix, two-order components are partly reserved in this model, which makes it possible for higher positioning accuracy and better convergence. For the positioning errors caused by ignoring code-loop deviation, a method to approximate code-phase is proposed without introducing new measurements. Consequently, the effect of code error can be significantly reduced, especially when the tracking loops are unstable. In the end, using real-sampled satellite signals, semi-physical experiments are carried out and the effectiveness and superiority of new methods are proved.

A well-designed carrier tracking loop in a receiver of the Global Navigation Satellite System (GNSS) is the premise of accurate positioning and navigation in an aircraft-based surveying and mapping system. To deal with the problems of Doppler estimation in high-dynamic maneuvers, the interest on maximum-likelihood estimation (MLE) is increasing among the academic community. Levenberg-Marquardt (LM) method is usually regarded as an effective and promising approach to obtain the solution of MLE, but the computation of Hessian matrix loads a great burden on the algorithm. Besides, a poor performance on convergency in final iterations is the common failing of LM implementations. To solve these problems, an LM method based on Gauss-Newton and a Quasi-Newton (QN) method based on Hessian approximation are derived, making the computation cost of Hessian decline from O(N) to O( 1 ) . Then, on the basis of these two methods, a closed carrier loop with adaptive LM-QN algorithm is further proposed which can switch between LM and QN adaptively according to a damping parameter. Besides, an ideal LM with super-linear convergence (SLM) is constructed and proved as a reference of the convergence analysis. Finally, through the analyses and experiments using aircraft data, the improvements on computation cost and convergence are verified. Compared with scalar tracking and vector tracking, results indicate a magnitude increase in the precision of LM-QN loop, even though more computation counts are needed by LM-QN.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF-based tracking loops. First, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Second, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other tracking methods in complex dynamic environments.

A well-designed tracking loop is the core of a Global Navigation Satellite System (GNSS) receiver and plays a decisive role in estimating parameters and positioning. To solve the problem that classical scalar tracking may fail to track the received signal frequently in high-dynamic and weak signal environments, novel tracking loops based on optimal estimation are proposed by many researchers. Among them, Kalman-based and vector tracking are expected to have a better performance in improving the precision of measurements and positions. Considering their superiority and promising applications in deeply coupled GNSS/ inertial navigation system, the theoretical analysis and practical implementation are worthy of being further explored. In this paper, an EKF-based tracking loop which uses the outputs of coherent channels directly is proposed. And then, the implementation of a vector tracking frequency-locked and delay-locked loop is further proposed in detail. At last, the performance and superiority of the two methods are tested where a software-defined receiver is constructed and the aircraft data from a GNSS constellation simulator is used. Compared with scalar tracking, a significant improvement on positioning and measuring parameters is achieved by the two methods. And the vector tracking indicates a 42.9% and a increase on the precision of positioning and pseudo-range measuring respectively.

Vector-tracking-based GNSS/INS deep coupling is a promising method for high-precision and robust navigation in urban challenging environments, especially for weak and dense multipath scenarios. However, the research on this topic is far from enough due to the complexity of multipath interference and system structure. Herein, a typical vector delay/frequency-locked loop is introduced. Then, based on this vector tracking loop, two different deep coupled schemes are proposed according to different approaches to estimating the corrections for the inertial navigation system. At last, the experiment based on a vehicular field collection and a software-defined platform was designed in which all the commonly used stand-alone-receiver-based and GNSS/INS-based methods are tested and compared. The results preliminarily illustrate the effectiveness and superiority of the deep coupling methods designed, on positioning and measurement improvement, especially in a weak signal and multipath scenario.