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.

Effective path tracking control plays a pivotal role in Unmanned Ground Vehicles (UGVs). However, UGVs systems face difficulties in fast convergence and overshooting when subject to actuator communication burden and execution wear. To improve the actuator wear and achieve fast convergence, this paper proposed a fast natural logarithmic sliding mode (FLnSM) control scheme based on the dynamic event-triggered (DET) mechanism. First, a novel FLnSM is developed to enhance convergence speed and reduce chatter. By employing the fast natural logarithmic function, it is possible to achieve high gains at the equilibrium point of the UGVs system. This method exhibits enhanced tracking accuracy and higher convergence speeds compared to other SMC methods. Secondly, a DET mechanism with an adjustable threshold is introduced to mitigate the communication burden and reduce actuator execution loss. Furthermore, as the update frequency of the controller is reduced, the chatter of the sliding mode surface is effectively diminished. Compared to most existing event-triggering methods with a static threshold, the proposed DET mechanism adaptively updates the triggering threshold online to achieve enhanced resource efficiency and avoid the Zeno phenomenon. With only a few parameters requiring tuning, and given that the natural logarithm function is easy to integrate into many UGVs systems, implementing the proposed DET-FLnSM in UGVs systems becomes effortless. Finally, a composite control scheme that integrates DET with FLnSM is proposed to achieve fast convergence, suppress chattering phenomenon, and reduce communication resource consumption of the UGVs system. The excellent performance of the proposed DET-FLnSM control strategy is demonstrated through numerical simulations and experimental results

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.