In order to overcome the shortcomings of the Beihang University of Aeronautical and Astronautics inertial terrain-aided navigation II (BITAN II) algorithm, which is based on extended Kalman filtering, a novel algorithm combined with the terrain contour matching (TERCOM) algorithm and particle filter is proposed. Compared with BITAN II, the proposed algorithm involves a new algorithm framework, acquisition mode, and mode control logic. In particular, the acquisition mode employs TERCOM to implement a consistency voting algorithm, which can quickly and accurately obtain an initial position given in a large initial position error; the mode control logic is designed using the instantaneous information of strapdown inertial navigation system, such that false fix can be effectively detected, and security and accuracy can be guaranteed as well. Experiments are carried out using the digital elevation model and real flight data, and show that, in comparison with BITAN II, the algorithm presented in this paper is reliable and achieves relatively high positioning accuracy in the case of large initial position errors and large altimeter measurement noises.

WiFi fingerprinting-based indoor positioning system (IPS) using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, there are two major bottlenecks that hamper its large-scale implementation. One widely recognized problem is the construction a proper fingerprint database with high efficiency and accuracy. Second is to improve the online positioning accuracy on the basis of the fingerprint database. To address these issues comprehensively, this paper proposes a novel system-DncIPS (Digital navigation center IPS), an IPS that enables automatic online radio map construction and Step-by-step positioning, aiming for the high-accuracy RSS estimation and high-precision positioning. DncIPS can capture WiFi data packets transmitted in WiFi traffic so that obtain the MAC addresses, frequency and RSS of any WiFi access point (AP) at any point. DncIPS use Gaussian process regression model based on a fireworks algorithm to approximate the RSS distribution of an indoor environment and to estimate the location of APs increasing the flexible of DncIPS work environment. This system also consists of a coarse localizer detecting the outliers and dividing clustering area and a fine localizer followed to improve the online positioning accuracy. Extensive experiments results indicate the proposed system DncIPS leads to improvement on radio map updating and localization accuracy.

For precise point positioning (PPP), Kalman filter is usually used to estimate the position and velocity parameters. But, the positioning accuracy and convergence time are highly susceptible to gross errors in observations and dynamic model errors in motion states. In addition, the weights of different types of observation are generally determined by the prior variance are usually inaccurate, which affects the performance of PPP. In order to achieve a more desirable positioning solutions, an improved robust adaptive filtering method is proposed. First in this method, classification robust equivalent weight model based on t-test statistic is developed to construct the equivalent weight matrix for each types of observation separately. Second, variance component estimation weighting method based on innovations is proposed to determine the scale factor of each types of observation. Finally, adaptive factor function model based on innovations is adopted to acquire the optimal adaptive factor. The availability of the proposed classification robust equivalent weight model and variance component estimation weighting method are verified with the static test data, and the performance of the improved robust adaptive filtering method is further validated with the dynamic test data. The results indicate that compared with the conventional robust adaptive filter, the improved one has a better performance in positioning accuracy, convergence time, and the stability of PPP.

An attitude control strategy based on Ziegler-Nichols rules for tuning PD (proportional-derivative) parameters of quadrotor helicopters is presented to solve the problem that quadrotor tends to be instable. This problem is caused by the narrow definition domain of attitude angles of quadrotor helicopters. The proposed controller is nonlinear and consists of a linear part and a nonlinear part. The linear part is a PD controller with PD parameters tuned by Ziegler-Nichols rules and acts on the quadrotor decoupled linear system after feedback linearization; the nonlinear part is a feedback linearization item which converts a nonlinear system into a linear system. It can be seen from the simulation results that the attitude controller proposed in this paper is highly robust, and its control effect is better than the other two nonlinear controllers. The nonlinear parts of the other two nonlinear controllers are the same as the attitude controller proposed in this paper. The linear part involves a PID (proportional-integral-derivative) controller with the PID controller parameters tuned by Ziegler-Nichols rules and a PD controller with the PD controller parameters tuned by GA (genetic algorithms). Moreover, this attitude controller is simple and easy to implement.

For the integrity monitoring of a multi-source PNT (Positioning, Navigation, and Timing) resilient fusion navigation system, a theoretical framework of multi-level autonomous integrity monitoring is proposed. According to the mode of multi-source fusion navigation, the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level. For isolated non-redundant navigation sources, the system level recovery verification model is used. For the isolated multi-redundant navigation sources, the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors, and the isolated fault sensors are verified in real-time. Finally, according to the fault detection and verification results at each level, the observed information in the fusion navigation solution is dynamically adjusted. On this basis, the integrity risk dynamic monitoring tree is established to calculate the Protection Level (PL) and evaluate the integrity of the multi-source integrated navigation system. The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System (INS), Global Navigation Satellite System (GNSS), Long Baseline Location (LBL), and Ultra Short Baseline Location (USBL). The test results show that the proposed method can effectively isolate the fault source within 5 s, and can quickly detect multiple faulty sensors, ensuring that the positioning accuracy of the fusion navigation system is within 5 m, effectively improving the resilience and reliability of the multi-source fusion navigation system.

WiFi fingerprinting-based indoor positioning system is vulnerable to the dynamic environment, which makes the positioning accuracy decrease and the fingerprint map invalid. To address these issues, an accurate indoor positioning system (AIPS) with fingerprint map adaptation is proposed. For online positioning, it treats the received signal strength (RSS) from each access point (AP) individually and can be divided into two steps: 1) coarse and 2) fine positioning. In coarse positioning, a novel clustering algorithm based on ${RSS}$ attenuation is proposed. In fine positioning, signal noise is considered to construct AP ring, and the reference point (RP) contained by the largest number of rings is selected as nearest RP, and then the RPs with larger number are searched out by region growing algorithm to estimate the location of test point (TP). For the fingerprint map adaptation, $K$ -means is adopted to divide APs into two types, based on the number of rings, and to find which APs’ information has been changed by the dynamic environment, and then update them through Gaussian process regression. The experimental results show that the positioning algorithm in AIPS can improve the positioning accuracy compared with other algorithms, and the fingerprint map adaptation scheme in AIPS can reduce the online running time while keeping accuracy.