Kalman filter (KF) algorithm is a form of recursive optimal estimations, which utilizes information in time domain with less computational efforts to reduce the system errors. Recently, Kalman filtering technique has become a standard approach for reducing errors in a least squares sense and is widely applied in navigation and positioning fields. Unfortunately, the traditional KF needs accurate statistical information about mobile terminal; otherwise, it will result in lower precision and even divergence. To make up for the deficiency of KF algorithm, this paper presents a novel robust single global position system (GPS) navigation algorithm based on dead reckoning and strong tracking filter (STF), which still has strong tracking ability even when precise knowledge of the system models is not available. The proposed algorithm utilizes adaptive fading factor to adjust the gain matrix in real time to satisfy the orthogonality principle (OP), which indicates all useful information has been extracted from residual. Furthermore, we define residual covariance (RC) as an important index to evaluate the performance of the filtering algorithm and deduce the closed relationship of RCs in KF under the case of: 1) inaccurate system model; 2) erroneous initial value; and 3) abrupt change of states, respectively. We find that the RCs of the KF algorithm under all three above-mentioned cases have a bias compared with desired ones; as a result, they do not conform to the OP and have poor tracking ability, while the RC of STF conforms to the OP due to the fading factor which indicates the tracking performance is greatly improved in the proposed algorithm. The theoretical analysis and simulation results demonstrate that the performance of the proposed single GPS navigation algorithm based on STF outperforms that of the traditional KF algorithm.

Real-time positioning feature is becoming an essential part in a variety of military and civilian applications. In particular, the cooperation of multisensors helps to improve navigation accuracy and the universality of the system. In this article, we propose a novel robust fault-tolerant federated filter based on strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), celestial navigation system (CNS), and doppler velocity log (DVL). In the proposed algorithm, the position information of the GNSS, the velocity information of the DVL, and the attitude information of the CNS are introduced as measurement information to correct the divergence error of SINS. The federated filter is exploited for its flexibility and capability in fault toleration. To amend fault and recover information in local filters, a simplified state Chi-square test (SSCST) is utilized as the fault detection, isolation, and recovery procedure. Meanwhile, a new adaptive information sharing factor algorithm based on the results of SSCST and the residuals between the actual observations and the predicted observations is designed, which can adaptively reflect the performance of each local filter. Experimental results show that the accuracy of the proposed integrated navigation and positioning algorithm can be improved and the system remains stable even when the error occurs.

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

In view of the complementary characteristics of the UAV communication network and satellite communication network, we consider UAVs, satellites, and ground devices to form a space-air-ground integrated network (SAGIN) that can provide service to ground users after a natural disaster. In this scenario, an unmanned aerial vehicle (UAV) is employed as a base station (BS) or mobile edge computing (MEC) server to provide communication/computation services to ground users. In addition, the UAV MEC server can offload computing tasks to a satellite. To achieve the goal of satisfying the users' quality-of-experience (QoE) requirements and simultaneously maximizing the energy efficiency, we investigate the problem of joint optimization of the UAV 3D trajectory with resource allocation. Thus, this paper establishes a joint optimization problem. Given that the above optimization problem contains a fractional multivariate objective function and nonconvex constraints, we solve the optimization problem by using an effective iterative algorithm. The simulation results show that the proposed optimization scheme achieves high energy efficiency.

To mitigate the accuracy precipitous descent problem of the integrated navigation system that combines inertial navigation system (INS) and global positioning system (GPS) under complicated environments, especially in GPS outages, a novel multidimensional hybrid prediction method is proposed. Firstly, a variational mode decomposition - improved wavelet denoising (VMD-IWD) data preprocessing algorithm is developed to reduce the internal noise of the INS, which offers distinct feature data for system integration. Secondly, a temporal dynamic attention neural network (TDANN) is proposed to predict GPS position increments to compensate for the missing navigation information of the system during GPS outages. The TDANN model combines spatial neighborhood aggregation (SNA), Squeeze-Excitation (SE) and time dynamic attention (TDA) modules to establish the nonlinear relationship between the inertial measurement unit (IMU) and GPS. When GPS is available, angular rate, specific force, and position increments are fed into the neural network for training simultaneously. When GPS outages occur, TDANN generates pseudo-GPS signals to correct the positioning accuracy of integrated navigation. Finally, simulation experiments and real road data tests are implemented to evaluate the performance of the proposed methods. The comparison results indicate that the proposed methodology can provide more stable and reliable navigation solution than the other existing machine learning algorithms in the event of GPS outages.