Unmanned aerial vehicle (UAV) swarms require accurate relative localization to safeguard flight missions in the global navigation satellite system-denied environment due to a lack of absolute position information. The existing work of relative localization faces challenges, such as the ranging information loss and the low localization frequency caused by long-distance ranging and large-scale characteristic of the UAV swarm. This article proposes a clustering-based cooperative relative localization scheme for UAV swarm, which contains a two-level framework: inter/intra-cluster localization. In order to investigate the tradeoff between intracluster cooperation and intercluster packet loss, the clustering-based problem is constructed as a coalition formation game (CFG) model. Given the designed coalition value, preference relation, and the coalition formation principles, it is proved that the proposed CFG model has a Nash stable partition. The designed coalition formation algorithm includes coalition heads and beacon drones selection mechanism. Simulation results show that the proposed CFG algorithms shorten the ranging time compared with global localization and achieve better localization performance (localization error and success rate) than contrast algorithms.

In indoor ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The existing methods, such as support vector machine (SVM), decision tree (DT), k-Nearest Neighbor (KNN), identify LOS/NLOS mainly using time-domain characteristics. However, using only time-domain characteristics cannot achieve satisfactory performance. In this letter, we propose a LOS/NLOS identification method based on Morlet wave transform and convolutional neural networks (MWT-CNN). MWT-CNN is capable of identifying LOS/NLOS in the time-frequency domain. Our simulations are based on the 802.15.4a UWB model and an open-source dataset. The simulation results show that MWT-CNN achieves an accuracy of 100% in the office scenario, 99.89% in the industrial scenario, 96.10% in the residential scenario, and 98.84% in a real experimental scenario. Further simulation results show that MWT-CNN is more suitable to be deployed in static scenarios.

A cooperative integrity monitoring (CIM) algorithm is proposed in this work. Under the CIM architecture, the algorithm can fully exploit the global navigation satellite system (GNSS) data and inter-vehicle measurements data to improve the detection and isolation of faulty measurements due to multipath or non line of sight (NLOS). Taking the advantages of cooperative scheme, a residual decomposition method is used to model the measurement errors into common and specific parts, a greedy search strategy is used to exclude the faulty measurements based on its sub-statistics. Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM). Also, CIM is capable of detecting the faulty outliers in inter-vehicle measurements. The results indicate that CIM can be applied to many existing multi-sensor cooperative positioning algorithms.

This paper proposes a hybrid cooperative positioning (CP) algorithm suitable for vehicular network applications which can fuse the measurements from global navigation satellites, ground stations, signals of opportunity, inter-node ranging from neighbouring vehicles and onboard inertial navigation systems (INS). By applying the framework of generalized approximate message passing (GAMP), the complex CP problem is transformed into an iterative yet lower computational load process. In each iteration, the time recurrence of navigation states and initialization of GAMP computation are conducted based on Kalman filter. The proposed algorithm guarantees the overall positioning performance of multiple vehicles in a hybrid navigation scenario, and improves the robustness and accuracy of CP navigation systems. Simulation results show that the proposed algorithm has better estimation accuracy than traditional CP algorithms, and has 20 times less computational load than the best existing algorithm with equivalent accuracy.

The accuracy of positioning in ultra-wide band (UWB) systems can be improved by identifying the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. At present, machine learning methods have already been extensively applied to NLOS identification. Unfortunately, those traditional methods are incapable of performing well in the cases of small samples and class-imbalance samples. In this letter, a novel method is proposed to identify LOS and NLOS components by utilizing Capsule Networks (CapsNet). As indicated by simulation results, the CapsNet-based method allows LOS and NLOS measurements to be classified with 94.63% accuracy, 95.58% precision, 94.74% recall rate, and a 0.9490 F2-score. In addition, CapsNet is compared against decision tree (DT), least squares vector machine (LS-SVM) and K nearest neighbor (KNN) based on experimental data. The comparison results show that the proposed CapsNet outperforms benchmark methods in the cases of both small samples and classimbalance samples.

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.