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.

This work proposes a new cooperative architecture that using Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and vehicle-to-vehicle (V2V) observations to obtain robust and accurate inter-vehicle state estimation. A new cascade structure of relative filter which consists of float estimator and fixed estimator is presented that can take advantage of both the multi-sensor data and the information from Least-squares ambiguity decorrelation adjustment (LAMBDA). Also, a cooperative baseline estimation method based on multidimensional scaling (MDS) is proposed to further exploit the relative estimation from many other collaborators. Lastly, we combine the cascade relative filter (CRF) with MDS to estimate the relative states cooperatively using a feedback scheme. In the verification part, we use realistic sensor noise and a GNSS signal simulator to obtain inter-vehicle and GNSS measurements for a multiple-vehicle network. In a harsh GNSS scenario, only 51.20% of epochs in RTKLIB software can pass the LAMBDA acceptance test, but our proposed methods can achieve 77.85% (CRF) and 85.05% (CRF/MDS). Referring to the recovery time from a float solution to fixed solution, RTKLIB needs 179.23s in the case of 4 satellites, but only 34.88s (CRF) and 21.39s (CRF/MDS) for the proposed methods. Results show that the proposed CRF has good performance when fusing with IMU and V2V observations, and has a better performance than existing methods. Moreover, the proposed architecture that combines CRF and MDS can have a further improvement, which substantially increases the robustness and accuracy of relative state estimation.

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.

Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF’s local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.

Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.