With the evolution of communication techniques, vehicle-to-vehicle (V2V) communications with low latency and high speed facilitate the study of cooperative positioning (CP) methods for vehicular applications. However, the current CP methods are incapable of bridging the gap between positioning accuracy required for crucial vehicular applications and what CP provides. The CP method with carrier-phase-based Real Time Kinematic (RTK) principle becomes a potential candidate for high-precision positioning. In this letter, we reveal a cooperative navigation filtering (CNF) algorithm fusing Global Navigation Satellite System (GNSS) data and Ultra-wideband (UWB) ranging data to achieve decimeter-level positioning. The main novelty of the proposed CP method is to include centimeter-level GNSS carrier phase information and derive a collaborative ambiguity resolution model. Numerical results and comparisons validate the feasibility and superiority of proposed method.

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

The development of vehicle-to-vehicle (V2V) communication facilitates the study of cooperative positioning (CP) techniques for vehicular applications. The CP methods can improve the positioning availability and accuracy by inter-vehicle ranging and data exchange between vehicles. However, the inter-vehicle ranging can be easily interrupted due to many factors such as obstacles in-between two cars. Without inter-vehicle ranging, the other cooperative data such as vehicle positions will be wasted, leading to performance degradation of range-based CP methods. To fully utilize the cooperative data and mitigate the impact of inter-vehicle ranging loss, a novel cooperative positioning method aided by plane constraints is proposed in this paper. The positioning results received from cooperative vehicles are used to construct the road plane for each vehicle. The plane parameters are then introduced into CP scheme to impose constraints on positioning solutions. The state-of-art factor graph optimization (FGO) algorithm is employed to integrate the plane constraints with raw data of Global Navigation Satellite Systems (GNSS) as well as inter-vehicle ranging measurements. The proposed CP method has the ability to resist the interruptions of inter-vehicle ranging since the plane constraints are computed by just using position-related data. A vehicle can still benefit from the position data of cooperative vehicles even if the inter-vehicle ranging is unavailable. The experimental results indicate the superiority of the proposed CP method in positioning performance over the existing methods, especially when the inter-ranging interruptions occur.

Laundry wastewater contains a large number of surfactants, suspended solids and other pollutants, and the COD value is high. If the wastewater is discharged into the water without harmless treatment, it will cause serious environmental pollution. The electrocoagulation process was selected as a promising environmentally friendly technology for treating domestic laundry wastewater. In this paper, the contribution of the liquid and powder detergents to the composition of domestic laundry wastewater was investigated. Combination of stainless steel and aluminum anode electrodes were compared. The effect of electrolysis time and current density on the removal of COD, LAS and turbidity of liquid detergent and powder detergent laundry wastewater were evaluated. The experimental results showed the electrocoagulation process has significant removal. It was found that the electrolysis time in 25 minute, current density in 5 mA/cm2 had optimum efficiency when considering efficient removal and economic energy consumption. For Liquid detergent laundry wastewater, COD, LAS and turbidity removal rates were 84%, 93% and 96%. For powder detergent laundry wastewater, COD, LAS and turbidity removal efficiency were 80%, 83% and 91%, respectively. Energy consumption in the optimum conditions for liquid detergent and powder detergent laundry wastewater by electrocoagulation were 7.324KWh/m3, 3.642KWh/m3, respectively, while using combination of stainless steel and aluminum anode electrodes equals 1 to 1.

With the large-scale use of BeiDou navigation and 5G technology worldwide, integrating BeiDou navigation and communication has become a hot research topic in navigation and positioning technology. Low-cost, miniaturized, and susceptible mixed-signal receivers will become the future receiver technology development trend. However, the current receiver technology still faces the challenge of further improving the positioning service capability and communication quality, which includes the lack of practical analysis of the compatibility between signals and the lack of mixed-signal processing capability of the receiver baseband key technology. To address these problems, we start by analyzing the signal part of 5G out-of-band signals falling into the BD B1 signal band, conduct a detailed analysis of the mixed signal regime and frequency planning, and design a hybrid receiver architecture compatible with both signals, and propose an SC-PMF-FFT fast capture algorithm based on strong correlation, which takes advantage of the strong correlation of signals broadcast on the BD B1 frequency point from B1I to B1C, and reuses the structure of the CDMA system signal capture algorithm to complete the fast capture of 5G signals using an OFDM system. The experiments show that the method can capture the BeiDou B1 signal with a sensitivity of −154 dBm and a whole constellation capture time of no more than 40 ms with the inlet power of the 5G signal not exceeding −45 dBw.

The sensor-rich vehicle (SRV) aided cooperative positioning (CP) method, which introduces several SRVs into a CP system to aid most common vehicles (CoVs), could further improve the CP performance for V2X applications. However, sometimes part of the CoVs may lose their connections with SRVs in complex urban environments, leading to the degradation of their positioning performance. In this work, we propose a two-step SRV aided CP method based on a node optimization strategy, aiming to fully use the high accuracy of the SRVs in unbalanced node connection scenarios. A node optimization strategy is formulated which firstly uses the SRVs to enhance the positioning of important vehicles (ImVs), whose performance would affect most of the vehicles in the system, by constructing an important sub-cluster (ImC) from the system. Then, the corrected position of the ImVs are used to enhance CP for rest CoVs. Based on the node optimization strategy, a two-step positioning algorithm is designed which firstly uses the centralized framework for ImC to fully improve the positioning performance of ImVs, and then uses the distributed framework for other CoVs. Simulations are conducted, except for one single vehicle, the cluster CP statistics are defined to evaluate the performance of the whole system. Results validate that the proposed method outperforms the existing methods, especially for the CoVs far away from the SRVs. In particular, the performance of the whole system improves more significantly when the SRVs are on the edge of the system or there are less SRVs in the system.