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In dense networks, the tremendous computational complexity and communication overhead of cooperative localisation are the two main bottlenecks that limit practical application. In this study, we introduce a bootstrap percolation scheme into Gaussian message passing-based cooperative localisation for precise positioning, aimed at reducing the system overhead. Considering the uncertainty information and geometric distribution of neighbours, an approximate collinear detection criterion is proposed to detect the possible flip ambiguities in cooperative localisation. According to the detection result and our connection constraint, agents are divided into three categories and are approximated by different distribution families. A message passing rule is designed to control the propagation direction from high precision to low precision, thereby mitigating potential error propagation. Additionally, a layer-by-layer positioning mechanism is established where the agents are located gradually. Analytical and simulation results indicate that when the ranging standard deviation is 0·2 m, 89·3% of the agent nodes can be located within 0·4 m using the proposed algorithm. Compared with the Hybrid Sum-Product Algorithm over A Wireless Network (H-SPAWN), this ratio is increased by 8·4% and the computational complexity is reduced by 72%.

In the Global Navigation Satellite System (GNSS), the multipath error affected by many aspects is the main error source that affects satellite navigation, and it is difficult to establish a much more accurate model to analyze it. Based on the ground multipath reflection model, it firstly deeply studies the influence of GNSS satellite orbit parameters on multipath fading frequency and establishes a multipath signal model related to satellite orbit parameters. Secondly, the influence of carrier phase cycle slip, receiver clock adjustment, and GNSS satellite orbit on multiparameter- (MP-) combined observations is analyzed in detail based on the measured data. Finally, aiming at the common phenomenon of code-carrier divergence in the Beidou system, the elevation-based pseudorange correction model and a sidereal filtering are built to correct the MP errors; experiments with measured data show that there is a fluctuation range reduction of 35.7% after sidereal filtering when the receiver reaches a steady state.

Interconnected three-dimension (3D) networks of novel helical carbon nanotubes (HCNTs) wrapped with reduced graphene oxide nanosheets (HCNTs/rGO) are successfully fabricated via a facile solution of self-assembly method, as well as a robust process for the simultaneous reduction and high N-doping of HCNTs/rGO composites (N-HCNTs/rGO) by photoreduction under NH3 atmosphere. The as-prepared N-HCNTs/rGO are directly employed as binder-free supercapacitor electrodes, and exhibit a highly conductive 3D-interconnected structure (5.85 S cm−1), large surface area (528.9 m2 g−1), low internal resistance (0.5 Ω), and good wettability. As a result, N-HCNTs/rGO show high specific capacitance (368 F g−1), high energy density (12.8 Wh kg−1), and cycling stability (90.7% retention at 1 A g−1 for 5000 cycles) in two-electrode systems. Moreover, the 3D N-HCNTs/rGO hybrid networks exhibit enhanced electrochemical performance in supercapacitors, which combine the synergistic effects of the two carbon nanostructures, enhanced wettability, low internal resistance, and improved ion-diffusion ability, together with the large surface areas of 3D hybrid networks and high-level N-doping. The as-synthesized composite is a potential candidate for flexible and binder-free electrodes for high-performance supercapacitors.

The traditional positioning system based on Global Positioning System (GPS) has provided a lot of convenience for people in the outdoor environment. With the continuous improvement of people's living standards in recent years, the indoor location is more and more popular in intelligent shopping malls and intelligent campus. The traditional GPS location accuracy is limited, so it cannot be used indoor. At present, indoor location technology mainly includes infrared, Bluetooth, Wi-Fi and wireless location which relates to indoor mobile network. Among them, wireless location technology is a research hotspot. Least Squares (LS) method, Chan algorithm and Taylor algorithm based on TDOA in UWB indoor positioning technology are analyzed and tested with dynamic and static data in indoor line-of-sight environment. Results show that the three positioning algorithms can achieve decimeter-level positioning accuracy, and the Taylor algorithm can achieve a 1-decimeter positioning accuracy.

To solve the problem of Ultra-wideband (UWB) TW-TOA ranging accuracy influenced by multipath and non-line-of-sight(NLOS) errors in indoor environment, the performance of UWB signal in indoor NLOS propagation environment is firstly analyzed, and then based on the basic Kalman filter, a two-state Kalman filter algorithm is proposed, which ajusts the Kalman filter gain according to the size of the residual error. If the filter residual is greater than the threshold, the UWB signal is judged as a NLOS signal and the filter gain is set to zero, and replace the current state estimate with a state prediction, otherwise, the basic Kalman filter algorithm is used. Finally, the UWB performance of CM4 in the IEEE802.15.4a channel model is analyzed through simulation, simulation on static nodes and dynamic users in indoors is performed, which verifies the validity and reliability of the dual-state Kalman filter algorithm and can be applied in medical field for patients tracking in real time and so on.

Airborne Single Observer Passive Location has the characteristics of mobility and a wide range impaction, while location method basing on phase difference change rate also has the characteristics of getting a quick location with high precision. Studying the Kalman Filter (KF) applied in Airborne Single Observer Passive Location, it analyzes the principle of the position method basing on phase difference change rate, and introduces the filtering principle and the filtering process of the KF algorithm. The simulation results show that, KF algorithm used in Airborne Single Observer Passive Location has an accurately positioning and rapid convergence.