Visual positioning is a basic component for UAV operation. The structure-based methods are, widely applied in most literature, based on local feature matching between a query image that needs to be localized and a reference image with a known pose and feature points. However, the existing methods still struggle with the different illumination and seasonal changes. In outdoor regions, the feature points and descriptors are similar, and the number of mismatches will increase rapidly, leading to the visual positioning becoming unreliable. Moreover, with the database growing, the image retrieval and feature matching are time-consuming. Therefore, in this paper, we propose a novel hierarchical visual positioning method, which includes map construction, landmark matching and pose calculation. First, we combine brain-inspired mechanisms and landmarks to construct a cognitive map, which can make image retrieval efficient. Second, the graph neural network is utilized to learn the inner relations of the feature points. To improve matching accuracy, the network uses the semantic confidence in matching score calculations. Besides, the system can eliminate the mismatches by analyzing all the matching results in the same landmark. Finally, we calculate the pose by using a PnP solver. Furthermore, we evaluate both the matching algorithm and the visual positioning method experimentally in the simulation datasets, where the matching algorithm performs better in some scenes. The results demonstrate that the retrieval time can be shortened by three-thirds with an average positioning error of 10.8 m.

As a novel hybrid quantum system, cavity optomechanical system shows super strong coupling strength, extremely low noise level and considerable coherent time under superconducting condition. In this paper, we briefly introduce basic principles of cavity optomechanics and cavity optomechanical systems. Meanwhile, we also classify the widely studied cavity optomechanical systems as five categories in their materials and structures. Significant parameters of these optomechanical systems, such as quality factor, mass and vibrating frequency of mechanical oscillator, are listed in detail. Technical merits and defects of these optomechanical systems are summarized. Furthermore, we introduce the research progress of non-classical microwave quantum states preparation by utilizing generalized cavity optomechanical systems, and we also analyze the performance advancements and remaining problems of this preparation method. In the end, we summarize the application cases at present and look forward to the potential application scenarios in the future. Our summary may be helpful for researchers who are focusing on quantum applications in sensing, radar, navigation, and communication in microwave domain.

Focusing on the problem of vulnerable anti-deception and weak anti-jamming ability in classical pseudorandom code correlation ranging, we present a navigation ranging scheme with true random entangled microwave signals. One path of the entangled microwave signals uses for transmitting signal and another path of the signals preserves at locality. Firstly, the target that possesses the function of quantum repeater receives transmitting signal and forwards it to the locality. Then, correlation detection is implemented between the forwarded signal and the local delayed signal. Finally, the target distance can be calculated by delay measurement. We analyze the ranging precision and anti-deception ability. Results indicate that the scheme not only is superior to classical code correlation ranging precision, but also has very strong anti-multipath ability and anti-multipath interference ability, which will enhance the security of the navigation ranging system. Our investigation has significant application prospects.

Scene matching-based navigation is a common method for UAV visual navigation. However, static maps and limited memory onboard pose challenges in balancing localization precision and the effective area of localization for existing methods. To address these issues, this work generates customizable image references onboard and proposes a hierarchical method for scene matching-based navigation. The proposed localization method trains instant NeRF to learn 3D implicit representations offline and generate customizable image references online. Moreover, a coarse-to-fine strategy is employed to enhance the accuracy of localization. Retrieval maps and localization maps are established for the coarse and fine stages, respectively. The experiments demonstrate that our localization achieves a comparable RMSE of less than 1.5m in virtual datasets and 3m in real datasets, indicating its competitive performance.

The entangled state of continuous variables of microwave frequency is an important resource in the field of quantum. In order to apply it to quantum communication protocol and quantum radar, the entanglement between two spatially separated subsystems, namely dual-path entangled quantum microwave is needed. However, for the circuit that generates the entangled quantum microwave, there is no suitable method to indicate whether the quality of the entangled microwave signal is good or not. Aiming at this problem, we put forward a method of evaluating the quality of dual-path entangled quantum microwave signals generated based on von Neumann entropy. The origin of the entangled quantum microwave is that vacuum state signals are transformed into squeezed state signals in driven pump, so in this paper we use a two-mode squeezed vacuum state to describe the formation of dual-path entangled quantum microwave signal, thus providing the function relation between the photon number and the squeezed parameter. In a communication system, the signal-noise ratio is usually used to express the reliability of system. Entropy is a measure of disorder degree in information. If both of them can be made the analogy, the entropy is used to evaluate the proportion of entangled state signals, the quality of original signals will be evaluated and the relationship among the entropy and squeezed parameter and the photon number will be analyzed. The simulation results show that the photon number in the entangled quantum microwave signal is determined by the squeezed parameter, and there is an index change with the square rule between them. Entropy decreases with the increase of squeezed parameter: its minimum value is 0, and its maximum value can be found from 0.9 to 1. The slope of curve is steep near the maximum, which reflects that the influence of squeezed parameter on the degree of entanglement is obvious, and that the range of optimal value choices in squeezed parameter is very narrow. The optimal value of squeezed parameter is dependent on photon number; it increases with the increase of the photon number. Entropy tends to decrease smoothly with the increase of squeezed parameter and it approximately has a negative exponent relation. The photon number in an actual signal is limited, so the limit value of entropy is estimated to be about 65%. The research shows that the quality of the entangled microwave signal can be improved by choosing appropriate squeezed parameter in different circuits that generate dual-path entangled quantum microwave signals for meeting the actual needs. Therefore, the research can provide the method of choosing the parameters of dual-path entangled quantum microwave circuit and improve the availability of system.

Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.