Low-cost Real-time Tightly-Coupled GNSS/INS Navigation System Based on Carrier-phase Double- differences for UAV Applications

In this paper, a novel three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-enabled passive UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS), which calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem whose goal is to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. To address this problem, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods that use feedforward neural networks to approximate Q functions, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the value decomposition based-MARL scheme with FAS and the proposed AR-MARL method without FAS.

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively.

In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.

This paper presents field tests of challenging flight applications obtained with a new family of lightweight low-power INS/GNSS (inertial navigation system/global satellite navigation system) solutions based on MEMS (micro-electro-mechanical- sensor) machined sensors, being used for UAV (unmanned aerial vehicle) navigation and control as well as for aircraft motion dynamics analysis and trajectory surveying. One key is a 42+ state extended Kalman-filter-based powerful data fusion, which also allows the estimation and correction of parameters that are typically affected by sensor aging, especially when applying MEMS-based inertial sensors, and which is not yet deeply considered in the literature. The paper presents the general system architecture, which allows iMAR Navigation the integration of all classes of inertial sensors and GNSS (global navigation satellite system) receivers from very-low-cost MEMS and high performance MEMS over FOG (fiber optical gyro) and RLG (ring laser gyro) up to HRG (hemispherical resonator gyro) technology, and presents detailed flight test results obtained under extreme flight conditions. As a real-world example, the aerobatic maneuvers of the World Champion 2016 (Red Bull Air Race) are presented. Short consideration is also given to surveying applications, where the ultimate performance of the same data fusion, but applied on gravimetric surveying, is discussed.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

In response to the challenges of posture diversity, motion blur and small target detection in unmanned aerial vehicle (UAV) target detection in long-distance and complex scenarios, this paper proposes an improved UAV target detection model:YOLO11n-SMSH. This model significantly improves the detection performance through four core mechanisms: Firstly, a CRIE module with edge perception integration is introduced in the backbone network to enhance the ability of extracting target edge features; Secondly, a SRA-DFF network with semantic association enhancement capability is used as the neck network to achieve high-quality feature interaction and fusion; Furthermore, the NTTAA detection head is finely designed, and through the weight sharing mechanism and bidirectional parallel task alignment path, the collaboration between classification and localization tasks is effectively strengthened; Finally, the GIoU loss function is introduced, and the boundary box regression is optimized using spatial coverage, improving the model's adaptability to UAV targets. Experimental results on the DUT Anti-UAV dataset show that YOLO11n-SMSH performs excellently. Compared with the baseline model, the accuracy (P), recall rate (R), mAP50 and mAP50-95 have significantly increased by 1.5%, 3.9%, 2.6% and 2.5%, respectively. The experimental results verify the effectiveness of the multi-module collaborative optimization strategy and provide a high-performance solution for UAV target detection tasks in practical applications.

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

This paper exploits the idea of integrating the outputs of several optical flow sensors (OFSs) and a micro-electro-mechanical systems (MEMS) inertial measurement unit (IMU). Several optical flow sensors are fixed on an unmanned aerial vehicle (UAV). Their locations and orientations are different so as to detect the optical flow from different directions. By fusing the optical flow information and inertial navigation system (INS) measurements using an extended Kalman filter (EKF), the INS errors can be estimated and compensated. The integration of optical flow sensors and MEMS-based IMU provides a small size, low cost and self-contained scheme for UAVs navigation at low height above the ground in GPS denied environments. The preliminary simulation results presented in this article illustrate the effectiveness of the proposed integrated navigation scheme.

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

In this paper, we describe the design and performance of a prototype multi-rotor unmanned aerial vehicle (UAV) platform featuring autonomous flying based on an inertial measurement unit (IMU) for use in bluetooth communication environments. Although there has been a fair amount of study on free-flying UAVs with multiple rotors, the more recent trend has been to outfit hexarotor UAVs with gimbals to support various services. This paper introduces the hardware and software systems for very compact and autonomous hexarotors, which can be used for search & rescue and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices, or vision systems. This study included the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling, and simulation of the UAV. Furthermore, the hexarotor UAV with the implemented IMU was connected to a microcontroller unit (ARM-cortex) board. The microcontroller was able to control the rotational speed of the rotors and used the IMU measurements as input signals. A control simulation and experiment with the real system were implemented for the test platform, and the results were evaluated and compared.

The paper deals with the three-dimensional (3D) modelling based on data acquired from 2D laser sensor and IMU (Inertial Measurement Unit) attached to the UAV (Unmanned Aerial Vehicle). The used multi-sensor unit produces 2D scans and provides information about Euler angles or quaternions. These angles are used to describe the UAV orientation in 3D space, more precisely the orientation of the laser sensor. In order to generate 3D occupancy map it is necessary to rotate the laser sensor around its axis yielding the 2D scans being mapped on 3D space using quaternions. Inertial sensor and Sweep LiDAR laser measurements are transmitted to the single board computer Odroid XU4 (SBC). The data fusion was performed under ROS (Robot Operating System) installed on the SBC, producing 3D space points. These points are transmitted over the network to the central computer on which the UAV localization and mapping processes are done within the ROS. The effectiveness of the proposed system for 3D modelling of the UAV environment is verified by experiment.

Aiming at the problem that the unmanned aerial vehicle (UAV) cannot complete the positioning and ranging in the complex background and the signal is interfered, we proposed a monocular measurement scheme of assigning 4 LED fiducials (also called landmarks) with 3 colors in total on the target UAV. Firstly, feature areas are screened out by using high saturation, high brightness and specific hue range of LED in HSV (Hue, Saturation, Value) color space. After morphological processing, the background that does not meet the features is removed. Then, the mapping relationship between target LED fiducials and image fiducials is established by using the average hue of connected domain and special geometric layout. The improved Lambda-Twist is used to find the distance from the target UAV to the center of the camera, and the performance of the UAV recognition and ranging from 90m to 245m is tested. The experimental results show that the method can quickly and accurately measure the distance to the target, and can be used for UAV auxiliary measurement.

In this paper a multi-sensor data fusion technique is applied to aerosonde unmanned aerial vehicle (UAV) model to enhance inertial navigation system. An inertial measuring unit (IMU) error model is built with different error parameters (biasing, scale factor, and noise). Each IMU output is applied to strap down inertial navigation system (INS) algorithm to obtain the navigation information: position, velocity, and attitude (PVA). Multi-sensor data fusion algorithm based on fuzzy c-means clustering (FCM) is used to fuse the IMUs data. The fused output is applied to the INS algorithm to obtain the PVA. The simulation results show the effectiveness of the proposed method in reducing the error in navigation information PVA than using a single IMU.

Unmanned aerial vehicles (UAVs) are widely used for commercial and military purposes. UAVs are mostly dependent on an Inertial Measurement Unit(IMU) for navigation. We have presented an alternate approach to estimate the states of an UAV using only onboard camera which can be either used alone or assimilated with the IMU output to enable reliable, accurate and robust navigation. As UAVs are generally fitted with cameras, they present information rich sensor alternative for obtaining useful information from the craft, with the added benefits of being light weight, small and no extra payload. Assuming a pinhole camera model and perspective projection, the first approach is based on optical flow and horizon detection. In the second method, the craft state is estimated using differential and discrete epipolar constraint with planar constraint assuming the scene is slowly moving. Algorithms results are compared with real time data collected from the IMU.