1. Introduction
Small fixed-wing unmanned aerial vehicles (UAVs) play an essential role in future air battles and civilian fields due to their low cost, small size, full autonomy, and dense formation. The normal autonomous flight of UAVs is inseparable from the accurate ground velocity and absolute position provided by the Global Navigation Satellite System (GNSS), and especially the real-time kinematic (RTK) measurement can provide centimeter-level position using the technique of dispersion assignment. The traditional navigation mode of the Inertial Navigation System (INS) assisted by GNSS is greatly challenged, since the GNSS is easily interfered with by complex external environments or human factors [
1,
2].
State estimation of UAVs can be defined as the process of tracking the current attitude, velocity, and position of the vehicle [
3]. The design of the motion estimation algorithm is necessary for flight control and is a crucial step in the development of autonomous flying machines. The accuracy of the attitude and heading reference systems (AHRS) have a crucial role. With the continuous improvement in the chip manufacturing process, the current low-cost UAV airborne sensors mainly use microelectromechanical systems (MEMS), which are limited by the performance of low-cost sensors and inevitably introduce interference noise and random noise. The rapid development of modern wireless communication systems provides technical support for various UAV positioning systems [
4], and provides good and fast data communication links for various positioning methods such as the Ultra-Wide Band (UWB) and GNSS, which rely on antenna to receive data [
5]. The traditional GNSS/INS-integrated navigation of UAVs use GNSS positioning measurement information to estimate the bias of the gyroscope, accelerometer, and attitude error, then feedback [
6]. According to the different fusion levels of the GNSS/INS-integrated navigation system, it can be divided into loose integration, tight integration, and deep integration [
7]. This method can improve the accuracy of attitude estimation significantly. However, in GNSS-denied environments, the UAV cannot rely on GNSS positioning information for guidance and control, which has a great impact on the autonomous flight of the UAV.
In recent years, many researchers have been exploring autonomous navigation techniques in GNSS-denied environments to improve positioning and attitude accuracy. Visual-based position does not depend on external equipment support and has high autonomy. Visual and inertial navigation integration technologies have made great strides in recent years. Mourikis et al. [
8] presented an EKF-based algorithm for real-time vision-aided inertial navigation. Tong Qin et al. [
9] proposed a tightly coupled, nonlinear optimization-based method used to obtain highly accurate visual–inertial odometry by fusing preintegrated IMU measurements and feature observations, which successfully solved the navigation problem of rotorcraft in GNSS-denied environments. At present, fixed-wing UAVs have the following difficulties in navigating GNSS-denied environments: (1) High flying speed [
10]: Higher speeds bring higher flight dynamics and vibrations caused by airflow, which puts higher requirements on the UAV damping design. The G-sensitivity of the gyroscope is more difficult to process directly at the algorithm level, and direct inertial integration will cause rapid dispersion of attitude position. (2) The high speed and high altitude of fixed-wing UAVs results in a vast field of view for the airborne camera. Consequently, capturing high-quality feature points becomes more complex, making it hard to apply vision-based inertial navigation algorithms to fixed-wing UAVs directly.
Most commonly used attitude estimation algorithms can be concluded to three kinds: the extended Kalman filter, the gradient descent algorithm, and the complementary filter algorithms. The EKF is more precise in the process of the state error transfer and the bias error, and the process noise of the gyroscope and accelerometer are modeled, thence the error parameters are estimated and compensated by other sensors, but it obviously adds computational complexity [
11]. Leutenegger, S et al. [
12] used an extended Kalman filter estimation framework to replace GPS updates with airspeed measurement under GPS-denied environments. The experiment demonstrates that the position error enlarges linearly with time. Despite its widespread use in UAV navigation, the EKF is subject to limitations. The local linearization of the process dynamic models and measurement models for feature points can degrade with the increasing nonlinearity in the system dynamics [
13]. The gradient descent algorithm uses a quaternion representation, allowing the accelerometer and magnetometer data to be used in an analytically-derived and optimized gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative [
14]. Based on the principle of dual vector gravity and magnetic field fixation in the complementary filters algorithm, the accelerometer data is considered as an approximate observation of the local gravity vector, and the accumulated error of the heading angle is corrected by magnetic field measurements [
15,
16]. Mahony, R. et al. [
15] first proposed and established the complementary filter on the special orthogonal group (SO3) and proved the Lyapunov stability to ensure the global stability of the observer error. However, this algorithm is sensitive to nongravitational acceleration, which may lead to the wrong attitude correction in maneuvering environments. In [
17,
18], the GPS velocity measurement was used to establish the model of the nongravitational acceleration of the vehicle, and the covariance of the measured noise was increased in the absence of the GPS signal, which did not solve the problem of the accurate estimation of nongravitational acceleration under GPS-denied conditions. Euston, M. et al. [
19] used airspeed measurement in vehicle nongravitational acceleration observation for the first time. By establishing the centripetal force model, the result of the gravity vector observation can be ameliorated, and the accuracy of attitude estimation enhanced under GPS-denied conditions can be maintained for a short time. Unfortunately, a large attitude error will be caused in maneuvering environments since the influence of linear acceleration was not considered in the gravitational acceleration estimation. Moreover, it is only used as attitude estimation without calculating the reliability of the velocity and position estimation. Li, X. et al. [
20] proposed a method to estimate the external acceleration with the purpose of improving navigation performance under dynamic conditions. Marantos et al. [
21] fully combined the visual algorithm and multisensor speed/position estimation with an adaptive complementary filter, which gave the algorithm a low computational complexity.
Compared with the convenience of rotorcraft to deploy intelligent algorithms related to vision, and lidar for simultaneous localization and mapping (SLAM) due to its low speed and more stable flight performance, there has been less work on the navigation and position of low-cost fixed-wing UAVs in the GNSS-denied environments because of the reasons mentioned above. Most of the previous works simply provide stable attitude output for UAVs in denied environments. The main contribution of this paper is to explore the provision of UAV stable state estimation in denied environments. The main work and innovations are as follows: (1) Based on [
19], the filtering model further improves the accuracy of dynamic modeling, and an estimation algorithm of UAV nongravitational acceleration using airspeed and inertial sensors is proposed. We then further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle by combining the magnetometer. (2) Subsequently, the data of the barometer are fused to realize the stable tracking of the UAV in the altitude and lifting rate channels. (3) Aiming at the defect that the horizontal velocity and position errors of UAVs are easy to accumulate, a complementary filter for inertial navigation speed correction using airspeed assistance is designed, which greatly elevates the accuracy of the velocity position estimation of the vehicle.
The framework of the algorithm is shown as
Figure 1. As a fully autonomous navigation solution, the algorithm proposed in this paper has been verified by real flight, which can be used as a key switch to airspeed compensation when GNSS is denied, and thus provides a more stable navigation result.
3. Experiment
The small fixed-wing drone was used for experiments to verify the correct functionality in a practical scenario, and the vehicle is shown in
Figure 4 and the UAV parameters are shown in
Table 2. As an algorithm-verified vehicle, the UAV flight is fully autonomous on route. The flight control system was synthesized on an OMAP-L138 C6000 using the MATLAB/Simulink code generation design tool to build the embedded code. A serial-to-parallel interface (SPI) was developed to connect directly with the ADIS16488B sensor, which consisted of the three-axis gyro, three-axis accelerometer, three-axis magnetometer, and the barometer sensor. This IMU error indicator is shown in
Table 3. We can see in
Figure 2 that the maximum wind speed under this experiment is
.
Both the flight control algorithm and the multisensor fusion algorithm are arranged into the flight control hardware platform. The flow chart of the experimental tasks of the whole system is shown in
Figure 5.
The main parameter values of the algorithm are shown in
Table 4. Since no higher accuracy inertial navigation is applied as the flight attitude reference, the combined GNSS/INS mode is still used for comparison and analysis in the UAV navigation control loop. The algorithm result data of the fused TAS/INS/BARO are saved to the flight log, and the vehicle completes the maneuvers of turning, circling, pulling up, and descending in the air independently to verify the navigation accuracy. Finally, the flight log is read after the vehicle lands for data comparison and analysis. The algorithm proposed in this paper is compared with the two-vector EKF model (denoted as EKF/TAS) proposed by [
16], the centripetal force compensation model fused with airspeed presented in [
19] (denoted as NCF/TAS), and the ACF model with adaptive adjustment weights estimated from external acceleration (denoted as ACF). Since there is little difference between the offline solution and online real-time calculation, we use offline processing to compare the accuracy of different algorithms. The results of the GPS/INS combination are used as true values for error calculation analysis. The results are shown below.
Figure 6 and
Figure 7, respectively, show the Euler angles calculation and attitude error comparison of the four algorithms. It can be seen from
Table 5 and
Figure 7 that the attitude error of the ACF is relatively stable, but it is challenging to correct the attitude error directly with the accelerometer because the external acceleration estimation of the air velocity is not accurate. It is obvious that attitude errors accumulate over time. EKF/TAS, NCF/TAS, and the algorithm proposed in this paper add airspeed measurement into the filter. Due to the influence of wind, higher fluctuations in attitude error can be seen. As EKF/TAS and NCF/TAS algorithms do not consider the effect of linear acceleration, the difference in attitude error among the three is not significant when the UAV flight speed is close to constant. At about 200 s, the UAV began to descend. It can be seen that the pitch error increased rapidly, and the instantaneous maximum reached nearly 11°. In 190 s, it can be seen in
Table 5 that the MAE and RMSE of the roll error are 1.1388° and 1.4195°, the MAE and RMSE of pitch error are 1.1114° and 1.4672°, and the MAE and RMSE of yaw error are 4.7935° and 5.5443°, respectively. The yaw angle preformed worse due to the accuracy of the yaw estimation and was affected by the precision of the magnetometer. (It is difficult to calibrate the magnetic field around the UAV accurately, and the magnetometer is highly susceptible to disturbances from the electromagnetic environment, which makes the heading angle accuracy worse.) Generally, the error of the proposed algorithm is stable. The results suggest that this algorithm can adapt to the attitude estimation of the UAV under flight dynamics.
Figure 8 shows the comparison among the velocity solutions of the different algorithms and RTK truth values. The horizontal velocity of the model in this paper can well track RTK velocity measurement with an error within 2 m/s. The defect of horizontal velocity error accumulated over time can be changed by using complementary filter and integrated airspeed. The vertical channel is integrated with a barometer to gain the maximum error of 0.5 m/s, which has obvious advantages over the other three algorithms.
Figure 9 shows a comparison of the positions settled by RTK and the other four algorithms.
Figure 10 compares the position errors of the different algorithms, while
Figure 11 depicts the comparison between the track solution and the real track. It can be seen that the new algorithm can better track the position of the RTK. The error of 30 s (about 600 m flying) in the horizontal channel flight is within 30 m, the error of 90 s (about 1800 m flying) is within 50 m, and the average error of the height channel is 0.5 m, with higher accuracy than the other three. We also notice that during 140–190 s, the north position error of the proposed algorithm is a little more than the ACF and NCF/TAS. The low-precision INS position error transfer equation is established as Equation (31).
where the
and
is the east and up direction attitude misalignment angle, respectively, which are the main source of north position errors. From
Figure 7 and
Figure 11, after the first turn, the roll angle error caused by random wind disturbance is converted to the north position cumulative error. After 160 s, the adaptive complementary filter in Equation (22) makes sense, and the north error stops growing. The algorithm eliminates the accumulated error caused by attitude misalignment error to position as much as possible.
We also compared the offline computing efficiency of the four algorithms. The host computer with the 3.2 GHz AMD Ryzen 7 5800H CPU was used to run the four models with MATLAB 2021A. The total running time was set to 200 s with each step length of 0.005 s.
Table 6 shows the comparison of the running time and its actual ratio among the different algorithms.
As can be seen from the
Table 6, the adaptive complementary filter algorithm (ACF) has the highest computational efficiency, accounting for only 2.12% of the actual operating time of the algorithm. It is usually a nice choice for a lightweight sensor fusion algorithm. The value of the fusion airspeed nonlinear complementary filter (NCF/TAS) is 3.21%. Due to differential tracking of airspeed data and other algorithm modules, the ratio of the running time in the proposed algorithm is 4.09%, slightly higher than ACF and NCF/TAS. Although the computation time is slightly longer, this processing improves the navigation accuracy, and this algorithm can be deployed in our flight control equipment for real autonomous flight verification. As for the EKF algorithm, it requires several high-dimensional matrix operations and is not superior in operational efficiency, accounting for 6.31%. The proposed algorithm consumes fewer computing resources than the EKF/TAS and can provide the higher precision attitude, position, and speed solutions than the EKF/TAS algorithm. The performance of the fusion algorithm is very satisfactory.
4. Conclusions
This paper proposes a robust and universal sensor fusion algorithm, including an IMU, barometer, magnetometer, and airspeed sensor. The contributions of this paper include the following: (1) We use airspeed to improve the estimation accuracy of the nongravitational acceleration of vehicle, subsequently, to optimize the nonlinear complementary filter model of the vehicle’s attitude misalignment angle based on observability derivation, which can adapt to the state estimation accuracy of the UAV under different maneuvers. In the flight test, the MAE and RMSE of roll error are 1.1388° and 1.4195°, the MAE and RMSE of pitch error are 1.1114° and 1.4672°, and the MAE and RMSE of yaw error are 4.7935° and 5.5443°, respectively, and, when compared with the more commonly used EKF algorithm, is improved. (2) At the level of the horizontal velocity fusion, a complementary filtering model using airspeed correction is established to suppress the accumulated errors caused by the calculation speed of INS. (3) At the height level, the Kalman filter model is designed using barometer data so that the vehicle can obtain the accurate solution of the lift rate and altitude without GNSS. The average error of the height channel is 0.5 m, and the maximum error of the lift rate is 0.5 m/s. This design idea uses a cascade fusion strategy that combines the benefits of an individual systems model using a cascade fusion strategy, combining the advantages of a single system. Compared against the other three conventional methods, the proposed method shows superior performance, providing good attitude velocity and position estimation, even in GNSS-denied environments. In addition, the algorithm proposed in this paper consumes lower computing resources and is suitable for common embedded systems. Taken as a whole, the new approach provides a feasible solution for the navigation and positioning of small UAVs, as much as possible in GNSS-denied environments; however, the result is still not very precise. In further works, we will continue to explore multiple fusion navigation technologies, and explore the use of low-cost camera sensors to enhance the robustness and fault tolerance of navigation systems.