A UAV airspeed sensing device and flight control method

Abstract

This invention relates to an airspeed sensing device and flight control method for unmanned aerial vehicles (UAVs), belonging to the field of UAV perception and control technology. The UAV airspeed sensing device of this invention is implemented by a hot-wire-based airspeed sensor and combined with the measurement of an inertial measurement unit (IMU). Data fusion is performed through an extended Kalman filter state estimation method to achieve online UAV speed and position perception. This further solves the local positioning problem of UAVs in special environments such as exploration in enclosed spaces and areas lacking satellite positioning, and improves the effect of flight control.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) sensing and control technology, specifically to an airspeed sensing device and flight control method for a UAV. More particularly, it relates to an airspeed inertial odometry for measuring the airspeed and position of a UAV, and related flight state estimation and control methods. Background Technology

[0002] With the rapid development of drone technology, micro fixed-wing drones are increasingly being used in various special environments, such as enclosed space exploration and areas lacking satellite positioning. In these scenarios, determining the precise location of the drone is crucial to support its safe flight, mission execution, and spatial positioning needs. However, due to the limited payload capacity of micro drones, traditional large-size, high-power, and high-computing-power positioning sensors are difficult to mount, posing a challenge to achieving accurate local positioning. To address this issue, researchers have begun exploring methods to infer the location of drones by measuring the airflow velocity passing through them. This method is called airflow velocity odometry.

[0003] Airspeed odometry calculates a drone's position by measuring its position relative to the surrounding airflow and combining this with dead reckoning or data fusion techniques. Previous research has primarily focused on quadcopter drone platforms, employing long-range sensor deployment to reduce the impact of propeller flow on airspeed measurements. However, even with this approach, the large size and weight of traditional airspeed sensors limit their deployment on micro-drones.

[0004] Therefore, in recent years, researchers have begun to explore the development of smaller, lighter airspeed sensors to meet the needs of micro-UAVs. These new sensors, based on different principles such as calorimetry and ultrasound, aim to provide accurate airspeed measurements, thereby supporting the local positioning of micro-UAVs. Advances in this field offer new possibilities for the application of micro-UAVs in complex environments, while also bringing new challenges and opportunities to the development of UAV positioning technology.

[0005] Furthermore, effectively utilizing sensor-collected data for flight control is another key research area. Unmanned aerial vehicles (UAVs) need to understand their current flight status in real time to perform complex flight missions and maintain stability. This involves a crucial technical aspect—state estimation. State estimation not only provides velocity and attitude information, which is essential for adjusting control inputs and maintaining or changing flight direction and altitude, but is also fundamental to ensuring the safe and efficient operation of UAVs. Therefore, researching and optimizing state estimation algorithms to better interpret sensor data and transform it into reliable flight control information represents a significant challenge and opportunity in the current development of UAV technology. Summary of the Invention

[0006] In view of the above problems, the present invention provides an airspeed sensing device and flight control method for unmanned aerial vehicles (UAVs). The UAV airspeed sensing device is implemented through a hot-wire-based airspeed sensor and combined with measurements from an inertial measurement unit (IMU). Data fusion is performed using an extended Kalman filter (EPF) state estimation method to achieve online UAV speed and position awareness. This further solves the local positioning problem of UAVs in special environments such as exploration in enclosed spaces and areas lacking satellite positioning, and improves the effectiveness of flight control.

[0007] The present invention provides an airspeed sensing device for unmanned aerial vehicles (UAVs), comprising: an airspeed sensor unit 19, an airspeed sensor unit 20, an inertial measurement unit 11, and a data fusion estimation module;

[0008] The airspeed sensor unit 9 and the airspeed sensor unit 10 are connected to the data fusion estimation module via wires.

[0009] The inertial measurement unit 11 is connected to the data fusion estimation module; the inertial measurement unit is used to collect the dynamic motion information of the UAV and obtain acceleration and angular velocity information related to the attitude of the UAV.

[0010] The airspeed sensor unit 9 and the airspeed sensor unit 10 are respectively installed on the two wings of the UAV and are used to generate the temperature difference of multiple thermistors. The three-dimensional airspeed of the UAV is obtained through the temperature difference of the multiple thermistors.

[0011] Preferably, the drone is a micro fixed-wing drone;

[0012] Preferably, the airspeed sensor unit 9 includes: a thermistor unit 2, a protective layer 1, and a flexible substrate 1;

[0013] The first protective layer is connected to the first flexible substrate and is disposed on the outer side of the wing of the UAV;

[0014] The thermistor unit 2 encapsulates multiple thermistor pairs 7;

[0015] The airspeed sensor unit 9 also includes a heater 8; the thermistor unit 2 and the heater 8 are disposed on the protective layer 1.

[0016] The heater 8 is located at the center of the thermistor unit 2;

[0017] Each thermistor pair includes two thermistors; the two thermistors in each thermistor pair are symmetrically arranged with the heater 8 as the center;

[0018] Furthermore, with heater 8 as the center, multiple sets of thermistor pairs 7 are arranged from the inside to the outside on the outer periphery of heater 8; the thermistor pairs 7 are arranged symmetrically on the outer periphery of heater 8 with heater 8 as the center.

[0019] For example, the multiple thermistors are arranged in a cross shape around the heater 8, and are symmetrically arranged in pairs on the outer periphery of the heater, in an orthogonal distribution; the multiple sets of thermistor pairs are disposed on the outer side of the UAV's wing through the protective layer 1 and the flexible substrate 1, and measure the airspeed in two orthogonal directions to obtain the three-dimensional airspeed of the UAV.

[0020] The second airspeed sensor unit 10 includes: a second thermistor unit, a second heater, a second protective layer, and a second flexible substrate; the second airspeed sensor unit is configured in the same way as the first airspeed sensor unit.

[0021] It is understood that the flexible substrate is a flexible printed circuit (FPC) circuit;

[0022] The thermistor is manufactured using micron-level processes and packaged on a flexible printed circuit (FPC) line.

[0023] In this invention, multiple thermistors are arranged in a cross shape on the outer side of the drone's wing to simultaneously measure two orthogonal directions, thereby accurately measuring three-dimensional wind speed. This provides precise measurement capabilities for airspeed direction and magnitude while maintaining the integrity of the drone's shape.

[0024] This invention uses multiple ultra-thin thermistors as the core components of each wind speed sensor unit. They continuously generate heat in the wind field, creating a temperature difference between the upstream and downstream of the wind flow. This temperature difference reflects thermal convection, offsets the influence of ambient heat, and further infers the directionality of airflow, reflecting changes in airspeed.

[0025] Preferably, the data fusion estimation module includes: an analog and / or digital conversion unit, a processor, and a data storage module;

[0026] The processor is connected to the analog and / or digital conversion unit and the data storage module, respectively;

[0027] The plurality of thermistors generate a temperature difference, the temperature difference is converted into voltage data by an analog and / or digital conversion unit, and the voltage data is transmitted to the processor.

[0028] Preferably, the inertial measurement unit 11 includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer; the inertial measurement unit 11 is a sensing element used to output measurement information;

[0029] The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer are connected in parallel; the three-axis accelerometer is connected to the processor;

[0030] The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer output the drone's acceleration and angular velocity, which are then transmitted to the processor; as shown in the attached figure. Figure 3 As shown, the voltage data and the drone's acceleration and angular velocity are used to obtain the drone's position through a data fusion algorithm.

[0031] Preferably, the UAV airspeed sensing device further includes: a sampling module;

[0032] The sampling module includes a thermistor interface 12-1, a thermistor interface 12-2, and a data transmission interface 13;

[0033] Thermistor interface 12-2 is connected to the first airspeed sensor unit and the analog and / or digital conversion unit respectively; thermistor interface 212-2 is connected to the second airspeed sensor unit and the analog and / or digital conversion unit respectively.

[0034] The data transmission interface 13 is connected to the three-axis gyroscope, the three-axis accelerometer, and the three-axis magnetometer;

[0035] Furthermore, the thermistor interface 12-1 and thermistor interface 12-2 are used to transmit the voltage data of airspeed sensor unit 1 and airspeed sensor unit 2 to the data fusion estimation module.

[0036] The data transmission interface 13 is used to transmit inertial measurement information such as the acceleration and angular velocity of the UAV to the data fusion estimation module.

[0037] Furthermore, the processor is a filter data fusion model;

[0038] Furthermore, the filter data fusion model includes: filter one, neural network, extended filter one, filter two, and extended filter two;

[0039] The voltage data is transmitted to the neural network through filter one to obtain the three-dimensional airspeed of the UAV;

[0040] The inertial measurement unit outputs the acceleration and angular velocity of the UAV; these are then transmitted through filter two to extended filter one to obtain attitude data.

[0041] The acceleration and angular velocity of the UAV are transmitted to extended filter one through filter two to obtain attitude data;

[0042] The three-dimensional airspeed and attitude data of the UAV are input into the second extended filter to obtain the position and velocity data of the UAV.

[0043] Furthermore, the filter data fusion model is an extended Kalman filter data fusion model;

[0044] Furthermore, both extended filter one and extended filter two are extended Kalman filters.

[0045] Furthermore, the data fusion estimation module also includes a sampling module;

[0046] The airspeed sensor unit described in this invention is distributed on the leading edge and outer section of the wing, minimizing fuselage obstruction and interference, and improving measurement accuracy.

[0047] The airspeed sensor unit of this invention uses an integrated circuit chip (microcontroller) to connect the acquisition circuit in the integrated circuit chip and the flight controller 14 through a serial interface to establish real-time communication for transmitting data from the airspeed sensor unit. At the same time, a storage log chip is set up for separate analysis of data from the sampling circuit after the flight mission is completed. Based on the voltage data transmitted by the airspeed sensor unit and the actual vector airspeed data measured in motion capture, a neural network is used to establish an airspeed calculation proxy model, which is simplified and deployed into the processor to realize online airspeed data measurement, and further realize position and velocity calculation and flight control.

[0048] Another object of the present invention is to provide a flight control method for an unmanned aerial vehicle (UAV) airspeed sensing device, comprising:

[0049] The voltage data at the current moment is obtained based on airspeed sensor unit 19 and airspeed sensor unit 20.

[0050] The current voltage data is input into the data fusion estimation module's filter and neural network to obtain the corresponding airspeed data;

[0051] The acceleration and angular velocity of the UAV at the current moment are obtained based on the inertial measurement unit 11;

[0052] The current acceleration and angular velocity of the UAV are fused by inputting filter 2 of the data fusion estimation module into extended filter 1 to obtain the attitude data at the current moment.

[0053] The current airspeed data and the current attitude data are input into the extended filter two of the data fusion estimation module and fused to obtain the current position data.

[0054] This invention employs parallel processing to help airspeed inertial odometry better understand the motion state of objects, providing more reliable data support for the attitude control and positioning of UAVs.

[0055] Furthermore, the attitude data is expressed as follows:

[0056]

[0057] in, Here is the attitude data of the UAV at time k. Let K be the predicted attitude data of the UAV at time k. k Let z be the Kalman gain at time k, h be the measurement equation describing the relationship between the airspeed sensor measurement and the UAV state, and z be the Kalman gain at time k. k This is the airspeed sensor unit measurement data at the k-th time, i.e., the voltage data.

[0058] Furthermore, the Kalman gain expression at the k-th time step is:

[0059]

[0060] in, Let H be the prediction covariance at time k, H be the Jacobian matrix of the observation matrix, and R be the measurement noise covariance matrix.

[0061] Furthermore, the predicted attitude data of the UAV at the k-th time moment is expressed as follows:

[0062]

[0063] Where f(·) is the state transition equation, This provides the predicted attitude data for the UAV at time k-1. This is the control input for the UAV at time k-1.

[0064] Furthermore, the prediction covariance of the UAV at the k-th time moment is expressed as follows:

[0065] P k - =AP k-1 A T +Q

[0066] Where A is the Jacobian matrix of the state transition matrix, which is related to the airspeed sensor measurement results, Q is the process noise covariance matrix, and P... k-1 Let be the covariance of the UAV at time k-1.

[0067] Furthermore, the covariance of the UAV at the (k-1)th time step is expressed as follows:

[0068]

[0069] Where I is the identity matrix.

[0070] In this invention, Kalman filtering iteratively updates the system's state estimate by alternating prediction and update steps until it converges to the optimal solution or reaches a certain number of iterations. It achieves system state estimation and prediction through the interaction between a dynamic system model and sensor measurement data. By extending the Kalman filter data fusion model, the odometry will output accurate position and velocity information, which will be transmitted to the flight control system for more accurate flight control.

[0071] This invention employs parallel processing to help airspeed inertial odometry better understand the motion state of objects, providing more reliable data support for UAV attitude control and positioning. By extending the Kalman filter data fusion model, the odometry will output accurate position and velocity information.

[0072] The UAV airspeed sensing device of the present invention is implemented by a hot-wire-based airspeed sensor and combined with the measurement of an inertial measurement unit (IMU). Data fusion is performed by an extended Kalman filter state estimation method to achieve online UAV speed and position awareness. This further solves the local positioning problem of UAVs in special environments such as exploration in enclosed spaces and areas lacking satellite positioning, and improves the effect of flight control.

[0073] Understandably, the local positioning problem includes the fact that when performing specific tasks such as search and rescue, agricultural monitoring, or infrastructure inspection, drones may need to operate with precision within a well-defined small area.

[0074] Compared with the prior art, the present invention has at least the following beneficial effects:

[0075] (1) The present invention designs the airspeed sensor unit according to the shape of the UAV, and provides the UAV with the ability to measure airspeed and position without damaging the shape or adding much extra weight.

[0076] (2) The present invention designs a pose estimation unit to obtain accurate UAV position and velocity data, which is applicable to micro fixed-wing UAVs;

[0077] (3) This invention enables real-time communication with the flight control system and deploys an airspeed inertial odometer online to achieve real-time measurement. Attached Figure Description

[0078] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0079] Figure 1 This is a schematic diagram of the application of the UAV airspeed sensing device on a UAV in an embodiment of the present invention;

[0080] Figure 2This is a schematic diagram of the hardware framework of the UAV airspeed sensing device in an embodiment of the present invention;

[0081] Figure 3 This is a schematic diagram of the calculation method framework for the UAV airspeed sensing device in an embodiment of the present invention;

[0082] Figure 4 This is a schematic diagram of a micro-UAV equipped with an airspeed sensing unit and an inertial measurement unit (IMU) in an embodiment of the present invention;

[0083] Figure 5 This is a schematic diagram showing the arrangement of the airspeed sensor thermistors around the heater in an embodiment of the present invention;

[0084] Figure 6 This is a schematic diagram of the sampling module in an embodiment of the present invention.

[0085] Figure label:

[0086] 1. Flexible substrate, 2. Thermistor unit, 3. Conductive channel, 4. Flexible wire, 5. Packaging interface, 6. Data transmission interface, 7. Thermistor pair, 8. Heater, 9. Airspeed sensor unit one, 10. Airspeed sensor unit two, 11. Inertial measurement unit, 12-1. Thermistor interface one, 12-2. Thermistor interface two, 13. Data transmission interface, 14. Flight controller Detailed Implementation

[0087] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0088] A specific embodiment of the present invention, such as Figure 1-6 This invention discloses an airspeed sensing device and flight control method for unmanned aerial vehicles (UAVs). To illustrate the effectiveness of the proposed method, a specific embodiment is provided below for detailed explanation of the above-mentioned technical solution. The specific implementation steps are as follows:

[0089] The present invention provides an airspeed sensing device for unmanned aerial vehicles (UAVs), comprising: an airspeed sensor unit 19, an airspeed sensor unit 20, an inertial measurement unit 11, and a data fusion estimation module;

[0090] The airspeed sensor unit 9 and the airspeed sensor unit 10 are connected to the data fusion estimation module via wires.

[0091] The inertial measurement unit 11 is connected to the data fusion estimation module; the inertial measurement unit is used to collect the dynamic motion information of the UAV and obtain acceleration and angular velocity information related to the attitude of the UAV.

[0092] The airspeed sensor unit 9 and the airspeed sensor unit 10 are respectively installed on the two wings of the UAV and are used to generate the temperature difference of multiple thermistors. The three-dimensional airspeed of the UAV is obtained through the temperature difference of the multiple thermistors.

[0093] Preferably, the drone is a micro fixed-wing drone;

[0094] Preferably, the airspeed sensor unit 9 includes: a thermistor unit 2, a protective layer 1, and a flexible substrate 1;

[0095] The first protective layer is connected to the first flexible substrate and is disposed on the outer side of the wing of the UAV;

[0096] The thermistor unit 2 encapsulates multiple thermistor pairs;

[0097] The airspeed sensor unit 9 also includes a heater 8; the thermistor unit 2 and the heater 8 are disposed on the protective layer 1.

[0098] The heater 8 is located at the center of the thermistor unit 2;

[0099] Each thermistor pair includes two thermistors; the two thermistors in each thermistor pair are symmetrically arranged with the heater 8 as the center;

[0100] Furthermore, with heater 8 as the center, multiple sets of thermistor pairs 7 are arranged from the inside to the outside on the outer periphery of heater 8; the thermistor pairs 7 are arranged symmetrically on the outer periphery of heater 8 with heater 8 as the center.

[0101] For example, the multiple thermistors are arranged in a cross shape around the heater 8, and are symmetrically arranged in pairs on the outer periphery of the heater, in an orthogonal distribution; the multiple sets of thermistor pairs are disposed on the outer side of the UAV's wing through the protective layer 1 and the flexible substrate 1, and measure the airspeed in two orthogonal directions to obtain the three-dimensional airspeed of the UAV.

[0102] The second airspeed sensor unit 10 includes: a second thermistor unit, a second heater, a second protective layer, and a second flexible substrate; the second airspeed sensor unit is configured in the same way as the first airspeed sensor unit.

[0103] It is understood that the flexible substrate is a flexible printed circuit (FPC) circuit;

[0104] The thermistor is manufactured using micron-level processes and packaged on a flexible printed circuit (FPC) line.

[0105] In this invention, multiple thermistors are arranged in a cross shape on the outer side of the drone's wing to simultaneously measure two orthogonal directions, thereby accurately measuring three-dimensional wind speed. This provides precise measurement capabilities for airspeed direction and magnitude while maintaining the integrity of the drone's shape.

[0106] This invention uses multiple ultra-thin thermistors as the core components of each wind speed sensor unit. They continuously generate heat in the wind field, creating a temperature difference between the upstream and downstream of the wind flow. This temperature difference reflects thermal convection, offsets the influence of ambient heat, and further infers the directionality of airflow, reflecting changes in airspeed.

[0107] Preferably, the data fusion estimation module includes: an analog and / or digital conversion unit, a processor, and a data storage module;

[0108] The processor is connected to the analog and / or digital conversion unit and the data storage module, respectively;

[0109] The plurality of thermistors generate a temperature difference, the temperature difference is converted into voltage data by an analog and / or digital conversion unit, and the voltage data is transmitted to the processor.

[0110] Preferably, the inertial measurement unit 11 includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer; the inertial measurement unit 11 is a sensing element used to output measurement information;

[0111] The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer are connected in parallel; the three-axis accelerometer is connected to the processor;

[0112] The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer output the drone's acceleration and angular velocity, which are then transmitted to the processor; as shown in the attached figure. Figure 3 As shown, the voltage data and the drone's acceleration and angular velocity are used to obtain the drone's position through a data fusion algorithm.

[0113] Preferably, the UAV airspeed sensing device further includes: a sampling module;

[0114] The sampling module includes a thermistor interface 12-1, a thermistor interface 12-2, and a data transmission interface 13;

[0115] Thermistor interface 12-2 is connected to the first airspeed sensor unit and the analog and / or digital conversion unit respectively; thermistor interface 212-2 is connected to the second airspeed sensor unit and the analog and / or digital conversion unit respectively.

[0116] The data transmission interface 13 is connected to the three-axis gyroscope, the three-axis accelerometer, and the three-axis magnetometer;

[0117] Furthermore, the thermistor interface 12-1 and thermistor interface 12-2 are used to transmit the voltage data of airspeed sensor unit 1 and airspeed sensor unit 2 to the data fusion estimation module.

[0118] The data transmission interface 13 is used to transmit inertial measurement information such as the acceleration and angular velocity of the UAV to the data fusion estimation module.

[0119] Furthermore, the processor is a filter data fusion model;

[0120] Furthermore, the filter data fusion model includes: filter one, neural network, extended filter one, filter two, and extended filter two;

[0121] The voltage data is transmitted to the neural network through filter one to obtain the three-dimensional airspeed of the UAV;

[0122] The inertial measurement unit outputs the acceleration and angular velocity of the UAV; these are then transmitted through filter two to extended filter one to obtain attitude data.

[0123] The acceleration and angular velocity of the UAV are transmitted to extended filter one through filter two to obtain attitude data;

[0124] The three-dimensional airspeed and attitude data of the UAV are input into the second extended filter to obtain the position and velocity data of the UAV.

[0125] Furthermore, the filter data fusion model is an extended Kalman filter data fusion model;

[0126] Furthermore, both extended filter one and extended filter two are extended Kalman filters.

[0127] Furthermore, the data fusion estimation module also includes a sampling module;

[0128] The airspeed sensor unit described in this invention is distributed on the leading edge and outer section of the wing, minimizing fuselage obstruction and interference, and improving measurement accuracy.

[0129] The airspeed sensor unit of this invention uses an integrated circuit chip (microcontroller) to connect the acquisition circuit in the integrated circuit chip and the flight controller 14 through a serial interface to establish real-time communication for transmitting data from the airspeed sensor unit. At the same time, a storage log chip is set up for separate analysis of data from the sampling circuit after the flight mission is completed. Based on the voltage data transmitted by the airspeed sensor unit and the actual vector airspeed data measured in motion capture, a neural network is used to establish an airspeed calculation proxy model, which is simplified and deployed into the processor to realize online airspeed data measurement, and further realize position and velocity calculation and flight control.

[0130] Another object of the present invention is to provide a flight control method for an unmanned aerial vehicle (UAV) airspeed sensing device, comprising:

[0131] The voltage data at the current moment is obtained based on airspeed sensor unit 19 and airspeed sensor unit 20.

[0132] The current voltage data is input into the data fusion estimation module's filter and neural network to obtain the corresponding airspeed data;

[0133] The acceleration and angular velocity of the UAV at the current moment are obtained based on the inertial measurement unit 11;

[0134] The current acceleration and angular velocity of the UAV are fused by inputting filter 2 of the data fusion estimation module into extended filter 1 to obtain the attitude data at the current moment.

[0135] The current airspeed data and the current attitude data are input into the extended filter two of the data fusion estimation module and fused to obtain the current position data.

[0136] This invention employs parallel processing to help airspeed inertial odometry better understand the motion state of objects, providing more reliable data support for the attitude control and positioning of UAVs.

[0137] Furthermore, the attitude data is expressed as follows:

[0138]

[0139] in, Here is the attitude data of the UAV at time k. Let K be the predicted attitude data of the UAV at time k. k Let z be the Kalman gain at time k, h be the measurement equation describing the relationship between the airspeed sensor measurement and the UAV state, and z be the Kalman gain at time k. k This is the airspeed sensor unit measurement data at the k-th time, i.e., the voltage data.

[0140] Furthermore, the Kalman gain expression at the k-th time step is:

[0141]

[0142] in, Let H be the prediction covariance at time k, H be the Jacobian matrix of the observation matrix, and R be the measurement noise covariance matrix.

[0143] Furthermore, the predicted attitude data of the UAV at the k-th time moment is expressed as follows:

[0144]

[0145] Where f(·) is the state transition equation, This provides the predicted attitude data for the UAV at time k-1.

[0146] This is the control input for the UAV at time k-1.

[0147] Furthermore, the prediction covariance of the UAV at the k-th time moment is expressed as follows:

[0148] P k - =AP k-1 A T +Q

[0149] Where A is the Jacobian matrix of the state transition matrix, which is related to the airspeed sensor measurement results, Q is the process noise covariance matrix, and P... k-1 Let be the covariance of the UAV at time k-1.

[0150] Furthermore, the covariance of the UAV at the (k-1)th time step is expressed as follows:

[0151]

[0152] Where I is the identity matrix.

[0153] In this invention, Kalman filtering iteratively updates the system's state estimate by alternating prediction and update steps until it converges to the optimal solution or reaches a certain number of iterations. It achieves system state estimation and prediction through the interaction between a dynamic system model and sensor measurement data. By extending the Kalman filter data fusion model, the odometry will output accurate position and velocity information, which will be transmitted to the flight control system for more accurate flight control.

[0154] This invention employs parallel processing to help airspeed inertial odometry better understand the motion state of objects, providing more reliable data support for UAV attitude control and positioning. By extending the Kalman filter data fusion model, the odometry will output accurate position and velocity information.

[0155] The UAV airspeed sensing device of the present invention is implemented by a hot-wire-based airspeed sensor and combined with the measurement of an inertial measurement unit (IMU). Data fusion is performed by an extended Kalman filter state estimation method to achieve online UAV speed and position awareness. This further solves the local positioning problem of UAVs in special environments such as exploration in enclosed spaces and areas lacking satellite positioning, and improves the effect of flight control.

[0156] Understandably, the local positioning problem includes the fact that when performing specific tasks such as search and rescue, agricultural monitoring, or infrastructure inspection, drones may need to operate with precision within a well-defined small area.

[0157] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A UAV airspeed sensing device, characterized in that, include: Airspeed sensor unit 1 (9), airspeed sensor unit 2 (10), inertial measurement unit (11), and data fusion estimation module; the airspeed sensor unit 1 (9) includes thermistor unit 1 (2); the airspeed sensor unit 1 (9), airspeed sensor unit 2 (10), and inertial measurement unit (11) are all connected to the data fusion estimation module; The airspeed sensor unit 1 (9) also includes a heater 1 (8); The heater (8) is disposed on the protective layer. The heater (8) is located at the center of the thermistor unit (2); The first protective layer is disposed on the outer section of the leading edge of the wing of the UAV; the first thermistor unit (2) is disposed on the first protective layer; The thermistor unit 1 (2) encapsulates multiple thermistor pairs (7); Each thermistor pair includes two thermistors; the two thermistors of each thermistor pair are symmetrically arranged with the heater (8) as the center; Multiple thermistors are arranged in a cross shape around heater 1 (8), and are symmetrically arranged in pairs around heater 1, in an orthogonal distribution. The inertial measurement unit (11) is used to collect dynamic motion information of the UAV and obtain acceleration and angular velocity information related to the attitude of the UAV. The airspeed sensor unit 1 (9) and airspeed sensor unit 2 (10) are respectively disposed on the outer leading edge of the two wings of the UAV; the protective layer 1 is connected to the flexible substrate 1 (1); the flexible substrate 1 is a flexible printed circuit line, and the thermistor is manufactured by micron-level process and encapsulated on the flexible printed circuit line; The second airspeed sensor unit (10) includes: a second thermistor unit, a second heater, a second protective layer, and a second flexible substrate; the second airspeed sensor unit is configured in the same way as the first airspeed sensor unit. The data fusion estimation module includes a processor; the processor is a filter data fusion model, including: filter one, neural network, extended filter one, filter two, and extended filter two; The multiple thermistors generate a temperature difference, which is converted into voltage data by an analog and / or digital conversion unit. The voltage data is then transmitted to a neural network through a filter to obtain the three-dimensional airspeed of the UAV. The acceleration and angular velocity of the UAV output by the inertial measurement unit are transmitted to the extended filter one through the second filter to obtain attitude data; The three-dimensional airspeed and attitude data of the UAV are input into the second extended filter to obtain the position data and velocity data of the UAV. Both extended filter one and extended filter two are extended Kalman filters; The inertial measurement unit (11) includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer; The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer are respectively connected to the data fusion estimation module; The three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer output the acceleration and angular velocity of the UAV and transmit them to the processor.

2. The UAV airspeed sensing device according to claim 1, characterized in that, The data fusion estimation module includes: an analog and / or digital conversion unit and a data storage module; the processor is connected to the analog and / or digital conversion unit and the data storage module respectively; the voltage data is transmitted to the processor.

3. The UAV airspeed sensing device according to claim 2, characterized in that, The UAV airspeed sensing device also includes a sampling module; The sampling module includes a thermistor interface one (12-1), a thermistor interface two (12-2), and a data transmission interface (13). The first thermistor interface (12-1) is connected to the first airspeed sensor unit (9) and the analog and / or digital conversion unit respectively; the second thermistor interface (12-2) is connected to the second airspeed sensor unit (10) and the analog and / or digital conversion unit respectively. The data transmission interface (13) is connected to the three-axis gyroscope, the three-axis accelerometer and the three-axis magnetometer.

4. The flight control method for the UAV airspeed sensing device according to any one of claims 1-3, characterized in that, include: Voltage data is acquired based on airspeed sensor unit 1 (9) and airspeed sensor unit 2 (10); The voltage data is fed into the neural network through filter 1 of the data fusion estimation module to obtain the corresponding three-dimensional airspeed data; The acceleration and angular velocity of the UAV are obtained based on the inertial measurement unit (11); The acceleration and angular velocity of the UAV are fused by inputting the second filter of the data fusion estimation module into the first extended filter to obtain attitude data; The three-dimensional airspeed data and attitude data are input into the data fusion estimation module and fused using the extended filter 2 to obtain position data and velocity data.

5. The flight control method for the UAV airspeed sensing device according to claim 4, characterized in that, The expression for the attitude data is: in, For the first k The attitude data of the drone at each moment For the first k The predicted attitude data of the drone at each moment. For the first k Kalman gain at each moment, For measurement equations; This is the first k Voltage data of the airspeed sensor unit at each moment.

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