Shipborne UAV positioning and navigation method, device, electronic equipment, and storage medium

Abstract

The present invention provides a method, device, electronic device and storage medium for positioning and navigating a ship-borne UAV, the method comprising: obtaining a radar image through a radar carried on the ship-borne UAV; obtaining a target detection result based on the radar image, wherein the target detection result includes target geometric parameters; obtaining target state information based on the target detection result through an extended Kalman filter and a continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target; obtaining positioning and navigation information of the ship-borne UAV through a state estimation Kalman filter based on the target state information. The present invention realizes precise positioning and navigation of the ship-borne UAV.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a method, device, electronic equipment and storage medium for positioning and navigating a shipborne unmanned aerial vehicle. Background Art

[0002] In recent years, with the continuous development of intelligent systems, marine unmanned system intelligent equipment represented by unmanned ships and unmanned submersibles has attracted widespread attention. Related research has been rapidly carried out and widely used in the civilian field.

[0003] A typical unmanned ship is an unmanned, intelligent operation platform that can be released and recovered by a surface ship or a shore base, and sail on the water in a semi-autonomous or fully autonomous manner. Compared with traditional surface ships, unmanned ships have the advantages of being small and light, flexible, adaptable, information-based, and intelligent. They are mainly used to perform dangerous tasks that are not suitable for manned ships. After being equipped with different sensors or equipment, unmanned ships can complete tasks such as search, detection, search and rescue, navigation, and hydrological and geographical surveys.

[0004] However, when operating at sea, due to the limitation of the earth's curvature, it is difficult to maintain stable point-to-point line-of-sight communication between the shore station and the long-distance unmanned ship, which severely limits the operating range of the unmanned ship. The sky wave beyond-horizon propagation technology that reflects the signal through the ionosphere and utilizes the atmospheric waveguide phenomenon can effectively increase the communication distance, but the related equipment is bulky and not suitable for small unmanned ships.

[0005] Due to their own defects, most single unmanned equipment is difficult to achieve significant performance improvements through technical iteration. Researchers have gradually shifted their attention to multi-unmanned equipment systems. In the field of marine unmanned equipment collaboration, unmanned aircraft, commonly known as drones, complement the advantages of unmanned ships to form a ship-borne drone collaboration system. With the advantage of the high position of drones, they can carry communication equipment to communicate with distant shore station targets, which can effectively solve the problem of short line-of-sight communication distance of unmanned ships. In addition, this collaborative system can greatly improve the operating range and reliability of marine unmanned equipment, and can efficiently complete complex marine tasks such as communication relay, maritime rescue, sea surface equipment inspection, disaster relief, stereoscopic observation, etc. The positioning and navigation of the ship-borne drone guides the ship-borne drone from the starting position all the way to the target location. By solving the current real-time motion state and position of the drone, the navigation system provides the relevant parameters of the drone's current state to the unmanned ship, thereby achieving correct control of the drone and route guidance.

[0006] Since the global navigation satellite system may be unreliable and inaccurate in the marine environment, other auxiliary technologies should be used to increase redundancy and reliability. At the same time, most of the existing target tracking research models the target as a point target without spatial range and cannot identify the physical size of the tracked target. Therefore, the present invention mainly studies how to achieve autonomous positioning and navigation of ship-borne UAVs based on tracking precise targets.

[0007] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a ship-borne unmanned aerial vehicle positioning and navigation method, device, electronic equipment, and storage medium.

[0009] The technical solution of the present invention provides a shipborne UAV positioning and navigation method, the method comprising:

[0010] Acquire radar images through radar mounted on a ship-borne drone;

[0011] Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter;

[0012] Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target;

[0013] Based on the target state information, the positioning and navigation information of the shipborne UAV is obtained through state estimation Kalman filtering.

[0014] Optionally, based on the radar image, a target detection result is obtained, wherein the target detection result includes a target geometric parameter, and further includes at least one of the following preprocessing steps for the radar image:

[0015] Preprocessing step a, applying fast time constant and sensitivity time control to reduce clutter echoes of rain and water surface in the radar image;

[0016] Preprocessing step b, removing areas of infrastructure and land reflections by specifying regions of interest on the radar image;

[0017] Preprocessing step c: extracting target spots on the radar image using a dynamic threshold method.

[0018] Optionally, extracting the target spots on the radar image using a dynamic threshold method further includes:

[0019] An average constant false alarm rate detector is applied by defining a window mask to extract target spots on the radar image.

[0020] Optionally, obtaining a target detection result based on the radar image, wherein the target detection result includes a target geometric parameter, further includes:

[0021] Use a fan-shaped polygon to describe the bounding box that includes the target reflection;

[0022] A line passing through the center position of the sector-shaped polygon at a predetermined angle and a boundary of the sector-shaped polygon form two intersection points, and the distance between the two intersection points is used as the geometric parameter of the target.

[0023] Optionally, based on the target detection result, target state information is acquired by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target, further comprising:

[0024] Obtain multiple length measurements as a function of target heading and their uncertainty estimates, as well as heading angle estimates and correlations of the tracked target and the ship-borne UAV;

[0025] The heading and length measurement values ​​processed by the unscented transformation method and the extended Kalman filter are converted into discrete distributions that can be used for motion state estimation, and combined with a continuous white noise acceleration model to obtain the target state information.

[0026] Optionally, based on the target state information, obtaining the positioning and navigation information of the shipborne UAV through state estimation Kalman filtering further includes:

[0027] The coordinates of the shipborne UAV, the target and the GPS system are uniformly converted into a representation under the NED coordinate system;

[0028] After positioning and navigating the shipborne UAV based on the state estimation Kalman filter algorithm, the position and posture information of the shipborne UAV in the NED coordinate system is obtained.

[0029] Optionally, the shipborne drone is equipped with a sensor suite consisting of a visual sensor, an auditory sensor and an inertial sensor.

[0030] The technical solution of the present invention also provides a shipborne UAV positioning and navigation device, the device comprising:

[0031] An acquisition module is used to acquire radar images through a radar carried on a ship-borne UAV;

[0032] A preliminary detection module, used to obtain a target detection result based on the radar image, wherein the target detection result includes a target geometric parameter;

[0033] A precise tracking module, used for acquiring target state information based on the target detection result through an extended Kalman filter and a continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target;

[0034] The navigation module is used to obtain the positioning and navigation information of the ship-borne UAV through state estimation Kalman filtering based on the target state information.

[0035] The technical solution of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the shipborne UAV positioning and navigation method as described in any one of the above items are implemented.

[0036] The technical solution of the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the ship-borne UAV positioning and navigation method as described in any of the above items are implemented.

[0037] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic flow chart of a method for positioning and navigating a shipborne UAV provided by the technical solution of the present invention;

[0040] Figure 2 A structural schematic diagram of a shipborne UAV positioning and navigation device is also provided for the technical solution of the present invention;

[0041] Figure 3 A schematic diagram of a flow chart of another embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the physical structure of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The present invention proposes a method for positioning and navigating a shipborne UAV based on a robust state estimation error state Kalman filter. When tracking a target, the geometric parameters and motion parameters of the target are estimated simultaneously. When the UAV encounters a target whose geographic parameters are unavailable, the shape information is provided to accurately assess the collision risk and generate a suitable collision-free path. The extended Kalman filter and the continuous white noise acceleration model are used to achieve target tracking. When determining the tracking target for positioning and navigation, the robust state estimation error state Kalman filter is used to fuse the drift-free visual measurement and low-drift acoustic measurement with the inertial data to obtain a non-singular attitude representation with sufficient linearization properties, thereby generating a robust and accurate state estimate, and finally achieving the positioning and navigation of the shipborne UAV.

[0045] The following is a detailed description of the shipborne UAV positioning and navigation method provided in the embodiment of the present application through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0046] Figure 1 A flow chart of a shipborne UAV positioning and navigation method provided by the technical solution of the present invention is as follows: Figure 1 As shown, the technical solution of the present invention provides a shipborne UAV positioning and navigation method, the method comprising:

[0047] S110, obtaining a radar image by using a radar mounted on a ship-borne UAV;

[0048] S120, obtaining a target detection result based on the radar image, where the target detection result includes a target geometric parameter;

[0049] S130, based on the target detection result, obtaining target state information by using an extended Kalman filter and a continuous white noise acceleration model, where the target state information includes a length measurement value of the target;

[0050] S140, based on the target state information, obtain the positioning and navigation information of the shipborne UAV through state estimation Kalman filtering.

[0051] Optionally, based on the radar image, a target detection result is obtained, the target detection result includes a target geometric parameter, and further includes at least one of the following preprocessing steps for the radar image:

[0052] Preprocessing step a: Apply fast time constant and sensitivity time control to reduce clutter echoes from rain and water in radar images;

[0053] Preprocessing step b, removing areas of infrastructure and land reflections by specifying regions of interest on the radar image;

[0054] Preprocessing step c: Use dynamic threshold method to extract target spots on radar images.

[0055] Optionally, extracting target spots on the radar image using a dynamic threshold method further includes:

[0056] The average constant false alarm rate detector is applied by defining a window mask to extract the target spots on the radar image.

[0057] Optionally, obtaining a target detection result based on the radar image, wherein the target detection result includes a target geometric parameter, further includes:

[0058] Use a fan-shaped polygon to describe the bounding box that includes the target reflection;

[0059] Two intersection points are formed between a line passing through the center position of the fan-shaped polygon at a predetermined angle and the boundary of the fan-shaped polygon, and the distance between the two intersection points is used as the geometric parameter of the target.

[0060] Optionally, based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, and the target state information includes a length measurement value of the target, further including:

[0061] Obtain multiple length measurements as a function of target heading and their uncertainty estimates, as well as heading angle estimates and correlations of the tracked target and the ship-borne UAV;

[0062] The heading and length measurements processed by the unscented transformation method and the extended Kalman filter are converted into discrete distributions that can be used for motion state estimation, and the target state information is obtained by combining with the continuous white noise acceleration model.

[0063] Optionally, based on the target state information, obtaining the positioning and navigation information of the shipborne UAV through state estimation Kalman filtering further includes:

[0064] Convert the coordinates of the shipborne UAV, target and GPS system to the NED coordinate system;

[0065] After positioning and navigating the shipborne UAV based on the state estimation Kalman filter algorithm, the position and posture information of the shipborne UAV in the NED coordinate system is obtained.

[0066] Optionally, the shipborne drone is equipped with a sensor suite consisting of visual sensors, auditory sensors and inertial sensors.

[0067] The shipborne UAV positioning and navigation device provided by the present invention is described below, and the shipborne UAV positioning and navigation device described below and the shipborne UAV positioning and navigation method described above can be referred to each other. It should be noted that the device described here includes a virtual device on a program running device such as a computer or a processor.

[0068] Figure 2 The technical solution of the present invention also provides a structural schematic diagram of a ship-borne UAV positioning and navigation device, such as Figure 2 As shown, the technical solution of the present invention also provides a shipborne UAV positioning and navigation device, the device comprising:

[0069] An acquisition module is used to acquire radar images through a radar carried on a ship-borne UAV;

[0070] A preliminary detection module is used to obtain target detection results based on radar images, and the target detection results include target geometric parameters;

[0071] The precise tracking module is used to obtain the target state information based on the target detection result through the extended Kalman filter and the continuous white noise acceleration model, and the target state information includes the length measurement value of the target;

[0072] The navigation module is used to obtain the positioning and navigation information of the ship-borne UAV based on the target state information through the state estimation Kalman filter.

[0073] This embodiment realizes the precise positioning and navigation of the ship-borne UAV.

[0074] Figure 3 FIG. 1 is a flow chart of another embodiment of the present invention, Figure 3 As shown, in another embodiment, this embodiment provides a shipborne UAV positioning and navigation method, and the specific steps of the method can be as follows:

[0075] Step 1: Shipborne UAV target detection and geometric parameter extraction

[0076] A millimeter-wave radar is carried on a ship-borne UAV, and an image is generated by the millimeter-wave radar measurement. The spots on the image are occupied by the reflection of the target, and a single spot is defined by the range and azimuth resolution. To obtain the target tracked by the ship-borne UAV, a set of preprocessing steps is required to extract the target area from the radar image, including the real target and the unwanted echo. To this end, this embodiment applies a fast time constant and sensitivity time control to minimize the clutter echoes of rain and water surface in the image collected by the UAV, and eliminates the area reflected from the infrastructure and land by specifying a region of interest (ROI) on the radar image. The dynamic threshold method is used to extract the target spots on the preprocessed radar image. In this embodiment, an average constant false alarm rate detector is applied by defining a window mask, and the size of the window mask is determined in consideration of the size of the target of interest; then, the extracted spots are classified as the same target; then, from the obtained and classified cells, the center point is extracted, and its position is estimated based on the relative azimuth and distance information. Since radar images are generated by a stream of radar spokes representing range and azimuth resolution, a sector polygon can be used to describe the bounding box that includes the target reflections, and approximate measurements of the target geometric parameters are obtained from the intersection between the polygon boundary and a line passing through the center position at a certain angle.

[0077] The final result of this step is the drone tracking target obtained by shipborne drone target detection and geometric parameter extraction, thus preparing for the subsequent target tracking and positioning navigation process. Fast time constant and sensitivity time control are applied to minimize the clutter echoes of rain and water surface in the drone-collected image, and the area reflected from infrastructure and land is eliminated by specifying the region of interest (ROI) on the radar image. The dynamic threshold method is used to extract the target spots on the pre-processed radar image, and the average constant false alarm rate detector is applied by defining a window mask. Using these image processing steps, the recognition accuracy and success rate of target detection can be improved.

[0078] Step 2: The target detection result obtained in step 1 is used to track the target using the extended Kalman filter and continuous white noise acceleration model

[0079] Step 2.1: Measurement extraction. To update the motion of the shipborne UAV and the tracked target, two sets of measurements are used. The estimated motion of the shipborne UAV is obtained from the first measurement dataset z uav Correction, which is provided by onboard navigation sensors such as the global positioning system, compass, and inertial measurement unit (IMU), can be expressed as uav =[x uav ,y uav , ψ uav , V uav ] T +v uav , where xuav and uav represents the position measurement defined in the northing and easting directions of the Universal Transverse Telescope (UTM) coordinates, ψ uav Represents the heading measurement value, V uav represents the velocity measurement, provided by the navigation sensor, and v uav is zero-mean Gaussian noise, which is used to reflect the uncertainty in the measurement model; the second measurement data set z is obtained from the target detection result obtained in step 1. uav′ To correct the estimated motion and geometric information of each target, detect the relative position and distance of the target, and update the motion information of the drone, including position, heading and speed. The length measurement is used to update the geometric parameters of the target. The specific formula can be expressed as in, and l uav′ Respectively represent relative orientation, range and length measurements, v uav′ is a zero-mean Gaussian measurement noise that reflects the uncertainty of the measurement model. Cells (or spots) on the radar image are defined by the azimuth and range resolution. Multiple cells are occupied by reflections from a single target. Relative azimuth and range measurements are obtained directly from a set of collected cells. On the other hand, length measurements are not directly extracted from radar images. Therefore, an additional process is required to extract the length and estimate its associated uncertainty. The distance between the intersection of the polygon including the radar reflections and the motion state and the line passing through the center of the polygon and oriented with the target course angle defined in the fixed coordinate system of the drone can be regarded as a length measurement.

[0080] In radar images, target reflections are usually composed of multiple units or spots, and the location and range of these units can be determined by relative azimuth and range resolution. However, it is not straightforward to extract the specific physical size (such as length) of the target directly from the radar image.

[0081] To obtain the length measurement l uav′ , an indirect but more accurate approach is proposed: consider a polygonal boundary that reflects the shape of the target's reflection area on the radar image. A straight line with a specific orientation to the target's path angle in the fixed coordinate system of the drone is passed through the center of the polygon. The distance between the intersection of this line and the polygonal boundary is calculated, and a measurement representing the length characteristic of the target is obtained. This process not only provides length data, but also estimates the associated uncertainty, thereby enhancing the understanding of the target's geometric characteristics and tracking accuracy.

[0082] The final result is two sets of measurement values ​​for motion estimation of the shipborne UAV and the tracking target. Using length measurement to obtain motion estimation of the shipborne UAV and the tracking target can identify the physical size of the tracking target, improve the accuracy of target tracking, and enhance the collision avoidance capability of the shipborne UAV.

[0083] Step 2.2: Accurate geometric parameter estimation. In this embodiment, the uncertainty is evaluated by extracting a finite statistical set of multiple length values ​​obtained as a function of the target heading, the measurement being defined by the heading of the tracked target and the radar reflection shown by the fan-shaped polygon; therefore, its value is provided in a nonlinear transformation as a function of the process, the target heading defined in the object fixed frame is expressed as The specific formula can be expressed as The estimated headings of the shipborne UAV and the tracked target follow Gaussian distribution. Considering the heading The nonlinear relationship between the length luav, and the untraceable transformation method is adopted here. In order to define the discrete distribution from the sample data, a set of sigma points of the route are selected. In the motion state of the extended Kalman filter, this embodiment assumes that the geometric parameters are only related to the heading state. In order to estimate the Gaussian distribution of the sigma points transformed in the nonlinear function, the weight parameter is defined as Among them, wm represents the weight of the average value, n is the dimension of the state, and λ is the conversion factor.

[0084] Finally, the extended Kalman filter and continuous white noise acceleration model are used to obtain the target that the ship-borne UAV needs to track and detect. The extended Kalman filter and continuous white noise acceleration model are used to track the target, and the nonlinear equation is locally linearized, so that the Gaussian distribution variable is still Gaussian after linear transformation. This makes it possible to continue to use the standard Kalman filter framework, which can have a very good filtering effect. Combined with the continuous white noise acceleration model, better target tracking can be achieved.

[0085] Step 3: Based on the tracking target identified in step 2, the state estimation Kalman filter is used to locate and navigate the shipborne UAV.

[0086] Step 3.1: Coordinate transformation, unified coordinate system. First, transform the coordinate system from the camera coordinate system of the ship-borne drone to the NED (North East Down) coordinate system, assuming is the transformation matrix computed by the visual reference system containing the rotation and translation of the camera in the tag coordinate system, is the transformation matrix, which contains the fixed translation from the camera to the center of the drone in the camera coordinate system, is the transformation matrix containing the rotation matrix from the label frame to the NED frame. For simplicity, the NED and label frames have the same origin, both located in the middle of the reference label. The specific formula can be expressed as Then, the coordinate system is transformed from the tracking target coordinate system to the NED coordinate system. Since the tracking target coordinate system always remains in front of the shipborne UAV, the angular velocity of the shipborne UAV needs to be compensated when measuring the velocity of a point different from the origin of the reference axis. For this purpose, l uav′ The target velocity is converted into the velocity of the NED coordinate system, so the linear velocity of the target can be expressed as v b =v uav +S(w uav ) uav′ , where v uav is the linear velocity of the tracking target, S(w uav ) is the skew-symmetric matrix of the target angular velocity vector. Therefore, the linear velocity can be expressed as the NED coordinate system. The specific formula is: in, is the rotation matrix from the tracked target to the NED. Finally, the GPS coordinate system is transformed to the NED coordinate system, and the northeast position (x n ,y n ), longitude l and latitude μ, it is necessary to calculate the radius of curvature R of the earth on the principal vertical line N and the radius of curvature R of the meridian M , the specific formula can be expressed as R N = Among them, r e =6378137m is the semi-minor axis (equatorial radius), e = 0.0818 is the eccentricity of the earth, given the measured latitude and longitude origin (l0, μ0), located at the center of the reference coordinate system, then the measured position (x n ,y n ) can be calculated according to the longitude and latitude (l, μ) by the formula. The specific formula can be expressed as

[0087] Finally, the shipborne UAV, tracking target and global positioning system are unified into a NED coordinate system for positioning and navigation. Unifying the shipborne UAV, tracking target and global positioning system into a NED coordinate system can simplify the calculation process of shipborne UAV positioning and navigation, shorten the calculation time of positioning and navigation algorithm, and greatly improve the operation efficiency of the whole algorithm.

[0088] Step 3.2: State estimation Kalman filter is used for positioning and navigation of shipborne UAV. Given the error state equation and measurement vector of the state estimation Kalman filter, the positioning and navigation of the shipborne UAV is performed by giving a mathematical model for state propagation. The estimation of the inertial navigation system is obtained by combining the strapdown navigation equation with the high-speed IMU measurement value to obtain specific forces and angular velocities. The strapdown navigation equation uses unit quaternion to represent the attitude. The state estimation Kalman filter uses the Gibbs vector. A key point of the state estimation Kalman filter is to reset the estimation vector by setting the estimated error state of each input measurement to zero. Therefore, the estimated error state δx is calculated by the state estimation Kalman filter before the estimated shipborne UAV state is obtained. The core idea of ​​the algorithm is the calculation of the unit quaternion error. The specific formula can be expressed as The calculation of this error can be parameterized using a three-parameter attitude representation, where δη and δ∈ are unit quaternion errors The real and imaginary parts of g =[g1, g2, g3] T To achieve the mapping between the three-parameter attitude representation and the unit quaternion error, the specific formula can be expressed as δa=2δa g , The scaled Gibbs vector is used to obtain the physical quantity in the Kalman filter covariance matrix. Therefore, the covariance matrix estimate will be expressed in radians squared, which is equivalent to using the first-order approximation of the angular error. The three-parameter attitude error introduced by the Gibbs vector representation is defined as δx = in, and Represent the accelerometer and gyroscope bias errors respectively, and the δa vector replaces the unit quaternion error In addition, the differential equation b describing the error state dynamics must be linearized to fit the discrete-time system matrix. Therefore, the error state dynamics can be approximated by a first-order linear differential equation, which can be expressed as in, is the specific force vector, is the angular velocity vector, T gyro and T acc are the time constants that ensure the bias errors exponentially go to zero during dead reckoning. They are usually found by trial and error, but can also be obtained by calculating the Allan variances of the gyroscope and specific force sensor and using this information to fit a bias model. The additive zero-mean Gaussian white noise term are used to model measurement and bias noise, respectively.

[0089] The final result is the position and posture of the shipborne UAV output by using the state estimation Kalman filter for positioning and navigation of the shipborne UAV. The operation of the shipborne UAV in a narrow port environment requires accurate navigation data for safe operation. Since the global navigation satellite system may be unreliable and inaccurate in such an environment, other auxiliary technologies should be used to increase redundancy and reliability. To this end, this embodiment proposes a sensor suite consisting of visual, auditory and inertial sensors, which improves the robustness of the entire positioning and navigation system, and increases the redundancy and reliability of the positioning and navigation system, which can be used to navigate the shipborne UAV system in a port environment.

[0090] This embodiment applies fast time constant and sensitivity time control to minimize clutter echoes from rain and water surfaces in images collected by drones, eliminates areas reflected from infrastructure and land by specifying a region of interest (ROI) on the radar image, extracts target spots on the preprocessed radar image using a dynamic threshold method, and applies an average constant false alarm rate detector by defining a window mask. By using these image processing steps, the recognition accuracy and success rate of target detection can be improved.

[0091] In terms of target tracking, length measurement is used to obtain motion estimates of the shipborne UAV and the tracking target, which can identify the physical size of the tracking target, improve the accuracy of target tracking, and enhance the collision avoidance capability of the shipborne UAV.

[0092] The extended Kalman filter and continuous white noise acceleration model are used to track the target. The nonlinear equation is locally linearized so that the Gaussian distribution variables remain Gaussian distribution after linear transformation. This makes it possible to continue to use the standard Kalman filter framework, which can have a very good filtering effect. Combined with the continuous white noise acceleration model, better target tracking can be achieved.

[0093] Unifying the shipborne UAV, tracking targets and global positioning system into a NED coordinate system can simplify the calculation process of shipborne UAV positioning and navigation, shorten the calculation time of positioning and navigation algorithms, and greatly improve the operating efficiency of the entire algorithm.

[0094] Maneuvering of shipborne UAVs in narrow port environments requires precise navigation data for safe operation. Since global navigation satellite systems may be unreliable and inaccurate in such environments, other auxiliary technologies should be used to increase redundancy and reliability. To this end, this embodiment proposes a sensor suite consisting of visual, auditory and inertial sensors, which improves the robustness of the entire positioning and navigation system and increases the redundancy and reliability of the positioning and navigation system, which can be used to navigate shipborne UAV systems in port environments.

[0095] Figure 4A schematic diagram of the physical structure of an electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the shipborne drone positioning and navigation method, and the method includes:

[0096] Acquire radar images through radar mounted on a ship-borne drone;

[0097] Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter;

[0098] Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target;

[0099] Based on the target state information, the positioning and navigation information of the shipborne UAV is obtained through state estimation Kalman filtering.

[0100] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0101] On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, the computer can execute the shipborne UAV positioning and navigation method provided by the above methods, the method comprising:

[0102] Acquire radar images through radar mounted on a ship-borne drone;

[0103] Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter;

[0104] Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target;

[0105] Based on the target state information, the positioning and navigation information of the shipborne UAV is obtained through state estimation Kalman filtering.

[0106] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the above-mentioned shipborne drone positioning and navigation methods, the method comprising:

[0107] Acquire radar images through radar mounted on a ship-borne drone;

[0108] Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter;

[0109] Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target;

[0110] Based on the target state information, the positioning and navigation information of the shipborne UAV is obtained through state estimation Kalman filtering.

[0111] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A shipborne UAV positioning and navigation method, characterized in that: The method comprises: Acquire radar images through radar mounted on a ship-borne drone; Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter; Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target; Based on the target state information, obtaining positioning and navigation information of the shipborne UAV through state estimation Kalman filtering; in, Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target, and further includes: Obtain multiple length measurements as a function of target heading and their uncertainty estimates, as well as heading angle estimates for tracked targets and ship-borne drones; The heading and length measurement values ​​processed by the unscented transformation method and the extended Kalman filter are converted into discrete distributions that can be used for motion state estimation, and the target state information is obtained by combining with a continuous white noise acceleration model; Based on the target state information, obtaining the positioning and navigation information of the shipborne UAV through state estimation Kalman filtering further includes: The coordinates of the shipborne UAV, the target and the GPS system are uniformly converted into a representation under the NED coordinate system; After positioning and navigating the shipborne UAV based on the state estimation Kalman filter algorithm, the position and posture information of the shipborne UAV in the NED coordinate system is obtained.

2. The shipborne UAV positioning and navigation method according to claim 1, characterized in that: Based on the radar image, a target detection result is obtained, wherein the target detection result includes target geometric parameters, and further includes at least one of the following preprocessing steps for the radar image: Preprocessing step a, applying fast time constant and sensitivity time control to reduce clutter echoes of rain and water surface in the radar image; Preprocessing step b, removing areas of infrastructure and land reflections by specifying regions of interest on the radar image; Preprocessing step c: extracting target spots on the radar image using a dynamic threshold method.

3. The shipborne UAV positioning and navigation method according to claim 2, characterized in that: The method of extracting target spots on the radar image using a dynamic threshold method further includes: An average constant false alarm rate detector is applied by defining a window mask to extract target spots on the radar image.

4. The shipborne UAV positioning and navigation method according to claim 2, characterized in that: Based on the radar image, obtaining a target detection result, wherein the target detection result includes a target geometric parameter, further includes: Use a fan-shaped polygon to describe the bounding box that includes the target reflection; A line passing through the center position of the sector-shaped polygon at a predetermined angle and a boundary of the sector-shaped polygon form two intersection points, and the distance between the two intersection points is used as the geometric parameter of the target.

5. The shipborne UAV positioning and navigation method according to claim 1, characterized in that: The shipborne UAV is equipped with a sensor suite consisting of a visual sensor, an auditory sensor and an inertial sensor.

6. A shipborne UAV positioning and navigation device, characterized in that: The device comprises: An acquisition module is used to acquire radar images through a radar carried on a ship-borne UAV; A preliminary detection module, used to obtain a target detection result based on the radar image, wherein the target detection result includes a target geometric parameter; A precise tracking module, used for acquiring target state information based on the target detection result through an extended Kalman filter and a continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target; A navigation module, used to obtain positioning and navigation information of the ship-borne UAV through state estimation Kalman filtering based on the target state information; in, Based on the target detection result, target state information is obtained by extending Kalman filtering and continuous white noise acceleration model, wherein the target state information includes a length measurement value of the target, and further includes: Obtain multiple length measurements as a function of target heading and their uncertainty estimates, as well as heading angle estimates for tracked targets and shipborne drones; The heading and length measurement values ​​processed by the unscented transformation method and the extended Kalman filter are converted into discrete distributions that can be used for motion state estimation, and the target state information is obtained by combining with a continuous white noise acceleration model; Based on the target state information, obtaining positioning and navigation information of the shipborne UAV through state estimation Kalman filtering further includes: The coordinates of the shipborne UAV, the target and the GPS system are uniformly converted into a representation under the NED coordinate system; After positioning and navigating the shipborne UAV based on the state estimation Kalman filter algorithm, the position and posture information of the shipborne UAV in the NED coordinate system is obtained.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the shipborne UAV positioning and navigation method as described in any one of claims 1-5 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the shipborne UAV positioning and navigation method as described in any one of claims 1 to 5 are implemented.

Images