Visual fusion method and system of aircraft and anti-collision early warning method and system

Abstract

The invention provides a visual fusion method and system of an aircraft and an anti-collision early warning method and system, and belongs to the technical field of aircrafts, and the visual fusion method of the aircraft comprises the following steps: generating synchronous radar data and synchronous inertial navigation data in the flight process of the aircraft; performing false alarm point filtering processing on the synchronous radar data through the background noise power dynamic threshold to form effective radar data; acquiring synchronous inertial navigation data corresponding to the timestamp of the effective radar data as effective inertial navigation data, and constructing an initial terrain grid by using the effective radar data and the effective inertial navigation data; and performing double-line interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid, and updating and rendering obstacles in the optimized terrain grid to complete visual fusion of the aircraft. According to the method, noise interference in the original radar data is removed through false alarm point filtering processing, and the accuracy and reliability of the radar data are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft, and relates to the vision fusion technology of aircraft and the anti-collision warning technology of aircraft. Specifically, it is a vision fusion method and system for aircraft and an anti-collision warning method and system. Background Art

[0002] With the rapid development of aircraft technology, aircraft can perform more and more complex tasks such as regional reconnaissance, target strike, search and rescue, and resource exploration. However, when performing these tasks, aircraft often face complex and variable environments, which are often unpredictable, resulting in damage or destruction of the aircraft. Therefore, in recent years, the flight safety of aircraft in the face of complex and variable environments has become the focus of research.

[0003] When an aircraft performs a task, it identifies complex terrains and dynamic obstacles through a radar system carried thereon. The currently commonly used radar system is a millimeter-wave anti-collision radar system. This millimeter-wave anti-collision radar system has good electromagnetic wave penetration ability (rain and fog attenuation < 0.2 dB / km) and stable all-weather working characteristics. Its working advantages are as follows: 1) All-weather detection ability: It can penetrate harsh environments such as rain, fog, and sand and dust for stable all-weather detection (rain and fog attenuation < 0.2 dB / km); 2) Dynamic target tracking: It uses the Doppler effect to support the speed detection of moving objects, and the detection accuracy is high (detection accuracy ±0.2 m / s); 3) Long-distance coverage: The typical detection distance reaches 500 m, which is better than 300 m of lidar and 200 m of the vision system. However, this millimeter-wave anti-collision radar system also has some defects when working. When facing multipath interference, its false alarm rate is relatively high. The false alarm rate is > 32% in urban environments and > 18% in mountainous environments, resulting in large errors in the radar data collected by this millimeter-wave anti-collision radar system, affecting the accuracy of three-dimensional terrain reconstruction and obstacle recognition. Summary of the Invention

[0004] In view of the technical problem described in the above background art that the existing millimeter-wave anti-collision radar system has large errors in the collected radar data, which affects the accuracy of three-dimensional terrain reconstruction and obstacle recognition, the present invention proposes a vision fusion method and system for aircraft and an anti-collision warning method and system.

[0005] The present invention filters out false alarm points from synchronous radar data, removes noise interference in the synchronous radar data, improves the accuracy and reliability of the synchronous radar data, provides accurate data for subsequent three-dimensional terrain reconstruction and obstacle recognition, and thus improves the accuracy of three-dimensional terrain reconstruction and obstacle recognition.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: A vision fusion method for an aircraft of the present invention includes the following steps: Generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft; Determine the dynamic threshold of the background noise power of the synchronized radar data, and perform false alarm point filtering on the synchronized radar data through the dynamic threshold of the background noise power to form effective radar data; Obtain the synchronized inertial navigation data corresponding to the timestamp of the effective radar data as the effective inertial navigation data, and construct an initial terrain grid using the effective radar data and the effective inertial navigation data; Perform bilinear interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid, update and render the obstacles in the optimized terrain grid, and complete the vision fusion of the aircraft.

[0007] Further defined, the generating of the synchronized radar data and the synchronized inertial navigation data during the flight of the aircraft specifically includes: Obtain the original radar data and the original inertial navigation data of the aircraft; Use the Lagrange interpolation method to align the time of the original radar data and the original inertial navigation data, and generate the synchronized radar data and the synchronized inertial navigation data during the flight of the aircraft.

[0008] Further defined, the determining of the dynamic threshold of the background noise power of the synchronized radar data specifically includes: Determine the background noise power estimate using the background noise power of the synchronized radar data; Determine the dynamic threshold of the background noise power using the background noise power estimate.

[0009] Further defined, the performing of the false alarm point filtering on the synchronized radar data through the dynamic threshold of the background noise power to form the effective radar data specifically includes: Compare and judge the background noise power corresponding to each synchronized radar data with the dynamic threshold of the background noise power: If the background noise power corresponding to the synchronized radar data does not exceed the dynamic threshold of the background noise power, then use this synchronized radar data as the effective radar data; otherwise, filter out this synchronized radar data.

[0010] Further defined, the performing of the bilinear interpolation processing and the adaptive resolution processing on the initial terrain grid to generate the optimized terrain grid specifically includes: Perform bilinear interpolation processing on the initial terrain grid to obtain the interpolated terrain grid; Obtain the base grid resolution and the resolution adjustment factor, determine the actual grid resolution according to the base grid resolution and the resolution adjustment factor, adaptively adjust the resolution of the interpolated terrain grid according to the actual grid resolution, and generate an optimized terrain grid.

[0011] Further defined, the updating and rendering of the obstacles in the optimized terrain grid specifically includes: Determine the position and size parameters of known obstacles and the position and size parameters of new obstacles; Compare the position and size parameters of the new obstacle with the position and size parameters of all known obstacles, and determine whether there are known obstacles with a coincidence degree less than the coincidence degree threshold with the position and size parameters of the new obstacle; If not, based on the position of the new obstacle and the positions of all known obstacles, determine whether there is a free space for the known obstacles in the geographical space. If there is a free space, add the new obstacle to the free space, render the new obstacle, and set the survival period of the new obstacle to the maximum survival period threshold; if there is no free space, discard the new obstacle; If there is, replace the known obstacle less than the coincidence degree threshold with the new obstacle, set the survival period of the new obstacle to the maximum survival period threshold, and render the new obstacle.

[0012] The vision fusion system of the aircraft formed based on the above vision fusion method of the aircraft of the present invention includes: Time synchronization module: used to generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft; False alarm point filtering module: used to determine the dynamic threshold of the background noise power of the synchronized radar data, perform false alarm point filtering processing on the synchronized radar data through the dynamic threshold of the background noise power, and form effective radar data; Initial terrain grid generation module: used to obtain the synchronized inertial navigation data corresponding to the timestamp of the effective radar data as effective inertial navigation data, and construct an initial terrain grid using the effective radar data and the effective inertial navigation data; And a vision fusion module: used to perform bilinear interpolation processing and adaptive resolution processing on the initial terrain grid, generate an optimized terrain grid, update and render the optimized terrain grid, and complete the vision fusion of the aircraft.

[0013] An aircraft anti-collision warning method generated based on the above vision fusion method of the aircraft of the present invention includes the following steps: Construct a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determine the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; Determine the risk level of the aircraft based on the dynamic threat index of the aircraft, and conduct anti-collision warning for the aircraft according to the risk level of the aircraft.

[0014] Further defined, the risk level of the aircraft is: DTI ≤ 0.4, no threat risk state; 0.4 < DTI ≤ 0.6, level-three warning risk state; 0.6 < DTI ≤ 0.85, level-two warning risk state; DTI > 0.85, level-one warning risk state; Among them, DTI is the dynamic threat index of the aircraft.

[0015] The aircraft anti-collision warning system formed by the present invention based on the above aircraft anti-collision warning method includes: Dynamic threat index determination module: used to construct a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determine the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; And an anti-collision warning module: used to determine the risk level of the aircraft based on the dynamic threat index of the aircraft, and conduct anti-collision warning for the aircraft according to the risk level of the aircraft.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. A vision fusion method for an aircraft according to the present invention generates synchronous radar data and synchronous inertial navigation data during the flight of the aircraft. The purpose is to preliminarily filter the original inertial navigation data and original radar data, so as to generate synchronous radar data and synchronous inertial navigation data. Then, by performing false alarm point filtering on the synchronous radar data, the noise interference in the synchronous radar data collected by the radar system is removed, the accuracy and reliability of the synchronous radar data are improved, and the synchronous radar data after false alarm point filtering is used as effective radar data, providing accurate data for subsequent three-dimensional terrain reconstruction and obstacle recognition, thereby improving the accuracy of three-dimensional terrain reconstruction and obstacle recognition.

[0017] 2. The present invention performs false alarm point filtering on the synchronous radar data through the dynamic threshold of the background noise power. It can automatically adjust the dynamic threshold of the background noise power according to the change of the background noise level to maintain a constant false alarm rate, achieving the control of the false alarm rate while reducing the false alarm rate.

[0018] 3. The present invention realizes the fast and accurate reconstruction of complex terrain through double-line interpolation processing and adaptive resolution processing of the initial terrain grid, not only improving the smoothness and accuracy of the data during terrain grid division, but also reducing the consumption of computing resources, thereby improving the real-time performance and efficiency of the system.

[0019] 4. The anti-collision warning method for the aircraft of the present invention constructs a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determines the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft. The anti-collision warning based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft can more accurately identify and classify obstacles, render and mark the obstacles, and at the same time can provide clearer and more intuitive obstacle information, enhancing the pilot's situation awareness ability. Determining the risk level of the aircraft based on the dynamic threat index of the aircraft, and thus performing anti-collision warning, can more accurately judge the flight path, avoid obstacles in time, and reduce the collision risk.

[0020] 5. Through the three-dimensional visualization technology and real-time data processing ability of the present invention, the pilot can obtain a smoother and more intuitive visual scene fusion display and collision warning during the flight, improving the comfort and convenience of the pilot's driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the visual scene fusion method for the aircraft of the present invention; Figure 2 It is a schematic diagram of the visual scene fusion system for the aircraft of the present invention; Figure 3 It is a schematic diagram of the anti-collision warning method for the aircraft of the present invention; Figure 4 It is a schematic diagram of the anti-collision warning system for the aircraft of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions of the present invention will be further explained below with reference to the drawings and embodiments, but the present invention is not limited to the embodiments described below.

[0023] Embodiment 1 Refer to Figure 1 , this embodiment provides a visual scene fusion method for an aircraft, including the following steps: Generate synchronous radar data and synchronous inertial navigation data during the flight of the aircraft; Determine the dynamic threshold of the background noise power of the synchronous radar data, and perform false alarm point filtering on the synchronous radar data through the dynamic threshold of the background noise power to form effective radar data; Obtain the synchronous inertial navigation data corresponding to the time stamp of the effective radar data as the effective inertial navigation data, and construct an initial terrain grid using the effective radar data and the effective inertial navigation data; Perform bilinear interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid, update and render the obstacles in the optimized terrain grid, and complete the visual scene fusion of the aircraft.

[0024] In this embodiment, generating synchronous radar data and synchronous inertial navigation data during the flight of the aircraft specifically includes: acquiring the original radar data and original inertial navigation data of the aircraft; using the Lagrange interpolation method to align the original radar data and original inertial navigation data in terms of time, and generating synchronous radar data and synchronous inertial navigation data during the flight of the aircraft. The original radar data in this embodiment comes from the millimeter-wave anti-collision radar system carried on the aircraft or other radar systems well-known to those skilled in the art. The original inertial navigation data in this embodiment comes from the inertial navigation system carried on the aircraft.

[0025] Specifically, using the Lagrange interpolation method to align the original radar data and original inertial navigation data in terms of time means: acquiring the original radar data (information such as time stamp tr, distance between the aircraft and the obstacle, azimuth angle of the aircraft, and speed of the aircraft, etc.) and original inertial navigation data (information such as time stamp ti, position information of the aircraft, speed information of the aircraft, and attitude information of the aircraft, etc.) under the same data packet number, aligning the original radar data and original inertial navigation data through the time stamp tr of the original radar data and the time stamp ti of the original inertial navigation data under the same data packet number, and performing preliminary filtering on the original inertial navigation data and original radar data to remove the original radar data and original inertial navigation data with inconsistent time stamps, thereby generating synchronous radar data and synchronous inertial navigation data. Among them, the original radar data includes radar ranging points and obstacle data, for example: information such as the distance between the aircraft and the obstacle, the azimuth angle of the aircraft, and the speed of the aircraft. The original inertial navigation data is used to provide key parameters such as the position information (accuracy, latitude, altitude) of the aircraft, the speed information (three-dimensional speed components) of the aircraft, and the attitude information (pitch angle, roll angle, and heading angle) of the aircraft.

[0026] The calculation formula for time alignment is: Δt = tr - ti In the formula, Δt is the time difference, unit: millisecond; tr is the time stamp of the original radar data, unit: millisecond; ti is the time stamp of the original inertial navigation data, unit: millisecond.

[0027] When Δt > δ max , discard the original radar data and original inertial navigation data, and δ max is the maximum allowable time difference, unit: millisecond, generally taking 5 milliseconds.

[0028] In this embodiment, determining the dynamic threshold of the background noise power of the synchronous radar data specifically includes: using the background noise power of the synchronous radar data to determine the estimated value of the background noise power; using the estimated value of the background noise power to determine the dynamic threshold of the background noise power.

[0029] Among them, the calculation formula for determining the estimated value of the background noise power using the background noise power of synchronous radar data is as follows: In the formula, N is the number of synchronous radar data, unit: piece; i is the serial number of the synchronous radar data, unit: piece; P i is the background noise power of the i-th synchronous radar data, unit: dB; P n is the estimated value of the background noise power, unit: dB.

[0030] In this embodiment, the false alarm point filtering process is performed on the synchronous radar data through the dynamic threshold of the background noise power to form effective radar data, which specifically includes: comparing and judging the background noise power corresponding to each synchronous radar data with the dynamic threshold of the background noise power: if the background noise power corresponding to the synchronous radar data does not exceed the dynamic threshold of the background noise power, then the synchronous radar data is used as effective radar data; otherwise, the synchronous radar data is filtered out.

[0031] Among them, the calculation formula for the dynamic threshold of the background noise power is as follows: In the formula, T is the dynamic threshold of the background noise power, unit: dB; P n is the estimated value of the background noise power, unit: dB; is the threshold factor, which is used to adjust the dynamic threshold of the background noise power to maintain a constant false alarm rate, dimensionless; e is the environmental dynamic factor, dimensionless; for the urban environment, the vehicle flow sound sources are dense, and the metal structures of buildings / bridges / roads are likely to cause electromagnetic echo interference, and its environmental dynamic factor is 1.2; for the forest environment, the terrain has large undulations, and it is easy to generate ground clutter and multipath reflection interference, and its environmental dynamic factor is 1.1; for the grassland environment, the surface is mainly low herbaceous plants, and the wind-driven vegetation causes random scattering, and its environmental dynamic factor is 1.0; for the desert / gobi environment, the surface is dry and the sand grains are loose, and its environmental dynamic factor is 0.9; for the open area, the terrain is flat, and its environmental dynamic factor is 0.8.

[0032] Threshold factor The calculation formula of is as follows: In the formula, N is the number of synchronous radar data, unit: piece; is the false alarm rate, dimensionless; as the false alarm rate decreases, the threshold factor will increase, resulting in an increase in the dynamic threshold of the background noise power. Based on this relationship, the false alarm rate is controlled and reduced.

[0033] The above comparison and judgment of the background noise power corresponding to each synchronous radar data with the dynamic threshold of the background noise power is specifically: if P i≤T, then the synchronous radar data is regarded as valid radar data; if P i >T, it is a false alarm point, and the synchronous radar data is filtered out.

[0034] In this embodiment, by performing false alarm point filtering on the synchronous radar data, the noise interference in the synchronous radar data collected by the radar system is removed, the accuracy and reliability of the synchronous radar data are improved, and the synchronous radar data after false alarm point filtering is used as valid radar data, providing accurate data for subsequent three-dimensional terrain reconstruction and obstacle recognition, thereby improving the accuracy of three-dimensional terrain reconstruction and obstacle recognition.

[0035] In this embodiment, performing bilinear interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid specifically includes: performing bilinear interpolation processing on the initial terrain grid to obtain the interpolated terrain grid; obtaining the basic grid resolution and the resolution adjustment factor, determining the actual grid resolution according to the basic grid resolution and the resolution adjustment factor, and adaptively adjusting the resolution of the interpolated terrain grid according to the actual grid resolution to generate the optimized terrain grid. Among them, both the basic grid resolution and the resolution adjustment factor are parameters set by the user.

[0036] The establishment process of the initial terrain grid is as follows: converting the terrain contour data in the valid radar data from the polar coordinate system to the WGS-84 geographic coordinate system, and its coordinate conversion formula is as follows: X = r×cos(θ) ×sin(φ) Y = r×sin(θ) ×sin(φ) Z = r×cos(φ) In the formula, r is the distance in the polar coordinate, unit: m; θ is the azimuth angle in the polar coordinate (with the front of the radar as 0 degrees, and the counterclockwise direction as positive), unit: °; φ is the elevation angle in the polar coordinate (with the horizontal line of the radar as 0 degrees, and the upward direction as positive), unit: °; X is the coordinate of the horizontal axis in the WGS-84 geographic coordinate system, unit: m; Y is the coordinate of the vertical axis in the WGS-84 geographic coordinate system, unit: m; Z is the height in the WGS-84 geographic coordinate system, unit: m.

[0037] Performing bilinear interpolation processing on the initial terrain grid is to adopt the bilinear interpolation algorithm for the terrain contour data, and by means of weighted average, combining the coordinate point information around the three-dimensional terrain target point, thereby calculating the value of the point to be interpolated. The steps are as follows: S1: Determine the point (x, y) to be interpolated; S2: Find the coordinate points corresponding to the upper left, upper right, lower left, and lower right positions of the point to be interpolated in the initial terrain grid, denoted as (x0, y0), (x1, y0), (x0, y1), and (x1, y1); S3: Calculate the interpolation factor and : , S4: Perform bilinear interpolation. The formula for bilinear interpolation is as follows: f(x, y) is the value of the point to be interpolated after interpolation, unit: m; f(x0, y0) is the value of the coordinate point (x0, y0), unit: m; f(x1, y0) is the value of the coordinate point (x1, y0), unit: m; f(x0, y1) is the value of the coordinate point (x0, y1), unit: m; f(x1, y1) is the value of the coordinate point (x1, y1); and are both interpolation factors, dimensionless.

[0038] The initial terrain grid forms an interpolated terrain grid after bilinear interpolation. Adaptive resolution processing is performed on the interpolated terrain grid to generate an optimized terrain grid.

[0039] The setting of the terrain grid resolution affects the accuracy and real-time performance of terrain display. In this embodiment, a resolution adjustment factor is introduced to dynamically adjust the actual grid resolution of the interpolated terrain grid, ensuring improved display accuracy in complex terrain areas and efficient rendering in flat areas.

[0040] The formula for the actual grid resolution is as follows: In the formula, G is the actual grid resolution, dimensionless; B is the basic grid resolution, dimensionless; R is the resolution adjustment factor, dimensionless.

[0041] Among them, the formula for the resolution adjustment factor R is as follows: In the formula, H is the elevation change rate, dimensionless; γ is the elevation weight factor, dimensionless, used to adjust the influence degree of the elevation change rate on the grid spacing; S is the average slope, dimensionless; β is the average slope weight factor, dimensionless, used to adjust the influence degree of the average slope on the grid spacing.

[0042] For the value ranges of the elevation weight factor γ and the average slope weight factor β, refer to Table 1 below.

[0043] Table 1: Value ranges of the elevation weight factor γ and the average slope weight factor β In this embodiment, updating and rendering the obstacles in the optimized terrain grid specifically includes: determining the position and size parameters of known obstacles and the position and size parameters of new obstacles; comparing the position and size parameters of the new obstacles with those of all known obstacles to determine whether there are known obstacles with a coincidence degree less than the coincidence degree threshold with the position and size parameters of the new obstacles; if not, based on the position of the new obstacle and the positions of all known obstacles, determining whether there is a free space for the known obstacles in the geographical space. If there is a free space, adding the new obstacle to the free space, rendering the new obstacle, and setting the survival period of the new obstacle to the maximum survival period threshold; if there is no free space, discarding the new obstacle; if so, replacing the known obstacle with a coincidence degree less than the coincidence degree threshold with the new obstacle, setting the survival period of the new obstacle to the maximum survival period threshold, and rendering the new obstacle. In this embodiment, the coincidence degree threshold, the maximum survival period threshold, and the minimum survival period threshold are all parameters for setting. In this embodiment, the survival period is the remaining display time of the obstacle under the current field of view.

[0044] Among them, the formula for calculating the position of the known obstacle is: In the formula, x and y are the coordinates of the known obstacle in the geographical coordinate system, unit: m; Re is the radius of the earth, unit: m; and are the latitude and longitude of the known obstacle respectively, unit: °; and are the latitude and longitude of the starting position of the aircraft respectively, unit: °; and are the northward distance and westward distance of the known obstacle relative to the aircraft respectively, unit: m.

[0045] During the above process of updating and rendering the obstacles in the optimized terrain grid, assuming that the coordinates of the position of the known obstacle (the coordinates of the center position) are (x 1 , y 1 , z 1 ), the length of the known obstacle is w 1 , unit: m; the width of the known obstacle is h 1 , unit: m; the coordinates of the center position of the new obstacle are (x 2 , y 2 , z 2 ), the length of the new obstacle is w 2 , unit: m, and the width of the new obstacle is h 2 , unit: m.

[0046] The formula for calculating the distance d 1 between the known obstacle and the new obstacle is: The size differences between known obstacles and new obstacles, including the length difference d 2 and the width difference d 3 , and the corresponding calculation formulas are as follows: When < T1 and < T2 and < T3, it is considered to be less than the coincidence degree threshold.

[0047] The following takes linear obstacles, isolated obstacles, and dense obstacles as examples to illustrate the update and rendering of obstacles in the optimized terrain grid: Linear obstacles: Determine the positions and lengths of known linear obstacles and the positions and lengths of new linear obstacles; compare the positions and lengths of the new linear obstacles with those of the known linear obstacles to determine whether there are any known linear obstacles with a coincidence degree less than the coincidence degree threshold with the positions and lengths of the new linear obstacles (determine the center position of the new linear obstacle according to the position and length of the new linear obstacle); if not, based on the positions of the new linear obstacle and all known linear obstacles, determine whether there are any vacant positions for the known linear obstacles in the geographical space. If there are vacant positions, add the new linear obstacle to the vacant position, render the new linear obstacle, and set the survival period of the new linear obstacle to the maximum survival period threshold; if there are no vacant positions, discard the new linear obstacle; if there are, replace the known linear obstacle with a coincidence degree less than the linear obstacle coincidence degree threshold with the new linear obstacle, set the survival period of the new linear obstacle to the maximum survival period threshold, and render the new linear obstacle. Linear obstacles include obstacles such as wires and rivers that are linearly distributed in the horizontal plane and have weak reflectivity to the beams emitted by the radar system. By updating the known linear obstacles, the continuous update of linear obstacles in the scene is realized.

[0048] Isolated Obstacles: Determine the positions of known isolated obstacles, the horizontal positions of known isolated obstacles, the positions of new isolated obstacles, and the horizontal positions of new isolated obstacles; compare the positions and horizontal positions of known isolated obstacles with the positions and horizontal positions of new isolated obstacles (determine the center position of the new isolated obstacle based on the position and horizontal position of the new isolated obstacle) to determine whether there are known isolated obstacles with a coincidence degree less than the coincidence degree threshold with the positions and horizontal positions of the new isolated obstacle; if not, based on the position of the new isolated obstacle and the positions of all known isolated obstacles, determine whether there is a vacant position in the geographical space for the known isolated obstacles. If there is a vacant position, add the new isolated obstacle to the vacant position, render the new isolated obstacle, and set the survival period of the new isolated obstacle to the maximum survival period threshold; if there is no vacant position, discard the new isolated obstacle; if there is, replace the known isolated obstacle with a coincidence degree less than the coincidence degree threshold with the new isolated obstacle, set the survival period of the new isolated obstacle to the maximum survival period threshold, and render the new isolated obstacle. Isolated obstacles include obstacles such as towers and mountain peaks that show a vertically slender distribution and have weak reflectivity to the beams emitted by the radar system. By updating the known isolated obstacles, the continuous update of isolated obstacles in the scene is realized.

[0049] Dense Obstacles: Determine the positions of known dense obstacles, the maximum and minimum boundary values in the horizontal and vertical directions of the known dense obstacles respectively, and the positions of new dense obstacles, the maximum and minimum boundary values in the horizontal and vertical directions of the new dense obstacles respectively; compare the positions of the new dense obstacles, the maximum and minimum boundary values in the horizontal and vertical directions of the new dense obstacles respectively with the positions of the known dense obstacles, the maximum and minimum boundary values in the horizontal and vertical directions of the known dense obstacles respectively (determine the center position of the new dense obstacle based on the position of the new dense obstacle and the maximum and minimum boundary values in the horizontal and vertical directions of the new dense obstacle) to determine whether there are known dense obstacles with a coincidence degree less than the coincidence degree threshold with the new dense obstacle; if not, based on the position of the new dense obstacle and the positions of all known dense obstacles, determine whether there is a vacant position in the geographical space for the known obstacles. If there is a vacant position, add the new dense obstacle to the vacant position, render the new dense obstacle, and set the survival period of the new dense obstacle to the maximum survival period threshold; if there is no vacant position, discard the new dense obstacle; if there is, replace the known dense obstacle with a coincidence degree less than the coincidence degree threshold with the new dense obstacle, set the survival period of the new dense obstacle to the maximum survival period threshold, and render the new dense obstacle. Dense obstacles include obstacles such as building groups and grasslands that are displayed in a large area. By updating the known dense obstacles, the continuous update of dense obstacles in the scene is realized.

[0050] In this embodiment, by performing bilinear interpolation processing and adaptive resolution processing on the initial terrain grid, rapid and accurate reconstruction of complex terrain is achieved. This not only improves the smoothness and accuracy of data during grid division but also reduces the consumption of computing resources, thereby enhancing the real-time performance and efficiency of the system.

[0051] Embodiment 2 Refer to Figure 2 , this embodiment provides a vision fusion system for an aircraft, which is formed based on the vision fusion method of the aircraft in the above-mentioned Embodiment 1, and includes a time synchronization module, a false alarm point filtering module, an initial terrain grid generation module, and a vision fusion module, where: Time synchronization module: used to generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft; False alarm point filtering module: used to determine the dynamic threshold of the background noise power of the synchronized radar data, and perform false alarm point filtering processing on the synchronized radar data through the dynamic threshold of the background noise power to form effective radar data; Initial terrain grid generation module: used to obtain the synchronized inertial navigation data corresponding to the timestamp of the effective radar data as the effective inertial navigation data, and construct an initial terrain grid using the effective radar data and the effective inertial navigation data; Vision fusion module: used to perform bilinear interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid, update and render the optimized terrain grid, and complete the vision fusion of the aircraft.

[0052] The vision fusion system of the aircraft in this embodiment corresponds exactly to the vision fusion method of the aircraft in Embodiment 1. For the specific content of the time synchronization module, false alarm point filtering module, initial terrain grid generation module, and vision fusion module not described in detail in this embodiment, refer to the description of the vision fusion method of the above-mentioned aircraft.

[0053] Embodiment 3 Refer to Figure 3 , this embodiment provides an aircraft collision avoidance warning method, which is generated based on the vision fusion method of the aircraft in the above-mentioned embodiment, and includes the following steps: Construct a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determine the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; determine the risk level of the aircraft based on the dynamic threat index of the aircraft, and perform aircraft collision avoidance warning according to the risk level of the aircraft.

[0054] Among them, the dynamic threat index model of the aircraft is: In the formula, DTI is the dynamic threat index of the aircraft, dimensionless; is the distance between the aircraft and the obstacle, unit: m; is the critical safety distance between the aircraft and the obstacle, unit: m; is the relative velocity vector of the aircraft, unit: m / s; n is the heading unit vector of the aircraft, dimensionless; is the maximum speed of the aircraft, unit: m / s; is the current angle of attack of the aircraft, unit: °; is the stall critical angle of the aircraft, unit: °.

[0055] In this embodiment, the risk level of the aircraft is: DTI ≤ 0.4, no threat risk state, the rendering identifier is displayed in a low-saturation cold color tone, and the edges are smoothly transitioned to weaken the visual salience; 0.4 < DTI ≤ 0.6, level 3 warning risk state, the rendering identifier is displayed in high-brightness amber color, triggering a fast focus shift; 0.6 < DTI ≤ 0.85, level 2 warning risk state, the rendering identifier is displayed in a two-color gradient mode, and the edge detection is enhanced through hue mutation; DTI > 0.85, level 1 warning risk state, the rendering identifier is a pulsating saturated red flash to ensure the urgency of the warning information; wherein, DTI is the dynamic threat index of the aircraft.

[0056] The flashing frequency F of the level 1 warning risk state can be dynamically adjusted to ensure the urgency and intuitiveness of the warning information. The calculation formula is: F = DTI × F a × F b In the formula, F is the flashing frequency of the level 1 warning risk state, unit: times / s; F a is the flashing frequency adjustment factor, dimensionless, and its value is set by the user; F b is the basic flashing frequency, unit: times / s.

[0057] It should be noted that for the content of the vision fusion method of the aircraft not elaborated in this embodiment, refer to the specific description of the vision fusion method of the aircraft in Embodiment 1 above.

[0058] Embodiment 4 Refer to Figure 4 , this embodiment provides an aircraft anti-collision warning system, which is formed based on the above-mentioned aircraft anti-collision warning method, and includes a dynamic threat index determination module and an anti-collision warning module, wherein: Dynamic threat index determination module: used to construct a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determine the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; Collision avoidance warning module: used to determine the risk level of the aircraft based on the dynamic threat index of the aircraft, and perform collision avoidance warning for the aircraft according to the risk level of the aircraft.

[0059] The aircraft collision avoidance warning system of this embodiment corresponds exactly to the aircraft collision avoidance warning method in the above-mentioned Embodiment 3. For the specific content of the dynamic threat index determination module and the collision avoidance warning module not described in detail in this embodiment, refer to the description in the part of the above-mentioned aircraft collision avoidance warning method.

[0060] The aircraft system in the present invention refers to a manned aircraft system.

[0061] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A method for visual fusion of an aircraft, characterized in that: The following steps are involved: Generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft; Determine a dynamic threshold of background noise power of synchronous radar data, and perform false alarm point filtering on the synchronous radar data by using the dynamic threshold of background noise power to form effective radar data; Acquire synchronous inertial navigation data corresponding to the timestamp of the effective radar data as the effective inertial navigation data, and construct an initial terrain grid using the effective radar data and the effective inertial navigation data; The initial terrain mesh is processed by bilinear interpolation and adaptive resolution to generate an optimized terrain mesh. The obstacles in the optimized terrain mesh are updated and rendered to complete the visual fusion of the aircraft.

2. The method for visual fusion of an aircraft according to claim 1, characterized in that: The generating of synchronous radar data and synchronous inertial navigation data during the flight of the aircraft specifically includes: Obtain the original radar data and original inertial navigation data of the aircraft; The Lagrange interpolation method is used to time-align the original radar data and the original inertial navigation data, and synchronized radar data and synchronized inertial navigation data are generated during the flight of the aircraft.

3. The method for visual fusion of an aircraft according to claim 1, characterized in that: Determining the dynamic threshold of background noise power of synchronous radar data specifically includes: Determining a background noise power estimate using the background noise power of the synchronized radar data; The background noise power estimate is used to determine a background noise power dynamic threshold.

4. The method for visual fusion of an aircraft according to claim 3, characterized in that: The performing false alarm point filtering processing on the synchronous radar data by using the background noise power dynamic threshold to form effective radar data specifically includes: Compare the background noise power corresponding to each synchronous radar data with the background noise power dynamic threshold: If the background noise power corresponding to the synchronous radar data does not exceed the background noise power dynamic threshold, the synchronous radar data is regarded as valid radar data; otherwise, the synchronous radar data is filtered out.

5. The method for visual fusion of an aircraft according to claim 1, characterized in that: The performing of bilinear interpolation processing and adaptive resolution processing on the initial terrain grid to generate the optimized terrain grid specifically includes: Perform bilinear interpolation processing on the initial terrain grid to obtain the interpolated terrain grid; The basic grid resolution and the resolution adjustment factor are obtained, the actual grid resolution is determined according to the basic grid resolution and the resolution adjustment factor, and the resolution of the interpolated terrain grid is adaptively adjusted according to the actual grid resolution to generate an optimized terrain grid.

6. The method for visual fusion of an aircraft according to claim 1 or 5, characterized in that: The updating and rendering of obstacles in the optimized terrain grid specifically includes: Determine the position and size parameters of known obstacles and the position and size parameters of new obstacles; Compare the position and size parameters of the new obstacle with those of all known obstacles to determine whether there is a known obstacle whose degree of coincidence with the position and size parameters of the new obstacle is less than a coincidence threshold; If not, based on the position of the new obstacle and the positions of all known obstacles, determine whether there is an empty position for the known obstacles in the geographic space. If there is an empty position, add the new obstacle to the empty position, render the new obstacle, and set the lifetime of the new obstacle to the maximum lifetime threshold. If there is no empty position, discard the new obstacle. If it exists, the known obstacles smaller than the overlap threshold are replaced by the new obstacles, the lifetime of the new obstacle is set to the maximum lifetime threshold, and the new obstacle is rendered.

7. A vision fusion system for an aircraft formed based on the vision fusion method for an aircraft according to claim 1, characterized in that: include: Time synchronization module: used to generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft; False alarm point filtering module: used to determine the dynamic threshold of background noise power of synchronous radar data, and perform false alarm point filtering on the synchronous radar data through the dynamic threshold of background noise power to form effective radar data; Initial terrain grid generation module: used to obtain the synchronous inertial navigation data corresponding to the timestamp of the effective radar data as the effective inertial navigation data, and construct the initial terrain grid using the effective radar data and the effective inertial navigation data; And the visual fusion module: used to perform bilinear interpolation and adaptive resolution processing on the initial terrain grid, generate an optimized terrain grid, update and render the optimized terrain grid, and complete the visual fusion of the aircraft.

8. An aircraft collision avoidance warning method generated based on the aircraft vision fusion method of claim 6, characterized in that: The following steps are involved: A dynamic threat index model of the aircraft is constructed based on obstacles in the optimized terrain grid and flight parameters of the aircraft, and a dynamic threat index of the aircraft is determined according to the dynamic threat index model of the aircraft; The risk level of the aircraft is determined based on the dynamic threat index of the aircraft, and an aircraft collision avoidance warning is performed according to the risk level of the aircraft.

9. The aircraft collision avoidance warning method according to claim 8, characterized in that: The risk level of the aircraft is: DTI ≤ 0.4, no threat risk state; 0.4 < DTI ≤ 0.6, level 3 warning risk state; 0.6 < DTI ≤ 0.85, level 2 warning risk state; DTI > 0.85, first-level warning risk status; Among them, DTI is the dynamic threat index of the aircraft.

10. An aircraft collision avoidance warning system formed based on the aircraft collision avoidance warning method according to claim 8, characterized in that: include: Dynamic threat index determination module: used to construct a dynamic threat index model of the aircraft based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and determine the dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; And the anti-collision warning module: used to determine the risk level of the aircraft based on the dynamic threat index of the aircraft, and to perform anti-collision warning for the aircraft according to the risk level of the aircraft.

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