A method and system for visual fusion of an aircraft and a method and system for collision warning

Abstract

The application provides a kind of aircraft view fusion method and system and anti-collision early warning method and system, belong to aircraft technical field, the aircraft view fusion method includes generating synchronous radar data and synchronous inertial navigation data in the process of aircraft flight;Synchronous radar data is filtered by background noise power dynamic threshold false alarm point, and effective radar data is formed;The synchronous inertial navigation data corresponding to the time stamp of effective radar data is obtained as effective inertial navigation data, and the initial terrain grid is constructed using effective radar data and effective inertial navigation data;The initial terrain grid is processed by double-line interpolation and adaptive resolution, and the optimized terrain grid is generated, the obstacles in the optimized terrain grid are updated and rendered, and the view fusion of the aircraft is completed.The application removes noise interference in the original radar data by false alarm point filtering processing, improves the accuracy and reliability of radar data.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of aircraft, and relates to a visual fusion technology of an aircraft and an anti-collision warning technology of the aircraft, in particular to a visual fusion method and system of an aircraft and an anti-collision warning method and system. BACKGROUND

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

[0003] The aircraft identifies complex terrain and dynamic obstacles by the radar system carried thereon when performing tasks. The commonly used radar system is a millimeter wave anti-collision radar system, which has good electromagnetic wave penetration capability (rain and fog attenuation <0.2 dB / km) and stable all-weather working characteristics. Its working advantages are: 1) all-weather detection capability: it can penetrate rain, fog, sand and other harsh environments for stable all-weather detection (rain and fog attenuation <0.2 dB / km); 2) dynamic target tracking: it uses Doppler effect to support motion object speed detection, and has high detection accuracy (detection accuracy ±0.2 m / s); 3) long distance coverage: the typical detection distance is up to 500 m, which is better than 300 m of laser radar and 200 m of visual system. However, the millimeter wave anti-collision radar system also has some defects when working. When it faces multipath interference, it has a high misjudgment rate, with a misjudgment rate >32% in urban environment and a misjudgment rate >18% in mountain environment, thereby causing large radar data error collected by the millimeter wave anti-collision radar system, affecting the accuracy of three-dimensional terrain reconstruction and obstacle identification. SUMMARY

[0004] In view of the technical problems described in the above background, the existing millimeter wave anti-collision radar system has the technical problem of large radar data error, which affects the accuracy of three-dimensional terrain reconstruction and obstacle identification. In view of this technical problem, the present application provides a visual fusion method and system of an aircraft and an anti-collision warning method and system.

[0005] The present application filters the false alarm points of the synchronous radar data, removes the 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 identification, and thereby improves the accuracy of three-dimensional terrain reconstruction and obstacle identification.

[0006] To solve the above technical problems, the application adopts the following technical solutions:

[0007] The method comprises the following steps:

[0008] Synchronous radar data and synchronous inertial navigation data are generated during flight of the aircraft;

[0009] A background noise power dynamic threshold of the synchronous radar data is determined, false alarm point filtering processing of the synchronous radar data is performed through the background noise power dynamic threshold, and effective radar data is formed;

[0010] Synchronous inertial navigation data corresponding to a time stamp of the effective radar data is obtained as effective inertial navigation data, and an initial terrain grid is constructed using the effective radar data and the effective inertial navigation data;

[0011] Optimized terrain grid is generated by performing double-line interpolation processing and adaptive resolution processing on the initial terrain grid, and obstacles in the optimized terrain grid are updated and rendered, thereby completing the visual fusion of the aircraft.

[0012] Further limitation, the synchronous radar data and synchronous inertial navigation data generated during flight of the aircraft specifically comprises:

[0013] The original radar data and original inertial navigation data of the aircraft are obtained;

[0014] The original radar data and original inertial navigation data are time-aligned by using Lagrange interpolation method, and the synchronous radar data and synchronous inertial navigation data are generated during flight of the aircraft.

[0015] Further limitation, the background noise power dynamic threshold of the synchronous radar data specifically comprises:

[0016] The background noise power estimate value is determined by using the background noise power of the synchronous radar data;

[0017] The background noise power dynamic threshold is determined by using the background noise power estimate value.

[0018] Further limitation, the false alarm point filtering processing of the synchronous radar data through the background noise power dynamic threshold to form the effective radar data specifically comprises:

[0019] The background noise power corresponding to each synchronous radar data is compared with the background noise power dynamic threshold for judgment:

[0020] 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 taken as the effective radar data; otherwise, the synchronous radar data is filtered out.

[0021] Further limited, the double-line interpolation processing and adaptive resolution processing of the initial terrain grid to generate the optimized terrain grid specifically includes:

[0022] The initial terrain grid is subjected to double-line interpolation processing to obtain an interpolated terrain grid.

[0023] The base grid resolution and the resolution adjustment factor are obtained, the actual grid resolution is determined according to the base 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 the optimized terrain grid.

[0024] Further limited, the updating and rendering of the obstacles in the optimized terrain grid specifically includes:

[0025] The position and size parameters of the known obstacles and the position and size parameters of the new obstacles are determined.

[0026] The position and size parameters of the new obstacle are compared with the position and size parameters of all known obstacles to determine whether there is a known obstacle with a coincidence degree less than a coincidence threshold with the position and size parameters of the new obstacle.

[0027] If not, it is determined whether there is a free space in the geographical space based on the position of the new obstacle and the positions of all known obstacles, if there is a free space, the new obstacle is added to the free space and rendered, and the survival period of the new obstacle is set to a maximum survival period threshold; if there is no free space, the new obstacle is discarded;

[0028] If so, replace the known obstacle less than the coincidence threshold with the new obstacle, set the survival period of the new obstacle to the maximum survival period threshold, and render the new obstacle.

[0029] The aircraft visual fusion system formed based on the above-mentioned aircraft visual fusion method, comprising:

[0030] A time synchronization module is used to generate synchronized radar data and synchronized inertial navigation data during the flight of the aircraft.

[0031] A false alarm point filtering module is used to determine a background noise power dynamic threshold of the synchronized radar data, and to perform false alarm point filtering processing on the synchronized radar data through the background noise power dynamic threshold to form effective radar data.

[0032] An initial terrain grid generation module is used to obtain synchronized inertial navigation data corresponding to the time stamp of the effective radar data as effective inertial navigation data, and to construct an initial terrain grid using the effective radar data and the effective inertial navigation data.

[0033] And a view fusion module: used for double-line interpolation processing and adaptive resolution processing on the initial terrain grid, generating an optimized terrain grid, updating and rendering the optimized terrain grid, and completing view fusion of the aircraft.

[0034] The aircraft anti-collision warning method based on the view fusion method of the aircraft is formed, and the method comprises the following steps:

[0035] A dynamic threat index model of the aircraft is constructed based on the obstacles in the optimized terrain grid and the 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.

[0036] The risk level of the aircraft is determined based on the dynamic threat index of the aircraft, and the aircraft anti-collision warning is performed according to the risk level of the aircraft.

[0037] It is further limited that the risk level of the aircraft is:

[0038] DTI ≤ 0.4, no threat risk state;

[0039] 0.4 < DTI ≤ 0.6, three-level alarm risk state;

[0040] 0.6 < DTI ≤ 0.85, two-level alarm risk state;

[0041] DTI > 0.85, one-level alarm risk state;

[0042] Wherein, DTI is the dynamic threat index of the aircraft.

[0043] The aircraft anti-collision warning system based on the aircraft anti-collision warning method is formed, and the system comprises:

[0044] A dynamic threat index determination module: used for constructing 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 determining a dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft;

[0045] And an anti-collision warning module: used for determining a risk level of the aircraft based on the dynamic threat index of the aircraft, and performing aircraft anti-collision warning according to the risk level of the aircraft.

[0046] Compared with the prior art, the present application has the beneficial effects that:

[0047] 1. The aircraft visual fusion method of the present application generates synchronized radar data and synchronized inertial navigation data during the flight of the aircraft, and the purpose is to preliminarily filter the original inertial navigation data and the original radar data, thereby generating synchronized radar data and synchronized inertial navigation data. Then, through false alarm point filtering processing of the synchronized radar data, the noise interference in the synchronized radar data collected by the radar system is removed, the accuracy and reliability of the synchronized radar data are improved, the synchronized radar data after false alarm point filtering processing is taken as effective radar data, accurate data is provided for subsequent three-dimensional terrain reconstruction and obstacle identification, thereby improving the accuracy of three-dimensional terrain reconstruction and obstacle identification.

[0048] 2. The present application filters the synchronized radar data through background noise power dynamic threshold, which can automatically adjust the background noise power dynamic threshold according to the change of the background noise level to maintain a constant false alarm rate, and realizes the control of the false alarm rate while reducing the false alarm rate.

[0049] 3. The present application realizes rapid and accurate reconstruction of complex terrain through double-line interpolation processing and adaptive resolution processing of the initial terrain grid, not only improves the smoothness and accuracy of data during terrain grid division, but also reduces the consumption of computing resources, thereby improving the real-time performance and efficiency of the system.

[0050] 4. The aircraft collision avoidance warning method of the present application 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 present application performs collision avoidance warning based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, can more accurately identify and classify obstacles, and render and identify the obstacles, while providing clearer and more intuitive obstacle information, thereby enhancing the situational awareness ability of the pilot. The risk level of the aircraft is determined based on the dynamic threat index of the aircraft, thereby performing collision avoidance warning, which can more accurately judge the flight path, avoid obstacles in time, and reduce the risk of collision.

[0051] 5. The present application realizes more smooth and intuitive visual fusion display and collision warning for the pilot during flight through three-dimensional visualization technology and real-time data processing capability, thereby improving the comfort and convenience of the pilot during driving. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is a schematic diagram of the aircraft visual fusion method of the present application;

[0053] Figure 2 is a schematic diagram of the aircraft visual fusion system of the present application;

[0054] Figure 3A schematic diagram of the aircraft collision avoidance warning method of the present application;

[0055] Figure 4 A schematic diagram of the aircraft collision avoidance warning system of the present application. DETAILED DESCRIPTION

[0056] The technical solutions of the present application will be further explained and described below in combination with the accompanying drawings and embodiments, but the present application is not limited to the following described embodiments.

[0057] Embodiment 1

[0058] Referring to Figure 1 The embodiment provides a flight vehicle view fusion method, comprising the following steps:

[0059] Synchronous radar data and synchronous inertial navigation data are generated during flight of the flight vehicle;

[0060] A background noise power dynamic threshold of the synchronous radar data is determined, false alarm point filtering processing is performed on the synchronous radar data through the background noise power dynamic threshold, and effective radar data is formed;

[0061] Synchronous inertial navigation data corresponding to a time stamp of the effective radar data is obtained as effective inertial navigation data, and an initial terrain grid is constructed using the effective radar data and the effective inertial navigation data;

[0062] Optimized terrain grid is generated by performing double-line interpolation processing and adaptive resolution processing on the initial terrain grid, obstacles in the optimized terrain grid are updated and rendered, and the view fusion of the flight vehicle is completed.

[0063] In the embodiment, the generation of the synchronous radar data and the synchronous inertial navigation data during the flight of the flight vehicle specifically comprises: obtaining original radar data and original inertial navigation data of the flight vehicle; the original radar data and the original inertial navigation data are time-aligned by using Lagrange interpolation method, and the synchronous radar data and the synchronous inertial navigation data are generated during the flight of the flight vehicle. The original radar data in the embodiment is from a millimeter wave collision avoidance radar system carried on the flight vehicle or other radar systems known to those skilled in the art. The original inertial navigation data in the embodiment is from an inertial navigation system carried on the flight vehicle.

[0064] The original radar data and the original inertial navigation data are time-aligned by using Lagrange interpolation method. Specifically, the original radar data (time stamp tr, distance between the aircraft and the obstacle, azimuth angle of the aircraft, and speed of the aircraft, etc.) and the original inertial navigation data (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 are obtained, the original radar data and the original inertial navigation data are time-aligned by using 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, the original inertial navigation data and the original radar data are preliminarily filtered, the original radar data and the original inertial navigation data with inconsistent time stamps are removed, and thus the synchronized radar data and the synchronized inertial navigation data are generated. The original radar data includes radar ranging points and obstacle data, for example, distance between the aircraft and the obstacle, azimuth angle of the aircraft, speed of the aircraft, etc. The original inertial navigation data is used to provide key parameters such as position information (precision, latitude, and altitude) of the aircraft, speed information (three-dimensional speed components) of the aircraft, and attitude information (pitch angle, roll angle, and heading angle) of the aircraft.

[0065] The calculation formula of time alignment is as follows:

[0066] Δt = tr-ti

[0067] In the formula, Δt is a time difference, and the unit is millisecond; tr is a time stamp of the original radar data, and the unit is millisecond; ti is a time stamp of the original inertial navigation data, and the unit is millisecond.

[0068] When Δt > δ max , the original radar data and the original inertial navigation data are discarded, δ max is a maximum allowed time difference, and the unit is millisecond, generally 5 milliseconds.

[0069] In the embodiment, the background noise power dynamic threshold of the synchronized radar data is determined as follows: a background noise power estimation value is determined by using the background noise power of the synchronized radar data; and the background noise power dynamic threshold is determined by using the background noise power estimation value.

[0070] The calculation formula of determining the background noise power estimation value by using the background noise power of the synchronized radar data is as follows:

[0071]

[0072] In the formula, N is the number of the synchronized radar data, and the unit is piece; i is the serial number of the synchronized radar data, and the unit is piece; P i is the background noise power of the i th synchronized radar data, and the unit is dB; P n is the background noise power estimation value, and the unit is dB.

[0073] In the embodiment, the false alarm points of the synchronous radar data are filtered by the background noise power dynamic threshold to form effective radar data, which specifically includes: comparing the background noise power corresponding to each synchronous radar data with the background noise power dynamic threshold to determine whether the background noise power corresponding to the synchronous radar data is not more than the background noise power dynamic threshold; if yes, the synchronous radar data is taken as effective radar data; otherwise, the synchronous radar data is filtered out.

[0074] The calculation formula of the background noise power dynamic threshold is as follows:

[0075]

[0076] In the formula, T is the background noise power dynamic threshold, unit: dB; P n is the background noise power estimation value, unit: dB; is a threshold factor, which is used to adjust the background noise power dynamic threshold to maintain a constant false alarm rate, dimensionless; e is an environmental dynamic factor, dimensionless; for the urban environment, the vehicle flow sound source is dense, and the building / bridge / road metal structure is easy to cause electromagnetic echo interference, and the environmental dynamic factor is 1.2; for the forest environment, the terrain is undulating, and it is easy to produce ground clutter and multipath reflection interference, and the environmental dynamic factor is 1.1; for the grassland environment, the ground surface is mainly low herbaceous plants, and the wind vegetation causes random scattering, and the environmental dynamic factor is 1.0; for the desert / gobi environment, the ground surface is dry and the sand particles are loose, and the environmental dynamic factor is 0.9; for the open area, the terrain is flat, and the environmental dynamic factor is 0.8.

[0077] The calculation formula of the threshold factor is as follows:

[0078]

[0079] In the formula, N is the number of synchronous radar data, unit: pieces; is the false alarm rate, dimensionless; as the false alarm rate decreases, the threshold factor will increase, thereby causing the increase of the background noise power dynamic threshold, based on this relationship, the false alarm rate is controlled and reduced.

[0080] The comparison of the background noise power corresponding to each synchronous radar data with the background noise power dynamic threshold is specifically as follows: if P i ≤T, the synchronous radar data is taken as effective radar data; if P i >T, it is a false alarm point, and the synchronous radar data is filtered out.

[0081] The embodiment removes noise interference in the synchronous radar data collected by the radar system through false alarm point filtering processing of the synchronous radar data, improves the accuracy and reliability of the synchronous radar data, takes the synchronous radar data after the false alarm point filtering processing as effective radar data, and provides accurate data for subsequent three-dimensional terrain reconstruction and obstacle identification, thereby improving the accuracy of the three-dimensional terrain reconstruction and the obstacle identification.

[0082] In the embodiment, the double-line interpolation processing and the adaptive resolution processing are performed on the initial terrain grid to generate the optimized terrain grid, specifically including: performing double-line interpolation processing on the initial terrain grid to obtain an interpolated terrain grid; obtaining a basic grid resolution and a resolution adjustment factor, determining an 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. The basic grid resolution and the resolution adjustment factor are both parameters set by a user.

[0083] The establishment process of the initial terrain grid is as follows: the terrain profile data in the effective radar data is converted from a polar coordinate system to a WGS-84 geographic coordinate system, and the coordinate conversion formula is as follows:

[0084] X = r×cos(θ) ×sin(φ)

[0085] Y = r×sin(θ) ×sin(φ)

[0086] Z = r×cos(φ)

[0087] In the formula, r is the distance in the polar coordinate, the unit is m; θ is the azimuth angle in the polar coordinate (0 degrees is taken as the front of the radar, and the counterclockwise direction is positive), the unit is °; φ is the pitch angle in the polar coordinate (0 degrees is taken as the horizontal line of the radar, and upward is positive), the unit is °; X is the coordinate of the horizontal axis in the WGS-84 geographic coordinate system, the unit is m; Y is the coordinate of the vertical axis in the WGS-84 geographic coordinate system, the unit is m; and Z is the height in the WGS-84 geographic coordinate system, the unit is m.

[0088] The double-line interpolation processing on the initial terrain grid is to adopt a double-line interpolation algorithm on the terrain profile data, to calculate the value of the point to be interpolated through a weighted average manner combined with the coordinate point information around the three-dimensional terrain target point, and the steps are as follows:

[0089] S1: determining the point (x, y) to be interpolated;

[0090] S2: finding the coordinate points corresponding to the four positions of the top left, the top right, the bottom left and the bottom right of the initial terrain grid of the point to be interpolated, denoted as (x0, y0), (x1, y0), (x0, y1) and (x1, y1);

[0091] S3: Calculate the interpolation factor and :

[0092] ,

[0093] S4: Perform bilinear interpolation, wherein the formula of bilinear interpolation is:

[0094]

[0095] f(x, y) is the value of the point to be interpolated after interpolation, unit: m; f(x0, y0) is the value of the (x0, y0) coordinate point, unit: m; f(x1, y0) is the value of the (x1, y0) coordinate point, unit: m; f(x0, y1) is the value of the (x0, y1) coordinate point, unit: m; f(x1, y1) is the value of the (x1, y1) coordinate point; and are all interpolation factors, dimensionless.

[0096] After the initial terrain grid is subjected to bilinear interpolation, an interpolated terrain grid is formed, and the interpolated terrain grid is subjected to adaptive resolution processing to generate an optimized terrain grid.

[0097] The setting of the terrain grid resolution affects the accuracy and real-time performance of terrain display. The present embodiment introduces a resolution adjustment factor to dynamically adjust the actual grid resolution of the interpolated terrain grid, thereby ensuring that the display accuracy is improved in complex terrain regions and high-efficiency rendering is maintained in flat regions.

[0098] The calculation formula of the actual grid resolution is:

[0099]

[0100] In the formula, G is the actual grid resolution, dimensionless; B is the basic grid resolution, dimensionless; and R is the resolution adjustment factor, dimensionless.

[0101] The calculation formula of the resolution adjustment factor R is:

[0102]

[0103] In the formula, H is the elevation change rate, dimensionless; γ is the elevation weight factor, dimensionless, and is used to adjust the influence degree of the elevation change rate on the grid spacing; S is the average slope, dimensionless; and β is the average slope weight factor, dimensionless, and is used to adjust the influence degree of the average slope on the grid spacing.

[0104] The value range of the elevation weight factor γ and the average slope weight factor β is shown in Table 1 below.

[0105] Table 1: Value range of elevation weight factor γ and average slope weight factor β

[0106]

[0107] In this embodiment, updating and rendering the obstacles in the optimized terrain grid specifically includes: determining the position and size parameters of the known obstacles and the position and size parameters of the new obstacle; comparing the position and size parameters of the new obstacle with the position and size parameters of all known obstacles to determine whether there is a known obstacle with a coincidence degree less than a coincidence threshold with the position and size parameters of the new obstacle; if not, determining whether there is a free space in the geographical space based on the position of the new obstacle and the positions of all known obstacles, if there is a free space, adding the new obstacle to the free space and rendering the new obstacle, and setting the survival period of the new obstacle to a maximum survival period threshold; if not, discarding the new obstacle; if so, replacing the known obstacle with a coincidence degree less than the coincidence 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 threshold, the maximum survival period threshold and the minimum survival period threshold are all parameters for setting. The survival period in this embodiment is the remaining display time of the obstacle in the current field of view.

[0108] wherein the position calculation formula of the known obstacle is:

[0109]

[0110] wherein x and y are respectively the coordinates of the known obstacle in the geographical coordinate system, unit: m; Re is the radius of the earth, unit: m; and are respectively the latitude and longitude of the known obstacle, unit: °; and are respectively the latitude and longitude of the starting position of the aircraft, unit: °; and are respectively the northward distance and westward distance of the known obstacle relative to the aircraft, unit: m.

[0111] In the above process of updating and rendering the obstacles in the optimized terrain grid, it is assumed that the coordinates of the known obstacle position (coordinates of the center position) are (x1, y1, z1), the length of the known obstacle is w1, unit: m; the width of the known obstacle is h1, unit: m; the coordinates of the center position of the new obstacle are (x2, y2, z2), the length of the new obstacle is w2, unit: m, and the width of the new obstacle is h2, unit: m.

[0112] The calculation formula of the distance d1 between the known obstacle and the new obstacle is:

[0113]

[0114] The size difference of the known obstacle and the new obstacle, including the length difference d2 and the width difference d3, corresponds to the following calculation formula:

[0115]

[0116]

[0117] When <T1 and <T2 and <T3, it is considered that the coincidence degree threshold is less than the coincidence degree threshold.

[0118] The following will take linear obstacles, isolated obstacles and dense obstacles as examples to update and render the obstacles in the optimized terrain grid:

[0119] Linear obstacle: determine the position and length of the known linear obstacle and the position and length of the new linear obstacle; compare the position and length of the new linear obstacle with the position and length of the known linear obstacle, and determine whether there is a known linear obstacle with a position and length (determined according to the position and length of the new linear obstacle) less than the coincidence degree threshold; if not, determine whether there is a free space in the geographical space based on the position of the new linear obstacle and the position of all known linear obstacles, if there is a free space, add the new linear obstacle to the free space, and render the new linear obstacle, at the same time set the survival period of the new linear obstacle to the maximum survival period threshold; if not, discard the new linear obstacle; if so, replace the known linear obstacle 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 power lines and rivers that are distributed in a straight line in the horizontal plane, have weak reflectivity to the beams emitted by the radar system, and through the update of the known linear obstacle, the continuous update of the linear obstacle in the scene is realized.

[0120] Isolated obstacle: determine the position of the known isolated obstacle, the horizontal position of the known isolated obstacle, and the position of the new isolated obstacle, the horizontal position of the new isolated obstacle; compare the position and horizontal position of the known isolated obstacle with the position and horizontal position of the new isolated obstacle (determine the center position of the new isolated obstacle according to the position and horizontal position of the new isolated obstacle); judge whether there is a known isolated obstacle with a position and horizontal position less than the coincidence threshold with the position and horizontal position of the new isolated obstacle; if not, judge whether there is a vacancy in the geographical space of the known isolated obstacle based on the position of the new isolated obstacle and the position of all known isolated obstacles; if there is a vacancy, add the new isolated obstacle to the vacancy and 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 vacancy, discard the new isolated obstacle; if there is, replace the known isolated obstacle less than the coincidence 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 towers, mountain peaks and other obstacles that are vertically thin and high, and have weak reflectivity to the beams emitted by the radar system. Through the update of the known isolated obstacle, the continuous update of the isolated obstacle in the scene is realized.

[0121] Dense obstacle: determine the position of the known dense obstacle, the maximum and minimum boundary values of the known dense obstacle in the lateral direction and the longitudinal direction respectively, and the position of the new dense obstacle, the maximum and minimum boundary values of the new dense obstacle in the lateral direction and the longitudinal direction respectively; compare the position of the new dense obstacle, the maximum and minimum boundary values of the new dense obstacle in the lateral direction and the longitudinal direction respectively with the position of the known dense obstacle, the maximum and minimum boundary values of the known dense obstacle in the lateral direction and the longitudinal direction respectively (determine the center position of the new dense obstacle according to the position of the new dense obstacle and the maximum and minimum boundary values of the new dense obstacle in the lateral direction and the longitudinal direction respectively); judge whether there is a known dense obstacle with a coincidence less than the coincidence threshold with the new dense obstacle; if not, judge whether there is a vacancy in the geographical space of the known obstacle based on the position of the new dense obstacle and the position of all known dense obstacles; if there is a vacancy, add the new dense obstacle to the vacancy and 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 vacancy, discard the new dense obstacle; if there is, replace the known dense obstacle less than the coincidence 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 buildings, grasslands and other obstacles that are displayed as a large area, and through the update of the known dense obstacle, the continuous update of the dense obstacle in the scene is realized.

[0122] The embodiment realizes rapid and accurate reconstruction of complex terrain by double-line interpolation processing and adaptive resolution processing on the initial terrain grid, improves the smoothness and accuracy of data during mesh division, reduces the consumption of computing resources, and thus improves the real-time performance and efficiency of the system.

[0123] Embodiment 2

[0124] Referring to Figure 2 The embodiment provides a flight vehicle visual fusion system, which is formed based on the flight vehicle visual fusion method in the above embodiment 1, and includes a time synchronization module, a false alarm point filtering module, an initial terrain grid generation module, and a visual fusion module, wherein:

[0125] The time synchronization module is used to generate synchronized radar data and synchronized inertial navigation data during flight of the flight vehicle.

[0126] The false alarm point filtering module is used to determine a background noise power dynamic threshold of the synchronized radar data, perform false alarm point filtering processing on the synchronized radar data through the background noise power dynamic threshold, and form effective radar data.

[0127] The initial terrain grid generation module is used to obtain synchronized inertial navigation data corresponding to a time stamp of the effective radar data as effective inertial navigation data, and construct an initial terrain grid by using the effective radar data and the effective inertial navigation data.

[0128] The visual fusion module is used to perform double-line 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 visual fusion of the flight vehicle.

[0129] The flight vehicle visual fusion system in the embodiment corresponds to the flight vehicle visual fusion method in the embodiment 1, and the specific content of the time synchronization module, the false alarm point filtering module, the initial terrain grid generation module, and the visual fusion module which are not described in detail in the embodiment can be referred to the description of the flight vehicle visual fusion method.

[0130] Embodiment 3

[0131] Referring to Figure 3 The embodiment provides a flight vehicle collision avoidance warning method, which is generated based on the flight vehicle visual fusion method in the above embodiment, and includes the following steps:

[0132] A dynamic threat index model of the flight vehicle is constructed based on obstacles in the optimized terrain grid and flight parameters of the flight vehicle, a dynamic threat index of the flight vehicle is determined according to the dynamic threat index model of the flight vehicle, a risk level of the flight vehicle is determined based on the dynamic threat index of the flight vehicle, and flight vehicle collision avoidance warning is performed according to the risk level of the flight vehicle.

[0133] wherein the dynamic threat index model of the aircraft is:

[0134]

[0135] wherein 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: °.

[0136] In this embodiment, the risk level of the aircraft is:

[0137] DTI ≤ 0.4, no threat risk state, the rendering mark is displayed in low saturation cold tone, and the edge is smoothly transitioned to weaken the visual saliency;

[0138] 0.4<DTI ≤ 0.6, three-level alarm risk state, the rendering mark is displayed in high luminance amber color, and rapid focus shift is triggered;

[0139] 0.6<DTI ≤ 0.85, two-level alarm risk state, the rendering mark is displayed in a two-color gradient mode, and the edge detection is strengthened through hue mutation;

[0140] DTI>0.85, one-level alarm risk state, the rendering mark is displayed in a pulse type saturated red flicker, ensuring the urgency of the alarm information;

[0141] wherein DTI is the dynamic threat index of the aircraft.

[0142] The flicker frequency F of the one-level alarm risk state can be dynamically adjusted to ensure the urgency and intuitiveness of the alarm information, and the calculation formula is:

[0143] F= DTI×F a ×F b

[0144] wherein F is the flicker frequency of the one-level alarm risk state, unit: times / s; F a is the flicker frequency adjustment factor, dimensionless, and its value is set by the user; F b is the basic flicker frequency, unit: times / s.

[0145] It should be noted that the contents of the flight vehicle's view fusion method not described in detail in this embodiment are described in the flight vehicle's view fusion method part of the above embodiment 1.

[0146] Embodiment 4

[0147] Referring to Figure 4 , the embodiment provides a flight vehicle collision avoidance warning system, which is formed based on the flight vehicle collision avoidance warning method described above, and includes a dynamic threat index determination module and a collision avoidance warning module, wherein:

[0148] The dynamic threat index determination module is configured to construct a dynamic threat index model of the flight vehicle based on the obstacles in the optimized terrain grid and the flight parameters of the flight vehicle, and determine the dynamic threat index of the flight vehicle according to the dynamic threat index model of the flight vehicle.

[0149] The collision avoidance warning module is configured to determine the risk level of the flight vehicle based on the dynamic threat index of the flight vehicle, and perform collision avoidance warning of the flight vehicle according to the risk level of the flight vehicle.

[0150] The flight vehicle collision avoidance warning system of the embodiment is completely corresponding to the flight vehicle collision avoidance warning method in the above embodiment 3. For the specific contents of the dynamic threat index determination module and the collision avoidance warning module not described in detail in this embodiment, refer to the description of the flight vehicle collision avoidance warning method part above.

[0151] The flight vehicle system in the present application refers to a manned flight vehicle system.

[0152] The above embodiments are only used to illustrate the technical solutions of the present application, and are not limited to the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement for part 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. An aircraft collision avoidance warning method, characterized by, The method comprises the following steps: synchronous radar data and synchronous inertial navigation data are generated during flight of the aircraft; a background noise power dynamic threshold of the synchronous radar data is determined, false alarm point filtering processing of the synchronous radar data is performed through the background noise power dynamic threshold, and effective radar data is formed; wherein the calculation formula of the background noise power dynamic threshold is: In the formula, T is a background noise power dynamic threshold, unit: dB; P n is a background noise power estimation value, unit: dB; is a threshold factor, used to adjust the background noise power dynamic threshold to maintain a constant false alarm rate, dimensionless; e is an environmental dynamic factor, dimensionless; for urban environment, vehicle flow sound source is dense, building / bridge / road metal structure is easy to cause electromagnetic echo interference, its environmental dynamic factor is 1.2; for forest environment, the terrain is undulating, easy to produce ground clutter and multipath reflection interference, its environmental dynamic factor is 1.1; for grassland environment, the ground is mainly low herbaceous plants, wind vegetation causes random scattering, its environmental dynamic factor is 1.0; for desert / gobi environment, the ground is dry and the sand is loose, its environmental dynamic factor is 0.9; for open land, the terrain is flat, its environmental dynamic factor is 0.8; the synchronous inertial navigation data corresponding to the time stamp of the effective radar data is obtained as the effective inertial navigation data, and the initial terrain grid is constructed by using the effective radar data and the effective inertial navigation data; double linear interpolation processing and adaptive resolution processing are performed on the initial terrain grid to generate an optimized terrain grid, obstacles in the optimized terrain grid are updated and rendered, and the visual fusion of the aircraft is completed; a dynamic threat index model of the aircraft is constructed based on the obstacles in the optimized terrain grid and the flight parameters of the aircraft, and the dynamic threat index of the aircraft is determined according to the dynamic threat index model of the aircraft; wherein the dynamic threat index model of the aircraft is: where 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: °; the risk level of the aircraft is determined based on the dynamic threat index of the aircraft, and the aircraft collision avoidance warning is performed according to the risk level of the aircraft; the risk level of the aircraft is: DTI ≤ 0.4, no threat risk state; 0.4 < DTI ≤ 0.6, three-level alarm risk state; 0.6 < DTI ≤ 0.85, two-level alarm risk state; DTI > 0.85, one-level alarm risk state; wherein DTI is the dynamic threat index of the aircraft; the flicker frequency F of the one-level alarm risk state can be dynamically adjusted to ensure the urgency and intuitiveness of the alarm information, and the calculation formula is: F = DTI x F a x F b In the formula, F is the flicker frequency of the first alarm risk state, unit: times / s; F a is the flicker frequency adjustment factor, dimensionless, which is set by the user; F b is the basic flicker frequency, unit: times / s.

2. The aircraft collision avoidance warning method of claim 1, wherein, The generation of synchronous radar data and synchronous inertial navigation data during the flight of the aircraft specifically includes: obtaining the original radar data and the original inertial navigation data of the aircraft; the original radar data and the original inertial navigation data are time-aligned by using the Lagrange interpolation method, and the synchronous radar data and the synchronous inertial navigation data are generated during the flight of the aircraft.

3. The aircraft collision avoidance warning method of claim 1, wherein, The determination of the background noise power dynamic threshold of the synchronous radar data specifically includes: determining the background noise power estimate value by using the background noise power of the synchronous radar data; determining the background noise power dynamic threshold by using the background noise power estimate value.

4. The aircraft collision avoidance warning method of claim 3, wherein, The false alarm point filtering processing of the synchronous radar data through the background noise power dynamic threshold specifically includes: comparing and judging 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 taken as effective radar data; otherwise, the synchronous radar data is filtered out.

5. The aircraft collision avoidance warning method of claim 1, wherein, The double linear interpolation processing and adaptive resolution processing of the initial terrain grid to generate the optimized terrain grid specifically includes: double linear interpolation processing is performed 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 the optimized terrain grid.

6. The aircraft collision avoidance warning method according to claim 1 or 5, characterized by, The updating and rendering of the obstacles in the optimized terrain grid specifically include: the position and size parameters of the known obstacles and the position and size parameters of the new obstacles are determined; Compare the position and size parameters of the new obstacle with the position and size parameters of all known obstacles to determine whether there is a known obstacle whose position and size parameters coincide with the new obstacle to less than a coincidence threshold; If not, determine whether there is a free space in the geographical space based on the position of the new obstacle and the positions of all known obstacles, if there is a free space, add the new obstacle to the free space and render the new obstacle, and set the survival period of the new obstacle to a maximum survival period threshold; if there is no free space, discard the new obstacle; If so, replace the known obstacle less than the coincidence threshold with the new obstacle, set the survival period of the new obstacle to the maximum survival period threshold, and render the new obstacle.

7. An aircraft collision warning system formed on the basis of the aircraft collision warning method according to claim 1, characterized in that It includes: A time synchronization module for generating synchronized radar data and synchronized inertial navigation data during aircraft flight; A false alarm point filtering module for determining a background noise power dynamic threshold of the synchronized radar data, filtering false alarm points of the synchronized radar data through the background noise power dynamic threshold, and forming effective radar data; An initial terrain grid generation module for obtaining synchronized inertial navigation data corresponding to the timestamp of the effective radar data as effective inertial navigation data, and constructing an initial terrain grid using the effective radar data and the effective inertial navigation data; A visual fusion module for performing double-line interpolation processing and adaptive resolution processing on the initial terrain grid to generate an optimized terrain grid, updating and rendering the optimized terrain grid, and completing visual fusion of the aircraft; A dynamic threat index determination module for constructing a dynamic threat index model of the aircraft based on obstacles in the optimized terrain grid and flight parameters of the aircraft, and determining a dynamic threat index of the aircraft according to the dynamic threat index model of the aircraft; And an anti-collision warning module for determining a risk level of the aircraft based on the dynamic threat index of the aircraft, and performing anti-collision warning of the aircraft according to the risk level of the aircraft.

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