Space-based space target trajectory segmentation fitting method based on infrared radiation characteristics

Abstract

The invention discloses a space-based space target trajectory segmentation fitting method based on infrared radiation characteristics, and the method comprises the following steps: (1), obtaining the initial positioning information of a space target in an earth-fixed coordinate system through double-satellite combined observation; (2) the infrared radiation intensity of the space target is monitored in real time through an infrared sensor, grading points of the space target in different flight stages are recognized according to the infrared radiation intensity and signal-to-noise ratio changes, and target track segmentation is completed; and (3) aiming at different flight stages, by introducing a least square method of an adaptive factor, completing trajectory optimization and fine fitting of a space target in a space-based scene, realizing high-precision positioning and tracking, and obtaining motion characteristics of the target. By utilizing the method, the problem of over-fitting or under-fitting in a traditional method can be reduced, so that the precision and reliability of space-based space target detection and tracking are improved, and the method is suitable for a complex space target observation environment.

Description

Technical Field

[0001] The invention relates to the field of target positioning and tracking, and in particular to a space-based space target trajectory segmentation fitting method based on infrared radiation characteristics. Background Art

[0002] In space-based space target detection, infrared radiation characteristics are an important means of identifying and tracking targets. Infrared radiation can penetrate clouds and the atmosphere, and has a high detection sensitivity for various space targets. By analyzing the infrared radiation characteristics of the target at different flight stages, the segmented fitting and precise tracking of the target trajectory can be effectively achieved. The aircraft will go through multiple stages during the flight, and the infrared radiation characteristics of each stage are different. The infrared radiation in the boost stage is strong, the radiation intensity in the mid-stage decreases significantly, and the radiation intensity in the re-entry stage increases again due to air friction. How to use these changes in radiation characteristics to effectively segment and fit the target trajectory is an important issue in the current space-based space target positioning and tracking.

[0003] At present, commonly used trajectory fitting and optimization methods include Kalman filtering, least squares polynomial fitting, etc. For example, the Chinese patent document with publication number CN111324848A discloses a method for optimizing vehicle-mounted trajectory data of a mobile laser radar measurement system, which performs motion state analysis based on Kalman filtering vehicle-mounted trajectory data and establishes a vehicle motion model. The Chinese patent document with publication number CN114312840A discloses a method for fitting the trajectory of an obstacle target in an autonomous driving system, which uses the least squares method to fit the historical trajectory of the obstacle target and construct the trajectory equation of a single obstacle target.

[0004] These methods have achieved certain success in trajectory fitting, but they also have some shortcomings: (1) Kalman filtering assumes that the target motion is linear. For complex nonlinear motion models and multi-stage motion targets, it is difficult to provide accurate trajectory fitting; (2) The least squares polynomial fitting method fits the target trajectory by constructing a polynomial model, which is suitable for the fitting problem of smooth trajectories. Its advantage is that the calculation is simple and it is suitable for trajectory fitting in a short time. However, this method cannot automatically identify the segmented characteristics of the trajectory. For target trajectories that include multiple flight stages, the fitting accuracy is not high and it is prone to overfitting or underfitting problems. Summary of the invention

[0005] The present invention provides a space-based space target trajectory segmentation fitting method based on infrared radiation characteristics. By real-time monitoring of the infrared radiation intensity changes of the target, the flight phase of the target is accurately identified, and the trajectory segmentation fitting is performed to reduce the overfitting or underfitting problems in traditional methods, thereby improving the accuracy and reliability of space-based space target detection and tracking, and is suitable for complex space target observation environments.

[0006] A space-based space target trajectory segmentation fitting method based on infrared radiation characteristics comprises the following steps:

[0007] (1) Obtain the initial positioning information of the space target in the Earth-fixed coordinate system through dual-satellite joint observation;

[0008] (2) Using infrared sensors to monitor the infrared radiation intensity of space targets in real time, identify the classification points of space targets at different flight stages based on the changes in infrared radiation intensity and signal-to-noise ratio, and complete target trajectory segmentation;

[0009] (3) For different flight phases, the least squares method with adaptive factors is introduced to complete the trajectory optimization and precise fitting of space targets in space-based scenarios, achieve high-precision positioning and tracking, and obtain the motion characteristics of the target.

[0010] The specific process of step (1) is as follows:

[0011] Observation Satellite S 1 , S 2 Obtain the position information of the space target M on the image plane and convert the position information of the space target M on the image plane into the sight vector in the ground-fixed coordinate system

[0012] According to the vector addition principle, the position of the space target M is expressed as

[0013]

[0014] In the formula, ∥S i -M∥ is the i-th observation satellite S i The distance between the space target and the space target; the coordinates of the space target M in the ground-fixed coordinate system are (x t ,y t ,z t ); When there are two satellites, expand the components in each direction Arranged into matrix form, we get:

[0015]

[0016] Among them, (x s1 ,y s1 ,z s1 )、(x s2 ,y s2 ,z s2 ) are observation satellites S 1 , S 2 Position in Earth-fixed coordinate system;

[0017] The result of the above formula is abbreviated as A·M=b, and the position of the space target in the ground-fixed coordinate system is solved using the least squares criterion as follows:

[0018] M=(A T A) -1 A T b

[0019] After the solution is completed, the initial positioning information of the space target in the earth-fixed coordinate system is obtained.

[0020] The position information of the space target M on the image plane is converted into the sight vector in the ground-fixed coordinate system through the geometric positioning model. The formula is:

[0021]

[0022] In the formula, R is the line of sight vector of the satellite pointing to the space target; ref (θ) is the plane reflection matrix corresponding to the pointing mirror reflection angle θ; θ is the pointing mirror reflection angle value; is the conversion relationship between the geocentric inertial coordinate system and the geocentric earth-fixed coordinate system, and t is the Julian time at the observation time; is the transformation matrix from the satellite body coordinate system to the geocentric inertial coordinate system; is the calibrated installation matrix of the camera in the satellite body coordinate system; (i 0 ,j 0 ) is the coordinate of the camera principal point in the pixel coordinate system; Δi 0 ,Δj 0 is the camera principal point offset error; Δx, Δy are the image point distortion in the x and y directions; dx, dy are the pixel sizes in the x and y directions; f is the camera principal distance.

[0023] In step (2), the signal-to-noise ratio equation is:

[0024]

[0025] Where D 0 D is the equivalent aperture of the camera; * is the mean of the normalized spectral detection; τ 0 is the spectral transmittance of the optical system; τ a is the atmospheric spectral transmittance; ΔJ is the difference between the target radiation intensity and the background radiation intensity; δ is the signal process factor; F is the aperture number of the camera; ω is the instantaneous field of view of the detector; Δf is the equivalent noise bandwidth of the detection system; R is the distance between the space target and the space-based platform.

[0026] In step (2), the classification points of space targets at different flight stages are identified according to the changes in infrared radiation intensity and signal-to-noise ratio, specifically:

[0027] Detect the change in the signal-to-noise ratio ΔSNR, the formula is:

[0028] ΔSNR=SNR Δt+i -SNR i

[0029] Among them, i is the i-th moment of effective observation, Δt is the set time of observation signal-to-noise ratio change, which is set to an integer multiple of the system observation period;

[0030] According to the changes in infrared radiation intensity and signal-to-noise ratio, the flight process of a space target is divided into the boost phase, mid-phase and re-entry phase.

[0031] The specific process of step (3) is as follows:

[0032] (3-1) Determine the fitting polynomial based on k observation data segments of given different segments:

[0033]

[0034] Among them, a i are the coefficients of the polynomial to be fitted, i = 0, 1, ..., n;

[0035] (3-2) Determine the residual δ t , solve the similarity coupling factor τ(t), normalization coefficient η, and further calculate the adaptive weight factor λ t ;

[0036] (3-3) The curve fitting problem is converted into minimizing the residual sum of squares and constructing the objective function;

[0037] (3-4) The curve fitting problem is transformed into solving the minimum point of the objective function, and the least squares method is used to calculate the unknown parameter a i , that is, obtaining the precise fitting trajectory of the target.

[0038] In step (3-2), the adaptive weight factor λ is calculated t The formula is as follows:

[0039]

[0040] Where t is the observation time; L is the trajectory sequence length; |δ| max is the residual sequence δ t The maximum value of is a sequence of adjacent trajectories that does not contain the current data point; is the trajectory sequence containing the current data point; ρ(t) is the correlation coefficient of the residuals of the two trajectory sequences; τ(t) is the similarity between adjacent sequences.

[0041] In step (3-3), the objective function is constructed as follows:

[0042]

[0043] Among them, y t is the initial positioning value of the space target in the ground-fixed coordinate system in step (1).

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention monitors the changes in the infrared radiation intensity of the target in real time, accurately identifies the flight phase of the target, and performs segmented trajectory fitting, thereby improving the accuracy and reliability of space-based space target positioning and tracking. The method is based on a space-based platform and uses an infrared sensor to monitor the radiation characteristics of the space target in real time. By analyzing the infrared radiation characteristics of the target in different flight phases, the boost phase, mid-stage and re-entry phase of the target are identified in real time. For different flight phases, an adaptive segmented fitting method is used to fit each segment of the trajectory separately to improve the fitting accuracy of the overall trajectory. The method can handle the trajectory changes of the target in multiple motion modes, is suitable for complex space target observation environments, and achieves high-precision positioning and tracking, so that the target's velocity information can be obtained based on high-precision trajectory information. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a segmented fitting method for space-based space target trajectory based on infrared radiation characteristics according to an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of dual-star positioning observation in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be pointed out that the embodiments described below are intended to facilitate the understanding of the present invention and do not have any limiting effect on the present invention.

[0049] like Figure 1 As shown in the figure, a space-based space target trajectory segmented fitting method based on infrared radiation characteristics is proposed. First, the initial observation position information of the space high-speed target in the ground fixed system is obtained by the dual-satellite positioning method; secondly, the radiation intensity information of the space target is monitored in real time by the infrared sensor, and the change of infrared radiation intensity is analyzed to obtain the classification points between different flight stages; finally, combined with the target motion characteristics, the least squares method with adaptive factors is introduced to eliminate the influence of outlier data, and the trajectory optimization and precise fitting of the space high-speed target in the space-based scenario is completed, high-precision positioning and tracking are achieved, and the motion characteristics of the target are obtained.

[0050] The specific steps include:

[0051] S01. Obtain initial positioning information of space targets

[0052] The positioning of high-speed targets in space by the space-based observation system is actually to convert the position information of the detected target on the two-dimensional image plane into the position information of the target in the three-dimensional space fixed system.

[0053] like Figure 2 As shown, They are observation satellites S 1 , S 2 For the observation sight line vector of the space target M, firstly, the present invention needs to convert the position of the target on the image plane into the sight line vector in the ground fixed system through a strict geometric positioning model:

[0054]

[0055] in, is the line of sight vector of the satellite pointing to the target; R ref (θ) is the plane reflection matrix corresponding to the pointing mirror reflection angle θ; θ is the reflection angle value of the pointing mirror; is the conversion relationship between the geocentric inertial coordinate system and the geocentric earth-fixed coordinate system, and t is the Julian time at the observation time; is the transformation matrix from the satellite body coordinate system to the geocentric inertial coordinate system; is the calibrated installation matrix of the camera in the satellite body coordinate system; (i 0 ,j 0 ) is the coordinate of the camera principal point in the pixel coordinate system; Δi 0 ,Δj 0 is the principal point offset error; Δx, Δy are the distortions of the image point in the x and y directions; dx, dy are the dimensions of the pixel in the x and y directions; f is the principal distance of the camera;

[0056] Secondly, according to the vector addition principle, the position of the target M can be expressed as:

[0057]

[0058] Among them, M(x t ,y t ,z t ) is the position of the target under the ground fixed system, ∥S i -M∥ is the distance between the satellite and the target. When there are two satellites, expand the components in each direction Arranged into matrix form, we can get:

[0059]

[0060] Among them, (x s1 ,y s1 ,z s1 ),(x s2 ,ys2 ,z s2 ) are satellites S 1 , S 2 Position under ground anchorage.

[0061] The result of the above formula is abbreviated as A·M=b, and the position of the target under the ground fixed system is solved by the least squares criterion as follows:

[0062] M=(A T A) -1 A T b

[0063] At this point, the present invention completes the solution of the target observation positioning position information.

[0064] S02. Trajectory segmentation based on radiation characteristics

[0065] During the flight of the target aircraft, each classification point can be effectively found according to the radiation characteristics. In the space-based early warning system, the complex electromagnetic environment will produce noise interference, affect the effective signal of the target, and randomly cause missed alarms or false alarms. In order to evaluate the quality of the signal received by the sensor, the signal-to-noise ratio (SNR) can be used to measure the detection results. The signal-to-noise ratio equation of the detection system is:

[0066]

[0067] Among them, D 0 The equivalent aperture of the optical system (i.e. satellite camera); D * is the mean of the normalized spectral detection; τ 0 is the spectral transmittance of the optical system; τ a is the atmospheric spectral transmittance; ΔJ is the difference between the target radiation intensity and the background radiation intensity; δ is the signal process factor; F is the F number of the optical system (i.e. the aperture number of the camera); ω is the instantaneous field of view of the detector; Δf is the equivalent noise bandwidth of the detection system; and R is the distance between the target and the space-based platform.

[0068] By continuously monitoring the infrared radiation intensity and signal-to-noise ratio changes of the aircraft, the various classification points can be identified through the following steps. The classification points can be determined by detecting significant changes in the signal-to-noise ratio ΔSNR:

[0069] ΔSNR=SNR Δt+i -SNR i

[0070] Where i is the i-th moment of effective observation, Δt is the set time for observation signal-to-noise ratio change, which is generally set to an integer multiple of the system observation period.

[0071] 1) During the boost phase, the tail flame radiation intensity increases from low to high, and the signal-to-noise ratio trend increases from low to high. At the end of the boost phase, the engine stops working, the infrared radiation intensity drops sharply, and the signal-to-noise ratio SNR also decreases accordingly, and the ΔSNR will obviously drop sharply;

[0072] 2) In the middle stage, the aircraft mainly flies by inertia, and the infrared radiation intensity is low and stable. The infrared radiation at this time mainly comes from the residual heat of the aircraft surface after being heated for a period of time and the solar radiation in the space environment. At this time, the signal-to-noise ratio SNR is low, and the ΔSNR changes little;

[0073] 3) During the reentry phase, the spacecraft re-enters the atmosphere. Air friction causes the temperature of the spacecraft shell to rise, the infrared radiation intensity increases, the signal-to-noise ratio SNR increases, and the ΔSNR will increase significantly.

[0074] So far, the present invention has completed the segmentation of the target's trajectory by analyzing the infrared radiation characteristics of the target. S03. Introducing the least squares trajectory fitting improved by adaptive factors

[0075] The motion of the aircraft during the boost phase is mainly affected by engine thrust, air resistance, gravity, Coriolis force and centripetal force. Therefore, the dynamic model of the aircraft during the boost phase is:

[0076]

[0077] Where p = [x, y, z] T and v=[v x ,v y ,v z ] 2 is the position and velocity of the aircraft; a t , a d , a g , a Cor , a cen , which are engine thrust, air resistance, gravity, Coriolis force and centripetal acceleration respectively.

[0078] For the mid-stage, the vehicle is flying in a vacuum, and the atmospheric resistance to the vehicle is almost zero. In the mid-stage, the vehicle is only affected by gravity and perturbation. For the re-entry stage, the vehicle is mainly affected by atmospheric resistance, gravity and perturbation, and the vehicle has both longitudinal and lateral movements.

[0079] After completing step S01 and step S02, the positioning information and classification point information of the target are obtained. Considering the motion characteristics of the target in different segments, trajectory fitting is first performed. The main steps of fitting are as follows:

[0080] 1) Determine the fitting polynomial based on k observation data segments of given different segments:

[0081]

[0082] Among them, a i (i=0,1,…,n) are the coefficients of the fitted polynomial.

[0083] 2) Determine the residual δ t , solve the similarity coupling factor τ(t), normalization coefficient η, and calculate the adaptive weight factor λ t :

[0084]

[0085] Where t is the observation time; L is the trajectory sequence length; |δ| max is the residual sequence δ t The maximum value of A sequence of adjacent trajectories that does not contain the current data point; is the trajectory sequence containing the current data point; ρ(t) is the correlation coefficient of the residuals of the two trajectory sequences; τ(t) is the similarity between adjacent sequences.

[0086] The higher the correlation coefficient, the smoother the observation point sequence is; the lower the correlation coefficient is, the more the current point is judged to be an outlier. When the residual between the observation point and the corresponding fitting curve is larger, the similarity between the observation point and the fitting trajectory is lower, and the weight factor decreases accordingly, reducing the credibility of the observation point; conversely, when the residual is smaller, λ t Increase, improve the credibility of the observation point, through λ t Adaptively correct outliers.

[0087] 3) Convert the curve fitting problem to minimize the residual sum of squares, and the objective function is:

[0088]

[0089] Among them, y t is the initial positioning value of the space target in the ground-fixed coordinate system in step 1.

[0090] 4) The curve fitting problem is transformed into solving the minimum point of the objective function, and the unknown parameter a can be calculated using the least squares method. i , the precise fitting trajectory of the target can be obtained.

[0091] So far, the present invention has completed the positioning of space targets and high-precision segmented trajectory fitting in space-based scenarios.

[0092] The embodiments described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A space-based space target trajectory segmentation fitting method based on infrared radiation characteristics, characterized in that: The following steps are involved: (1) Obtain the initial positioning information of the space target in the Earth-fixed coordinate system through dual-satellite joint observation; (2) Using infrared sensors to monitor the infrared radiation intensity of space targets in real time, identify the classification points of space targets at different flight stages based on the changes in infrared radiation intensity and signal-to-noise ratio, and complete target trajectory segmentation; (3) For different flight phases, the least squares method with adaptive factors is introduced to complete the trajectory optimization and precise fitting of space targets in space-based scenarios, achieve high-precision positioning and tracking, and obtain the motion characteristics of the target.

2. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 1 is characterized in that: The specific process of step (1) is as follows: Observation satellites S1 and S2 obtain the position information of space target M on the image plane and convert the position information of space target M on the image plane into the line of sight vector in the earth-fixed coordinate system. According to the vector addition principle, the position of the space target M is expressed as In the formula, ||S i -M|| is the i-th observation satellite S i The distance between the space target and the space target; the coordinates of the space target M in the ground-fixed coordinate system are (x t ,y t ,z t ); When there are two satellites, expand the components in each direction. Arranged into matrix form, we get: Among them, (x s1 ,y s1 ,z s1 )、(x s2 ,y s2 ,z s2 ) are the positions of the observation satellites S1 and S2 in the earth-fixed coordinate system respectively; the result of the above formula is abbreviated as A·M=b, and the position of the space target in the earth-fixed coordinate system is solved by the least squares criterion as follows: M=(A T A) -1 A T b After the solution is completed, the initial positioning information of the space target in the earth-fixed coordinate system is obtained.

3. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 2 is characterized in that: The position information of the space target M on the image plane is converted into the sight vector in the ground-fixed coordinate system through the geometric positioning model. The formula is: In the formula, R is the line of sight vector of the satellite pointing to the space target; ref (θ) is the plane reflection matrix corresponding to the pointing mirror reflection angle θ; θ is the pointing mirror reflection angle value; is the conversion relationship between the geocentric inertial coordinate system and the geocentric earth-fixed coordinate system, and t is the Julian time at the observation time; is the transformation matrix from the satellite body coordinate system to the geocentric inertial coordinate system; is the calibrated installation matrix of the camera in the satellite coordinate system; (i0, j0) is the coordinate of the camera principal point in the pixel coordinate system; Δi0, Δj0 is the camera principal point offset error; dx, dy is the size of the pixel in the x and y directions; Δx, Δy is the distortion of the image point in the x and y directions; f is the camera principal distance.

4. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 1 is characterized in that: In step (2), the signal-to-noise ratio equation is: Where D0 is the equivalent aperture of the camera; D * is the mean value of the normalized spectral detection; τ0 is the spectral transmittance of the optical system; τ a is the atmospheric spectral transmittance; ΔJ is the difference between the target radiation intensity and the background radiation intensity; δ is the signal process factor; F is the aperture number of the camera; ω is the instantaneous field of view of the detector; Δf is the equivalent noise bandwidth of the detection system; R is the distance between the space target and the space-based platform.

5. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 4 is characterized in that: In step (2), the classification points of the space target at different flight stages are identified according to the changes in infrared radiation intensity and signal-to-noise ratio, specifically: Detect the change in the signal-to-noise ratio ΔSNR, the formula is: ΔSNR=SNR Δt+i -SNR i Among them, i is the i-th moment of effective observation, Δt is the set time of observation signal-to-noise ratio change, which is set to an integer multiple of the system observation period; According to the changes in infrared radiation intensity and signal-to-noise ratio, the flight process of a space target is divided into the boost phase, mid-phase and re-entry phase.

6. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 4 is characterized in that: The specific process of step (3) is as follows: (3-1) Determine the fitting polynomial based on k observation data segments of given different segments: Among them, a i are the coefficients of the polynomial to be fitted, i = 0, 1, ..., n; (3-2) Determine the residual δ t , solve the similarity coupling factor τ(t), normalization coefficient η, and further calculate the adaptive weight factor λ t ; (3-3) The curve fitting problem is converted into minimizing the residual sum of squares and constructing the objective function; (3-4) The curve fitting problem is transformed into solving the minimum point of the objective function, and the least squares method is used to calculate the unknown parameter a i , that is, obtaining the precise fitting trajectory of the target.

7. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 6 is characterized in that: In step (3-2), the adaptive weight factor λ is calculated t The formula is as follows: Where t is the observation time; L is the trajectory sequence length; |δ| max is the residual sequence δ t The maximum value of is a sequence of adjacent trajectories that does not contain the current data point; is the trajectory sequence containing the current data point; ρ(t) is the correlation coefficient of the residuals of the two trajectory sequences; τ(t) is the similarity between adjacent sequences.

8. The space-based space target trajectory segmented fitting method based on infrared radiation characteristics according to claim 6 is characterized in that: In step (3-3), the objective function is constructed as follows: Among them, y t is the initial positioning value of the space target in the ground-fixed coordinate system in step (1).

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