Feature constraint-based active and passive combined space-based moving target positioning method

Abstract

The invention relates to an active and passive combined space-based moving target positioning method based on feature constraint, and the method comprises the steps: S1, obtaining a measurement value of a target position, and the measurement value comprises an azimuth angle sequence, a pitch angle sequence and a distance sequence; s2, performing segmented constant acceleration process filtering on the azimuth angle sequence and the pitch angle sequence; s3, obtaining a coordinate value of the remote sensor under the earth-fixed system, and calculating to obtain a three-dimensional position measurement value of the target under the earth-fixed system; s4, obtaining a positioning correction result in the error elliptical plane through coordinate transformation and height constraint correction; s5, carrying out constant acceleration motion model filtering on the positioning correction result of the error elliptical plane; and step S6, repeatedly executing the step S4 and the step S5 until convergence, and obtaining a final error correction result. According to the invention, the positioning precision in an active and passive integrated detection scene is improved.

Description

Technical Field

[0001] The present invention relates to the field of optical remote sensing technology, and in particular to an active-passive combined space-based moving target positioning method based on feature constraints. Background Art

[0002] Optical remote sensing is a passive measurement method that images reflected and radiated energy. A single remote sensor cannot measure the distance between the target and the remote sensor, and can only measure the angles in pitch and azimuth. The use of dual-satellite observations allows for stereo intersection positioning, but this will increase the cost of the remote sensor. At the same time, there are constraints on observation conditions such as the intersection angle, which affects the application efficiency of the system. On the basis of traditional passive remote sensing imaging, a laser measurement device is added to perform active and passive integrated target measurement. A single satellite can measure angles and distances, thereby obtaining three-dimensional information of the target. It has indicators and cost advantages in certain scenarios, and is currently one of the important technical development directions for optical remote sensing detection.

[0003] However, in actual detection applications, there is still much room for technical improvement in how to deeply mine the information detected by active and passive integrated detection and make full use of characteristic constraints to improve the quality of detection information. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an active and passive combined space-based moving target positioning method based on feature constraints, which fully combines the characteristics of the active and passive integrated detection error model, takes into account characteristic constraints such as target motion, and improves the positioning accuracy in the active and passive integrated detection scenario.

[0005] To achieve the above-mentioned object of the invention, the present invention provides an active and passive combined space-based moving target positioning method based on feature constraints, comprising the following steps:

[0006] Step S1, obtaining measurement values ​​of the target position, wherein the measurement values ​​include an azimuth angle sequence, a pitch angle sequence and a distance sequence;

[0007] Step S2, performing segmented constant acceleration process filtering on the azimuth angle sequence and the pitch angle sequence;

[0008] Step S3, obtaining the coordinate value of the remote sensor in the ground fixed system, and calculating the three-dimensional position measurement value of the target in the ground fixed system;

[0009] Step S4, obtaining a positioning correction result in the error ellipse plane through coordinate conversion and height constraint correction;

[0010] Step S5, performing constant acceleration motion model filtering on the positioning correction result of the error ellipse plane;

[0011] Step S6, repeatedly executing step S4 and step S5 until convergence, to obtain a final error correction result.

[0012] According to a technical solution of the present invention, in step S2, a quadratic polynomial is used to fit and filter the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) based on a motion error model to obtain corrected azimuth angle results α1(t) and pitch angle results β1(t), and the corrected target position in the image plane polar coordinate system is [α1(t), β1(t), R0(t)].

[0013] According to a technical solution of the present invention, in step S2, the parameter equation for fitting and filtering the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) is: F(t)=a*t^2+b*t+c;

[0014] Among them, t is the independent variable of fitting, F(t) is the dependent variable of fitting, and the fitting method is the least squares method; when F(t) is α0(t), the parameter matching result of α1(t) is obtained; when F(t) is β0(t), the parameter matching result of β1(t) is obtained.

[0015] According to a technical solution of the present invention, the step S2 further includes:

[0016] Through coordinate transformation, the target position [α1(t), β1(t), R0(t)] in the image plane polar coordinate system is transformed into the target position [Xm0(t), Ym0(t), Zm0(t)] in the image plane Cartesian coordinate system, where:

[0017] Xm0(t)=R0(t)*cos(β1(t))*cos(α1(t));

[0018] Ym0(t)=R0(t)*cos(β1(t))*sin(α1(t));

[0019] Zm0(t)=R0(t)*sin(β1(t)).

[0020] According to a technical solution of the present invention, step S3 specifically includes:

[0021] Step S31, obtaining the coordinate value of the remote sensor in the ground fixed system;

[0022] Step S32: Obtain the three-dimensional position measurement value of the target in the fixed system at a single time point through coordinate transformation, expressed as:

[0023]

[0024] Among them, [Xm1(t), Ym1(t), Zm1(t)] represents the position coordinates of the target in the ground-fixed system at a single time point, and Ris represents the coordinate transformation matrix of the camera system to the ground-fixed system.

[0025] According to a technical solution of the present invention, the step S4 specifically includes:

[0026] Step S41, convert the position coordinates [Xm1(t), Ym1(t), Zm1(t)] in the ground fixed system into the longitude, latitude and height form [Lat(t), Lon(t), H(t)], expressed as:

[0027]

[0028] Among them, Rdj represents the conversion matrix from the rectangular coordinates of the earth-fixed system to the longitude and latitude;

[0029] Step S42, obtain the corrected latitude and longitude high coordinates [Lat1(t), Lon1(t), H1(t)] through height constraint.

[0030] According to a technical solution of the present invention, the step S42 specifically includes:

[0031] The nearest intersection point between the error plane and the height constraint channel is taken as the correction value, and coordinate transformation is performed to obtain the corrected positioning result [Xm2(t), Ym2(t), Zm2(t)], which is expressed as:

[0032]

[0033] According to a technical solution of the present invention, in step S5, the method for filtering based on the motion model is:

[0034]

[0035] Among them, a11, a12, and a13 are obtained by fitting a quadratic polynomial based on Xm2(t) with t as the variable; b11, b12, and b13 are obtained by fitting a quadratic polynomial based on Ym2(t) with t as the variable; and c11, c12, and c13 are obtained by fitting a quadratic polynomial based on Zm2(t) with t as the variable.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention proposes an active-passive combined space-based moving target positioning method based on feature constraints, which makes full use of the error distribution characteristics of active and passive integration and applies a filtering model in the angle measurement stage to improve the positioning accuracy at each moment.

[0038] The present invention uses a height-constrained pipeline to perform error correction, which can further reduce the three-dimensional position error.

[0039] The present invention combines height constraint and three-dimensional motion constraint to further improve positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 Schematic diagram showing the distribution of measurement errors in the active-passive combination system;

[0042] Figure 2 Schematically showing a flow chart of a method for combining active and passive space-based moving target positioning based on feature constraints according to an embodiment of the present invention;

[0043] Figure 3 Schematically showing the results before and after correction of a time measurement sequence based on characteristic constraints according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, the basic application scenario of the present invention is that the remote sensor performs active and passive integrated detection of the target. The measured value of the target position is generally two angles and one direction in the image plane coordinate system, namely azimuth, pitch angle and detection distance. The azimuth and pitch angle errors are larger than the distance direction. After analysis, the positioning error distribution of the target is close to an ellipse.

[0046] like Figures 1 to 3 As shown, the present invention provides a method for positioning a space-based moving target by combining active and passive features based on feature constraints, comprising the following steps:

[0047] Step S1, obtaining measurement values ​​of the target position, wherein the measurement values ​​include an azimuth angle sequence, a pitch angle sequence and a distance sequence;

[0048] Step S2, performing segmented constant acceleration process filtering on the azimuth angle sequence and the pitch angle sequence;

[0049] Since the distance error is relatively small, a filter can be performed on the two measured angles. The more classic one is a quadratic polynomial in a local time period to characterize the constant acceleration process. Therefore, the step S2 specifically includes: fitting and filtering the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) to obtain the corrected azimuth angle result α1(t) and pitch angle result β1(t), then the corrected target position in the image plane polar coordinate system is [α1(t), β1(t), R0(t)];

[0050] Among them, the parameter equation for fitting and filtering the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) is: F(t) = a*t^2+b*t+c;

[0051] Among them, t is the independent variable of fitting, F(t) is the dependent variable of fitting, and the fitting method is the least squares method; when F(t) is α0(t), the parameter matching result of α1(t) is obtained; when F(t) is β0(t), the parameter matching result of β1(t) is obtained.

[0052] In some embodiments of the present invention, the step S2 further includes:

[0053] Through coordinate transformation, the target position [α1(t), β1(t), R0(t)] in the image plane polar coordinate system is transformed into the target position [Xm0(t), Ym0(t), Zm0(t)] in the image plane Cartesian coordinate system, where:

[0054] Xm0(t)=R0(t)*cos(β1(t))*cos(α1(t));

[0055] Ym0(t)=R0(t)*cos(β1(t))*sin(α1(t));

[0056] Zm0(t)=R0(t)*sin(β1(t)).

[0057] Step S3, obtaining the coordinate value of the remote sensor in the ground fixed system, and calculating the three-dimensional position measurement value of the target in the ground fixed system;

[0058] Since the coordinate representation of altitude constraint is different from that of motion constraint, the measured value needs to be converted into latitude and longitude coordinates first. At this time, the coordinate value of the remote sensor in the ground-fixed system should be obtained first, that is, step S31; step S32, through coordinate transformation, the three-dimensional position measurement value of the target in the ground-fixed system at a single time point is obtained, which is expressed as:

[0059]

[0060] Among them, [Xm1(t), Ym1(t), Zm1(t)] represents the position coordinates of the target in the ground-fixed system at a single time point, and Ris represents the coordinate transformation matrix of the camera system to the ground-fixed system.

[0061] Constraints based on target height and three-dimensional motion can be further used to improve positioning accuracy using feature constraints.

[0062] Step S4, obtaining a positioning correction result in the error ellipse plane through coordinate conversion and height constraint correction, specifically including:

[0063] Step S41, convert the position coordinates [Xm1(t), Ym1(t), Zm1(t)] in the ground fixed system into the longitude, latitude and height form [Lat(t), Lon(t), H(t)], expressed as:

[0064]

[0065] Among them, Rdj represents the conversion matrix from the rectangular coordinates of the earth-fixed system to the longitude and latitude;

[0066] Step S42, obtaining the corrected latitude and longitude high coordinates [Lat1(t), Lon1(t), H1(t)] through height constraint, specifically includes:

[0067] The nearest intersection point between the error plane and the height constraint channel is taken as the correction value, and coordinate transformation is performed to obtain the corrected positioning result [Xm2(t), Ym2(t), Zm2(t)], which is expressed as:

[0068]

[0069] Step S5: filtering the positioning correction result of the error ellipse plane with a constant acceleration motion model, using a quadratic polynomial in a local time period to characterize the constant acceleration process. The method for filtering based on the motion model is:

[0070]

[0071] Among them, a11, a12, and a13 are obtained by fitting a quadratic polynomial based on Xm2(t) with t as the variable; b11, b12, and b13 are obtained by fitting a quadratic polynomial based on Ym2(t) with t as the variable; and c11, c12, and c13 are obtained by fitting a quadratic polynomial based on Zm2(t) with t as the variable.

[0072] Step S6, repeatedly executing step S4 and step S5 until convergence, to obtain a final error correction result.

[0073] Figure 3The figure below shows the target position, error distribution, height constraint, measured position, and corrected position during the process. The error distribution of the target trajectory after measurement is an ellipse at each moment. After constraining and filtering the height, motion equation, and error plane at each moment, a more accurate positioning correction result can be obtained. Figure 3 It can be seen that the application of the active and passive combined space-based moving target positioning method of the present invention can improve the positioning accuracy in the active and passive integrated detection scenario.

[0074] In the present invention, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or terminal device including the element.

[0075] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A method for combining active and passive space-based moving target positioning based on feature constraints, characterized in that: The following steps are involved: Step S1, obtaining measurement values ​​of the target position, wherein the measurement values ​​include an azimuth angle sequence, a pitch angle sequence and a distance sequence; Step S2, performing segmented constant acceleration process filtering on the azimuth angle sequence and the pitch angle sequence; Step S3, obtaining the coordinate value of the remote sensor in the ground fixed system, and calculating the three-dimensional position measurement value of the target in the ground fixed system; Step S4, obtaining a positioning correction result in the error ellipse plane through coordinate conversion and height constraint correction; Step S5, performing constant acceleration motion model filtering on the positioning correction result of the error ellipse plane; Step S6, repeatedly executing step S4 and step S5 until convergence, to obtain a final error correction result.

2. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 1 is characterized in that: In step S2, a quadratic polynomial is used to fit and filter the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) based on a motion error model to obtain a corrected azimuth angle result α1(t) and a pitch angle result β1(t), and the corrected target position in the image plane polar coordinate system is [α1(t), β1(t), R0(t)].

3. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 2 is characterized in that: In step S2, the parameter equation for fitting and filtering the azimuth angle sequence α0(t) and the pitch angle sequence β0(t) is: F(t)=a*t^2+b*t+c; Among them, t is the independent variable of fitting, F(t) is the dependent variable of fitting, and the fitting method is the least squares method; when F(t) is α0(t), the parameter matching result of α1(t) is obtained; when F(t) is β0(t), the parameter matching result of β1(t) is obtained.

4. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 3 is characterized in that: The step S2 further includes: Through coordinate transformation, the target position [α1(t), β1(t), R0(t)] in the image plane polar coordinate system is transformed into the target position [Xm0(t), Ym0(t), Zm0(t)] in the image plane Cartesian coordinate system, where: Xm0(t)=R0(t)*cos(β1(t))*cos(α1(t)); Ym0(t)=R0(t)*cos(β1(t))*sin(α1(t)); Zm0(t)=R0(t)*sin(β1(t)).

5. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 4 is characterized in that: The step S3 specifically includes: Step S31, obtaining the coordinate value of the remote sensor in the ground fixed system; Step S32: Obtain the three-dimensional position measurement value of the target in the fixed system at a single time point through coordinate transformation, expressed as: Among them, [Xm1(t), Ym1(t), Zm1(t)] represents the position coordinates of the target in the ground-fixed system at a single time point, and Ris represents the coordinate transformation matrix of the camera system to the ground-fixed system.

6. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 5 is characterized in that: The step S4 specifically includes: Step S41, convert the position coordinates [Xm1(t), Ym1(t), Zm1(t)] in the ground fixed system into the longitude, latitude and height form [Lat(t), Lon(t), H(t)], expressed as: Among them, Rdj represents the conversion matrix from the rectangular coordinates of the earth-fixed system to the longitude and latitude; Step S42, obtain the corrected latitude and longitude high coordinates [Lat1(t), Lon1(t), H1(t)] through height constraint.

7. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 6 is characterized in that: The step S42 specifically includes: The nearest intersection point between the error plane and the height constraint channel is taken as the correction value, and coordinate transformation is performed to obtain the corrected positioning result [Xm2(t), Ym2(t), Zm2(t)], which is expressed as:

8. The active and passive combined space-based moving target positioning method based on feature constraints according to claim 7 is characterized in that: In step S5, the method for filtering based on the motion model is: Among them, a11, a12, and a13 are obtained by fitting a quadratic polynomial based on Xm2(t) with t as the variable; b11, b12, and b13 are obtained by fitting a quadratic polynomial based on Ym2(t) with t as the variable; and c11, c12, and c13 are obtained by fitting a quadratic polynomial based on Zm2(t) with t as the variable.

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