Multi-target multi-mode fusion positioning method based on motion platform

Abstract

The invention relates to an optical positioning method, in particular to a multi-target multi-mode fusion positioning method based on a motion platform, and solves the problem that an existing optical positioning mode cannot meet target positioning requirements for complex application scenes with complex observation backgrounds, long observation distances, multiple and dense target image points and long observation arc segments. When intersection positioning is carried out on multiple targets, laser ranging positioning is carried out on a key main tracking target, intersection measurement positioning is carried out on targets observed by two measurement stations at the same time, single-satellite pure-angle positioning is carried out on a target observed by only a single measurement station, and target positioning can be realized by using single-station and double-station observation data; therefore, the target measurement arc length is increased, and long arc section positioning of more targets can be realized under limited measurement station resources.

Description

Technical Field

[0001] This invention relates to optical positioning methods, specifically to a multi-target, multi-mode fusion positioning method based on a motion platform. Background Technology

[0002] High-precision positioning technology for multiple targets in space is one of the core technologies in the field of space target observation. Due to the high positioning accuracy of its optical measurement methods, it has been widely applied. Currently, commonly used optical positioning methods include active and passive approaches. Specific positioning techniques include rendezvous measurement positioning, laser ranging positioning, and single-satellite pure angle positioning. All of these techniques can be used to acquire spatiotemporal information such as the three-dimensional coordinates, velocity, and acceleration of a target point. Currently, the most widely used optical positioning technologies are rendezvous measurement positioning, laser ranging positioning, and single-satellite pure angle positioning.

[0003] Among them, rendezvous and positioning technology, due to its large imaging field of view, can achieve high-precision positioning of multiple targets simultaneously and has high real-time performance, and has been widely used in space target positioning and observation. Rendezvous and positioning technology mainly uses station coordinates and line-of-sight data to calculate the spatial three-dimensional coordinates of the target, and achieves high-precision positioning of the target by simultaneously observing the target from two measurement stations. In the application of rendezvous and positioning technology, an important factor affecting the rendezvous accuracy is the rendezvous angle. If the rendezvous angle is less than 30° or greater than 150°, the positioning accuracy is poor. The rendezvous angle depends on the relative position of the target and the measurement station. Since both the target and the measurement station are in motion, during the target's long arc movement, the rendezvous angle may be less than 30° or greater than 150°, resulting in poor positioning accuracy.

[0004] Laser ranging and positioning technology, as an active observation positioning method, has the advantage of high positioning accuracy and is widely used in the field of space target observation. It calculates the target's three-dimensional spatial coordinates using distance and line-of-sight data. However, a single laser rangefinder can only locate one target at a time, making it difficult to meet the need for simultaneous positioning of multiple targets.

[0005] Single-satellite pure angle positioning technology is a single-station positioning method based on prior information of the target's three-dimensional spatial position. It mainly uses line-of-sight data to estimate the target's three-dimensional spatial coordinates in real time. Its three-dimensional trajectory convergence takes a period of time, and the positioning accuracy is relatively poor.

[0006] In summary, rendezvous measurement positioning, laser ranging positioning, and single-satellite pure angle positioning each have their advantages and disadvantages. However, for complex application scenarios with complex observation backgrounds, long observation distances, numerous and dense target image points, and long observation arcs, it is difficult to meet the target positioning requirements using any single one of these three methods. Therefore, there is an urgent need to find a technical means that can meet the target positioning requirements of the aforementioned complex application scenarios. Summary of the Invention

[0007] The purpose of this invention is to solve the technical problem that existing optical positioning methods cannot meet the target positioning requirements in complex application scenarios with complex observation backgrounds, long observation distances, numerous and dense target image points, and long observation arcs. This invention provides a multi-target, multi-mode fusion positioning method based on a motion platform.

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: A multi-target, multi-mode fusion localization method based on a motion platform, characterized by the following steps: S1. Install two photoelectric precision tracking devices on two motion platforms to form two measuring stations, which are numbered as the first measuring station and the second measuring station, respectively, and install the laser rangefinder on the first measuring station. Angle measuring components are installed on the first and second measuring stations respectively. The angle measuring components acquire the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform in real time. S2. Use a laser rangefinder to measure the distance data of the main tracking target. This distance data includes the positioning time and the target distance. Measurement data of all targets within their respective fields of view are obtained using the first and second measuring stations. This measurement data includes the positioning time, target number, line-of-sight data, and station coordinates. The line-of-sight data is the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform, which are obtained in real time by the angle measuring component. The orientation of the target relative to the motion platform in the Earth's inertial coordinate system is calculated by using the attitude of the motion platform in the Earth's inertial coordinate system, i.e., the line-of-sight data of the target. S3. Convert all line-of-sight data of targets measured by the first and second measuring stations into azimuth and pitch angles of the targets in the Earth's inertial coordinate system. S4. The spatial three-dimensional coordinates of the main tracking target are calculated by using the target distance of the main tracking target measured by the laser rangefinder and the azimuth and elevation angles of the main tracking target in the Earth's inertial coordinate system measured by the first measuring station. S5. Using the station coordinates of the first and second measuring stations, as well as the azimuth and elevation angles of all targets within their respective fields of view measured by the two stations in the Earth's inertial frame, all targets measured by the first and second measuring stations are matched using a two-station target image point matching algorithm to obtain inter-station matching results. One set of matching results corresponds to one target. S6. Based on the inter-station matching results, calculate the spatial three-dimensional coordinates of the target with matching results using the intersection principle; S7. The spatial three-dimensional coordinates of the main tracking target are fused with the spatial three-dimensional coordinates of the main tracking target calculated using the intersection principle to obtain the final spatial three-dimensional coordinates of the main tracking target. S8. Based on the spatial three-dimensional coordinates of the targets within the respective fields of view of the first and second measuring stations, select all targets whose spatial three-dimensional coordinates are not among the target numbers. If a target without spatial three-dimensional coordinates meets the single-star pure angle positioning condition, then use the single-star pure angle positioning algorithm to obtain the spatial three-dimensional position of the corresponding target; if a target without spatial three-dimensional coordinates does not meet the single-star pure angle positioning condition, then the spatial three-dimensional coordinates of this target are not output at the current time. The single-star pure angle positioning condition is: the target has spatial three-dimensional coordinates at the previous positioning time and has line-of-sight data at the current positioning time.

[0009] Further, step S3 specifically involves converting the line-of-sight data of the target measured by the first and second measuring stations into the azimuth angle of the target in the Earth's inertial coordinate system. and pitch angle ; ; in, , , These are the three components of the target line-of-sight data.

[0010] Furthermore, in step S5, the target image point matching algorithm between the two stations is as follows: The coordinates of the first measuring station are defined as (X1, Y1, Z1), and the coordinates of the second measuring station are defined as (X2, Y2, Z2). At the current moment, the line-of-sight data unit vector of any target o1 within the field of view of the first measuring station relative to the first measuring station is... The line-of-sight data unit vector of any target o2 within the field of view of the second measuring station relative to the second measuring station is: Then the skew straight-line distance between the unit vectors of the two line-of-sight data is... for: ; skew straight-line distance between two line-of-sight data unit vectors Adaptive threshold for: ; in, This represents the upper limit of measurement error in the X-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Y-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Z-axis direction of the station location coordinates. This represents the upper limit of the azimuth measurement error relative to the target station. This represents the upper limit of the measurement error for the elevation angle of the target relative to the measuring station. The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines with respect to variables The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives; Accumulate the skew straight-line distance d of the target over N consecutive positioning times and its adaptive threshold. The multi-time average skew line distances between all targets within the field of view of the first measuring station and all targets within the field of view of the second measuring station were calculated. and multi-time average non-linear distance adaptive threshold ; If the average non-planar straight-line distance between two targets at multiple time points is greater than the adaptive threshold for the average non-planar straight-line distance at multiple time points, then the average non-planar straight-line distance between the two targets at multiple time points is set to infinity. The minimum value of the average non-planar straight-line distance between the two targets at multiple time points in the field of view is used as the criterion, and the inter-station matching results of all targets between the first and second measurement stations are calculated using the Hungarian algorithm.

[0011] Furthermore, in step S8, the state equation of the single-star pure angle positioning algorithm is expressed as: ; In the formula, Indicates time, For a moment The state vector is composed of the target's position and velocity. For system process noise, The sampling interval is... Let be a parameter matrix, and ; The measurement equation for the single-star pure angle positioning algorithm is expressed as: ; In the formula, The measured value of the target. For measurement functions, To measure noise, For the station location coordinates, The target at time t is obtained from the state equation of the single-star pure angle positioning algorithm. The three-dimensional coordinates of the space below.

[0012] Compared with the prior art, the beneficial effects of the present invention are: The multi-target, multi-mode fusion positioning method based on a motion platform provided by this invention performs laser ranging positioning on the main tracking target when performing intersection positioning on multiple targets, performs intersection measurement positioning on targets observed by two measurement stations simultaneously, and performs single-star pure angle positioning on targets observed by only a single measurement station. Target positioning can be achieved using both single-station and dual-station observation data, thereby increasing the target measurement arc length and enabling long-arc segment positioning of more targets with limited measurement station resources. Attached Figure Description

[0013] Figure 1 This is a flowchart of an embodiment of the multi-target, multi-mode fusion localization method based on a motion platform according to the present invention. Detailed Implementation

[0014] according to Figure 1 As shown, the multi-target, multi-mode fusion localization method based on a motion platform provided by this invention includes the following steps: S1. Install two photoelectric precision tracking devices on two motion platforms to form two measuring stations, which are numbered as the first measuring station and the second measuring station, respectively, and install the laser rangefinder on the first measuring station. Angle measuring components are installed on the first and second measuring stations respectively. The angle measuring components acquire the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform in real time. S2. Use a laser rangefinder to measure the distance data of the main tracking target. This distance data includes the positioning time and the target distance. Measurement data of all targets within their respective fields of view are obtained using the first and second measuring stations. This measurement data includes the positioning time, target number, line-of-sight data, and station coordinates. The line-of-sight data is the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform, which are obtained in real time by the angle measuring component. The orientation of the target relative to the motion platform in the Earth's inertial coordinate system is calculated by using the attitude of the motion platform in the Earth's inertial coordinate system, i.e., the line-of-sight data of the target. S3: Convert the line-of-sight data of all targets measured by the first and second measuring stations into the azimuth angle of the targets in the Earth's inertial coordinate system. and pitch angle ; ; in, , , These are the three components of the target line-of-sight data; S4. The spatial three-dimensional coordinates of the main tracking target are calculated by using the target distance of the main tracking target measured by the laser rangefinder and the azimuth and elevation angles of the main tracking target in the Earth's inertial coordinate system measured by the first measuring station. S5. Using the station coordinates of the first and second measuring stations, as well as the azimuth and elevation angles of all targets within their respective fields of view measured by the two stations in the Earth's inertial frame, all targets measured by the first and second measuring stations are matched using a two-station target image point matching algorithm to obtain inter-station matching results. One set of matching results corresponds to one target. The target image point matching algorithm for the two stations is as follows: The coordinates of the first measuring station are defined as (X1, Y1, Z1), and the coordinates of the second measuring station are defined as (X2, Y2, Z2). At the current moment, the line-of-sight data unit vector of any target o1 within the field of view of the first measuring station relative to the first measuring station is... The line-of-sight data unit vector of any target o2 within the field of view of the second measuring station relative to the second measuring station is: Then the skew straight-line distance between the unit vectors of the two line-of-sight data is... for: ; Adaptive threshold for the skew line distance d between unit vectors of two line-of-sight data for: ; in, This represents the upper limit of measurement error in the X-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Y-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Z-axis direction of the station location coordinates. This represents the upper limit of the azimuth measurement error relative to the target station. This represents the upper limit of the measurement error for the elevation angle of the target relative to the measuring station. The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines with respect to variables The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives; Accumulate the skew straight-line distance d of the target over N consecutive positioning times and its adaptive threshold. The multi-time average skew line distances between all targets within the field of view of the first measuring station and all targets within the field of view of the second measuring station were calculated. and multi-time average non-linear distance adaptive threshold ; If the average non-planar straight-line distance between two targets at multiple times is greater than the adaptive threshold for the average non-planar straight-line distance at multiple times, then the average non-planar straight-line distance between the two targets at multiple times is set to infinity. The minimum value of the average non-planar straight-line distance between the two targets at multiple times in the field of view is used as the criterion, and the inter-station matching results of all targets between the first and second measurement stations are calculated using the Hungarian algorithm. S6. Based on the inter-station matching results, calculate the spatial three-dimensional coordinates of the target with matching results using the intersection principle; S7. The spatial three-dimensional coordinates of the main tracking target are fused with the spatial three-dimensional coordinates of the main tracking target calculated using the intersection principle to obtain the final spatial three-dimensional coordinates of the main tracking target. S8. Based on the spatial three-dimensional coordinates of the targets within the respective fields of view of the first and second measuring stations, select all targets whose spatial three-dimensional coordinates are not among the target numbers. If a target without spatial three-dimensional coordinates meets the single-star pure angle positioning condition, then use the single-star pure angle positioning algorithm to obtain the spatial three-dimensional position of the corresponding target; if a target without spatial three-dimensional coordinates does not meet the single-star pure angle positioning condition, then the spatial three-dimensional coordinates of this target are not output at the current time. The state equation of the single-star pure angle positioning algorithm is expressed as follows: ; In the formula, Indicates time, For a moment The state vector is composed of the target's position and velocity. For system process noise, The sampling interval is... Let be a parameter matrix, and ; The measurement equation for the single-star pure angle positioning algorithm is expressed as: ; In the formula, The measured value of the target. For measurement functions, To measure noise, For the station location coordinates, The target at time t is obtained from the state equation of the single-star pure angle positioning algorithm. The following three-dimensional coordinates in space; The single-star pure angle positioning condition is: the target has spatial three-dimensional coordinates at the previous positioning time and has line-of-sight data at the current positioning time.

[0015] The embodiments described above are merely illustrative of specific implementations of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A multi-target, multi-mode fusion localization method based on a motion platform, characterized in that, Includes the following steps: S1. Install two photoelectric precision tracking devices on two motion platforms to form two measuring stations, which are numbered as the first measuring station and the second measuring station, respectively, and install the laser rangefinder on the first measuring station. Angle measuring components are installed on the first and second measuring stations respectively. The angle measuring components acquire the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform in real time. S2. Use a laser rangefinder to measure the distance data of the main tracking target. This distance data includes the positioning time and the target distance. Measurement data of all targets within their respective fields of view are obtained using the first and second measuring stations. This measurement data includes the positioning time, target number, line-of-sight data, and station coordinates. The line-of-sight data is the azimuth and pitch angles of the line of sight in the coordinate system of the motion platform, which are obtained in real time by the angle measuring component. The orientation of the target relative to the motion platform in the Earth's inertial coordinate system is calculated by using the attitude of the motion platform in the Earth's inertial coordinate system, i.e., the line-of-sight data of the target. S3. Convert all line-of-sight data of targets measured by the first and second measuring stations into azimuth and pitch angles of the targets in the Earth's inertial coordinate system. S4. The spatial three-dimensional coordinates of the main tracking target are calculated by using the target distance of the main tracking target measured by the laser rangefinder and the azimuth and elevation angles of the main tracking target in the Earth's inertial coordinate system measured by the first measuring station. S5. Using the station coordinates of the first and second measuring stations, as well as the azimuth and elevation angles of all targets within their respective fields of view measured by the two stations in the Earth's inertial frame, all targets measured by the first and second measuring stations are matched using a two-station target image point matching algorithm to obtain inter-station matching results. One set of matching results corresponds to one target. S6. Based on the inter-station matching results, calculate the spatial three-dimensional coordinates of the target with matching results using the intersection principle; S7. The spatial three-dimensional coordinates of the main tracking target are fused with the spatial three-dimensional coordinates of the main tracking target calculated using the intersection principle to obtain the final spatial three-dimensional coordinates of the main tracking target. S8. Based on the spatial three-dimensional coordinates of the targets within the respective fields of view of the first and second measuring stations, select all targets whose numbers do not contain spatial three-dimensional coordinates. If a target without spatial three-dimensional coordinates meets the single-star pure angle positioning condition, then use the single-star pure angle positioning algorithm to obtain the spatial three-dimensional position of the corresponding target. If a target without spatial three-dimensional coordinates does not meet the conditions for single-star pure angle positioning, then the target will not output spatial three-dimensional coordinates at the current moment. The single-star pure angle positioning condition is: the target has spatial three-dimensional coordinates at the previous positioning time and has line-of-sight data at the current positioning time; Complete multi-target, multi-mode fusion positioning based on a motion platform.

2. The multi-target, multi-mode fusion localization method based on a motion platform according to claim 1, characterized in that, Step S3 is as follows: The line-of-sight data of the target measured by the first and second measuring stations were converted into the azimuth angle of the target in the Earth's inertial coordinate system. and pitch angle ; ； in, , , These are the three components of the target line-of-sight data.

3. The multi-target, multi-mode fusion positioning method based on a motion platform according to claim 2, characterized in that, In step S5, the target image point matching algorithm between the two stations is as follows: The coordinates of the first measuring station are defined as (X1, Y1, Z1), and the coordinates of the second measuring station are defined as (X2, Y2, Z2). At the current moment, the line-of-sight data unit vector of any target o1 within the field of view of the first measuring station relative to the first measuring station is... The line-of-sight data unit vector of any target o2 within the field of view of the second measuring station relative to the second measuring station is: Then, at the current positioning time, the skew straight-line distance d between the unit vectors of the line-of-sight data of the two targets is: ； The adaptive threshold for the skew straight-line distance d between the unit vectors of the line-of-sight data of the two targets at the current positioning time. for: ； in, This represents the upper limit of measurement error in the X-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Y-axis direction of the station location coordinates. This represents the upper limit of measurement error in the Z-axis direction of the station location coordinates. This represents the upper limit of the azimuth measurement error relative to the target station. This represents the upper limit of the measurement error for the elevation angle of the target relative to the measuring station. The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines with respect to variables The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives, The distance function between skew lines is a function of variables. The partial derivatives; Accumulate the skew straight-line distance d between the target and its adaptive threshold over N consecutive positioning times. The multi-time average skew line distances between all targets within the field of view of the first measuring station and all targets within the field of view of the second measuring station were calculated. and multi-time average non-linear distance adaptive threshold ; If the average non-planar straight-line distance between two targets at multiple time points is greater than the adaptive threshold for the average non-planar straight-line distance at multiple time points, then the average non-planar straight-line distance between the two targets at multiple time points is set to infinity. The minimum value of the average non-planar straight-line distance between the two targets at multiple time points in the field of view is used as the criterion, and the inter-station matching results of all targets between the first and second measurement stations are calculated using the Hungarian algorithm.

4. The multi-target, multi-mode fusion localization method based on a motion platform according to claim 3, characterized in that: In step S8, the state equation of the single-star pure angle positioning algorithm is expressed as: ； In the formula, Indicates time, For a moment The state vector is composed of the target's position and velocity. For system process noise, The sampling interval is... Let be a parameter matrix, and ； The measurement equation for the single-star pure angle positioning algorithm is expressed as follows: ； In the formula, The measured value of the target. For measurement functions, To measure noise, For the station location coordinates, The target at time t is obtained from the state equation of the single-star pure angle positioning algorithm. The three-dimensional coordinates of the space below.

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