A multi-epoch correlated dynamic inverse synthetic aperture radio positioning method
By employing a dynamic inverse synthetic aperture method with multi-epoch correlation, and utilizing observations from multiple epoch anchor points and time-invariant trajectory parameter fitting modeling, the problem of limited positioning accuracy under small antenna aperture or long distance was solved, achieving higher positioning accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING INST OF TECH
- Filing Date
- 2025-05-15
- Publication Date
- 2026-07-31
AI Technical Summary
Conventional radio positioning methods suffer from large angle measurement errors in scenarios with small antenna apertures or long operating distances, which limits positioning accuracy.
The dynamic inverse synthetic aperture method with multi-epoch association is adopted. By acquiring the relative observations between multiple epoch anchor points and the moving target, the trajectory model of the moving target is established by fitting and modeling with time-invariant trajectory parameters, and the fitting coefficients are solved by the objective function to realize inverse synthetic aperture positioning.
By coherently synthesizing multi-epoch observations, positioning accuracy was improved, observation errors were reduced, and the positioning accuracy of moving targets was enhanced.
Smart Images
Figure CN120491036B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a dynamic inverse synthetic aperture radio positioning method with multi-epoch correlation, belonging to the fields of signal processing, navigation and positioning, aerospace telemetry and control, and target tracking technology. Background Technology
[0002] Radio navigation and positioning, including satellite navigation, ground-based navigation, and cellular signal navigation, offers advantages such as all-weather, all-time operation, long range, and high positioning accuracy, leading to its widespread application in numerous civilian and military fields. In radio navigation and positioning, the anchor point measures the relative distance, velocity, and angle with the target, and solves the equations to achieve the positioning result. The accuracy of angle measurement is typically related to the effective aperture size of the antenna; a larger antenna aperture results in higher angle measurement accuracy and a more precise calculated target position.
[0003] Conventional radio positioning methods use instantaneous distance and angle measurements taken from anchor points to locate targets. For a single anchor point, if the antenna aperture is limited, the instantaneous angle measurements will contain significant errors, degrading positioning accuracy. Furthermore, the greater the relative distance between the target and the anchor point, the greater the positioning deviation will be for the same angle measurement error. Therefore, conventional positioning methods using instantaneous observations are limited in accuracy in scenarios with small antenna apertures or long operating distances. Summary of the Invention
[0004] The purpose of this invention is to provide a dynamic inverse synthetic aperture radio positioning method based on multi-epoch correlation, which can improve positioning accuracy even when the observations have large measurement errors.
[0005] The technical solution for implementing the present invention is as follows:
[0006] In a first aspect, the present invention provides a dynamic inverse synthetic aperture radio positioning method with multi-epoch correlation, the specific process of which is as follows:
[0007] Step 1: Obtain the relative observation h between the N epoch anchor points and the moving target;
[0008] Step 2: Use time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target to obtain the trajectory model of the moving target;
[0009] Step 3: The time-varying position and velocity of the moving target in the observation h in Step 1 are represented by the moving target trajectory model. The positioning objective function of each trajectory model is established and solved to obtain the fitting coefficients in the moving target trajectory model. Based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to achieve inverse synthetic aperture positioning.
[0010] Optionally, the modeling of the time-varying position and velocity of a moving target using time-invariant trajectory parameters described in this invention is as follows:
[0011]
[0012] Where, α l β l These represent the initial epoch position, velocity fitting polynomial coefficients, and instantaneous invariant trajectory parameters, ξ. l,t is an orthogonal polynomial basis, and L is the order of the fitting coefficients.
[0013] Optionally, in step three of this invention, the time-varying position and velocity of the moving target in the observed h from step one are represented using the moving target trajectory model, specifically as follows:
[0014]
[0015] Where, ρ i,t f i,t α i,t as well as Let i represent the relative distance, velocity, azimuth, and elevation angle observations between anchor point i at epoch t and the moving target, respectively. They represent x, y, z axis components This represents the 3D position of the i-th stationary anchor point, arctan() represents the arctangent operation, and n ρ n f n α , These represent the measurement errors for distance, speed, azimuth, and elevation, respectively.
[0016] Optionally, the objective function of this invention is:
[0017]
[0018] Where, f({α,β}) L ) represents the mapping function between the coefficients of the position and velocity fitting polynomials and the relative observation h, and W is the covariance matrix of the noise of the distance, velocity, azimuth, and elevation angle observations.
[0019] Optionally, the objective function of this invention is solved using the Gauss-Newton iteration method to obtain the time invariant {α,β}. L =[α1...,α L ,β1...,β L ].
[0020] In a second aspect, the present invention provides a multi-epoch-correlated dynamic inverse synthetic aperture radio positioning device, comprising:
[0021] The observation acquisition module is used to acquire the relative observation h between N epoch anchor points and the moving target;
[0022] The target trajectory fitting module uses time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target, thereby obtaining the trajectory model of the moving target.
[0023] The synthetic aperture positioning module is used to represent the time-varying position and velocity of the moving target in the observation h using the moving target trajectory model, establish and solve the positioning objective function of each trajectory model, and obtain the fitting coefficients in the moving target trajectory model; based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to realize inverse synthetic aperture positioning.
[0024] Beneficial effects:
[0025] This invention uses the change in position of a moving target at different times as a "virtual baseline," models the trajectory of the moving target using a time-invariant polynomial to compensate for the change in the target's position, and coherently synthesizes the distance, velocity, and angle observations between the anchor point and the moving target at different times. By coherently synthesizing multiple instantaneous observations, a "effective aperture" that is much larger than a single observation is generated, thereby reducing observation errors and improving the positioning accuracy of the moving target. Attached Figure Description
[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a flowchart illustrating the process structure of the method of the present invention. Detailed Implementation
[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0029] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0030] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.
[0031] The structural block diagram of this invention is as follows: Figure 1 As shown, the embodiments of this application include three steps: multi-epoch observation extraction and merging, moving target trajectory modeling, and localization objective function establishment and solution. Figure 1 As shown.
[0032] Step 1: Obtain the relative observation h between the N epoch anchor points and the moving target;
[0033] Step 2: Use time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target to obtain the trajectory model of the moving target;
[0034] Step 3: The time-varying position and velocity of the moving target in the observation h in Step 1 are represented by the moving target trajectory model. The positioning objective function of each trajectory model is established and solved to obtain the fitting coefficients in the moving target trajectory model. Based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to achieve inverse synthetic aperture positioning.
[0035] This application embodiment combines the instantaneous distance, velocity, and angle observations of multi-epoch anchor points and the moving target to be located, coherently synthesizing them to produce an "effective aperture" much larger than that of a single instantaneous observation, thereby improving positioning accuracy. The specific process of each step is described in detail below:
[0036] (1) Extraction and merging of multi-epoch observations
[0037] Assuming the number of anchor points is M, for epoch t, D t The observation h of each anchor point and the moving target t It can be represented as
[0038]
[0039] Where, ρ i,t f i,t α i,t as well as Let i represent the relative distance, velocity, azimuth, and elevation angle (which are known quantities) between anchor point i at epoch t and the moving target, respectively. Specifically, this can be expressed as:
[0040] ρ i,t =||p t -s i ||+n ρ
[0041]
[0042] in, Represents the three-dimensional position of a moving target, () T This indicates the transpose operation. v represents the three-dimensional position of the i-th stationary anchor point. t This represents the three-dimensional velocity of a moving target, arctan() represents the arctangent operation, and n ρ n f n α , These represent the measurement errors for distance, velocity, azimuth, and elevation, respectively. In formula (2), p... t and v t The parameter is unknown and needs to be solved.
[0043] Anchor points are used for inverse synthetic aperture positioning by combining observations from N epochs. These combined observations can be expressed as...
[0044]
[0045] Within a time interval of length N, the number of observations at each epoch is 4D. t The total number of observations obtained is
[0046] (2) Modeling the trajectory of moving targets
[0047] Combining multi-epoch observations can effectively increase the number of observations. Since the position of a moving target is time-varying, combining multi-epoch observations will proportionally increase the number of variables in the positioning solution. Therefore, it is necessary to fit the time-varying position with time-invariant trajectory parameters to correlate the unknowns in the multi-epoch solution, thereby compensating for changes in the target position and coherently synthesizing the multi-epoch observations.
[0048] Considering the short-term stationary nature of the moving target's trajectory, the time-varying position and velocity of the moving target over a time interval of length N are modeled using an orthogonal polynomial model of order L, h(α1,...,α). L ,β1,...,β L To perform modeling, that is:
[0049]
[0050] Where, α l β l These represent the initial epoch position, velocity fitting polynomial coefficients, and ξ, respectively. l,t Let be the orthogonal polynomial basis functions with respect to time.
[0051] Using a trajectory model, the time-varying position p of the moving target is determined. t Time-varying velocity v t The time-invariant parameter α l β l Let l∈{1,...,L} be used for representation. The number of variables in the trajectory model solution depends only on the variables to be solved in the initial epoch trajectory model, that is, multiple epochs share the same solution variables, thereby achieving coherent synthesis of multi-epoch observations.
[0052] (3) Establishing and solving the objective function
[0053] After modeling the trajectory of the moving target using a trajectory model, multi-epoch observations are coherently synthesized, and a trajectory model parameter solution objective function is constructed for localization. The trajectory model h(α1,...,α) is then used for localization. L ,β1,...,β L By substituting the observations at epoch t, we can correlate the observations at epoch t with the parameters of the initial epoch trajectory model:
[0054]
[0055] in, They represent The x, y, and z axis components.
[0056] Combining the above equations allows for the coherent synthesis of instantaneous observations from multiple epochs, and the establishment of a coherent relationship with the unknowns {α,β} to be solved. L =[α1...,α L ,β1...,β L The relationship between them is represented as:
[0057] h = f({α,β}) L )+ε (7)
[0058] Where, f({α,β}) L ) represents the mapping function between the coefficients of the position and velocity fitting polynomial and the relative observation h, and ε represents the measurement noise.
[0059] Therefore, a localization objective function can be constructed for each trajectory model:
[0060]
[0061] Where W is the covariance matrix of the noise of the distance, velocity, azimuth, and elevation angle observations.
[0062] The objective function can be solved using the Gauss-Newton iteration method to obtain the trajectory model solution variables {α,β}. L =[α1...,α L ,β1...,β L Furthermore, based on the model parameters, the time-varying position and velocity of the moving target are deduced to achieve inverse synthetic aperture positioning.
[0063] In another embodiment of this application, a multi-epoch-correlated dynamic inverse synthetic aperture radio positioning device includes:
[0064] The observation acquisition module is used to acquire the relative observations between N epoch anchor points and the moving target;
[0065] The target trajectory fitting module uses time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target, thereby obtaining the trajectory model of the moving target.
[0066] The synthetic aperture positioning module is used to represent the time-varying position and velocity of the moving target in the observation using the moving target trajectory model, establish and solve the positioning objective function of each trajectory model, and obtain the fitting coefficients in the moving target trajectory model; based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to realize inverse synthetic aperture positioning.
[0067] This invention uses anchor points to acquire distance, velocity, and angle measurements relative to a moving target at different times. The changes in the target's position at different times are used as a "virtual baseline." The trajectory of the moving target is modeled using a time-invariant polynomial, and the changes in the target's position are compensated. Multiple instantaneous observations are coherently synthesized to produce an "effective aperture" that is much larger than a single observation, thereby improving the angle measurement resolution and the positioning accuracy of the moving target.
[0068] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A dynamic inverse synthetic aperture radio positioning method with multi-epoch correlation, characterized in that, The specific process is as follows: Step 1, obtain N Relative observables between the epoch anchor point and the moving target ; Step 2: Use time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target to obtain the trajectory model of the moving target; Step 3, the observations from Step 1 The time-varying position and velocity of the moving target are represented by the moving target trajectory model. The positioning objective function of each trajectory model is established and solved to obtain the fitting coefficients in the moving target trajectory model. Based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to realize inverse synthetic aperture positioning. The modeling of the time-varying position and velocity of the moving target using time-invariant trajectory parameters is as follows: in, , These represent the initial epoch position and the coefficients of the velocity fitting polynomial, respectively. It is an orthogonal polynomial basis. The order of the fitting coefficients; In step three, the observations from step one are... The time-varying position and velocity of the moving target in the image are represented using the moving target trajectory model, specifically: in, , , as well as Let i represent the relative distance, velocity, azimuth, and elevation angle observations between anchor point i at epoch t and the moving target, respectively. , , They represent x, y, z axis components Indicates the first The three-dimensional position of a stationary anchor point This represents the arctangent operation. , , , These represent the measurement errors for distance, speed, azimuth, and elevation, respectively.
2. The multi-epoch correlation dynamic inverse synthetic aperture radio positioning method according to claim 1, characterized in that, The objective function is: in, Represents the coefficients of the fitting polynomial for position and velocity and the relative observations. h The mapping function, This is the covariance matrix of the noise from the distance, velocity, azimuth, and elevation angle observations.
3. The multi-epoch correlation dynamic inverse synthetic aperture radio positioning method according to claim 2, characterized in that, The objective function is solved using the Gauss-Newton iterative method to obtain the time invariants. .
4. A multi-epoch-correlated dynamic inverse synthetic aperture radio positioning device, characterized in that, include: The observation acquisition module is used to acquire... N Relative observables between the epoch anchor point and the moving target ; The target trajectory fitting module uses time-invariant trajectory parameters to fit and model the time-varying position and velocity of the moving target, thereby obtaining the trajectory model of the moving target. Synthetic aperture positioning module, used to measure the observed values The time-varying position and velocity of the moving target are represented by the moving target trajectory model. The positioning objective function of each trajectory model is established and solved to obtain the fitting coefficients in the moving target trajectory model. Based on the fitting coefficients, the time-varying position and velocity of the moving target are deduced to realize inverse synthetic aperture positioning. The modeling of the time-varying position and velocity of the moving target using time-invariant trajectory parameters is as follows: in, , These represent the initial epoch position and the coefficients of the velocity fitting polynomial, respectively. It is an orthogonal polynomial basis. The order of the fitting coefficients; The observation The time-varying position and velocity of the moving target in the image are represented using the moving target trajectory model, specifically: in, , , as well as Let i represent the relative distance, velocity, azimuth, and elevation angle observations between anchor point i at epoch t and the moving target, respectively. , , They represent x, y, z axis components Indicates the first The three-dimensional position of a stationary anchor point This represents the arctangent operation. , , , These represent the measurement errors for distance, speed, azimuth, and elevation, respectively.
5. The multi-epoch-correlated dynamic inverse synthetic aperture radio positioning device according to claim 4, characterized in that, The objective function is: in, Represents the coefficients of the fitting polynomial for position and velocity and the relative observations. h The mapping function, This is the covariance matrix of the noise from the distance, velocity, azimuth, and elevation angle observations.
6. The multi-epoch-correlated dynamic inverse synthetic aperture radio positioning device according to claim 5, characterized in that, The objective function is solved using the Gauss-Newton iterative method to obtain the time invariants. .