A feedback error dimension discrimination and multi-scale registration method, system, device and medium for port targets

Abstract

The application discloses a feedback error dimension discrimination and multi-scale registration method, system, equipment and medium for a port target, models different dimension errors in a radar measurement process by measuring target position information of the radar, discriminates error dimensions at the current moment according to true value and measurement value information of a cooperative target, and performs error registration of different scales on an observed target according to error dimension feedback information of the cooperative target. Three error models of different dimensions are modeled, then the current error model is accurately matched according to AIS information and radar measurement information of the cooperative target, the error matching result is fed back to a filtering estimation process of the observed target, error registration of different scales is performed according to the error model at the current moment, so that the error registration efficiency of the system and the target tracking precision are improved.

Description

Technical Field

[0001] This invention relates to the field of target tracking technology, and in particular to a feedback-based error dimension discrimination and multi-scale registration method, system, device and medium for port targets. Background Technology

[0002] During radar detection, system errors, spatiotemporal registration errors, and interference errors can cause significant deviations in the accuracy of radar target tracking and positioning. Existing online error registration methods achieve error calibration by expanding the dimension of the target's motion state and using the error as a state variable for filtering and estimation during tracking. However, this method uses a fixed error model and often employs constant errors, which is not ideal for time-varying multi-scale error calibration. Summary of the Invention

[0003] To solve the above problems, the present invention proposes the following technical solution:

[0004] In a first aspect, the present invention provides a feedback-based error dimension discrimination and multi-scale registration method for port targets, comprising:

[0005] By measuring the target's location information using radar, we model the errors in different dimensions during the radar measurement process.

[0006] The error dimension at the current moment is determined based on the actual and measured values ​​of the cooperation objective.

[0007] Based on the feedback information of the error dimension of the cooperative target, error registration is performed on the observed target at different scales.

[0008] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the target location information is represented as:

[0009] ;

[0010] in, Let k represent the target distance. Let be the target angle at time k, and T be the matrix transpose.

[0011] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the modeling of different dimensional errors during radar measurement includes: additive bias model, rigid body transformation model, and scale transformation model.

[0012] The additive bias model is expressed as:

[0013] ;

[0014] The rigid body transformation model is represented as follows:

[0015] ;

[0016] ;

[0017] The scaling transformation model is expressed as follows:

[0018] ;

[0019] in, This indicates the distance error offset. This indicates the angular error offset. Represents the rotation error matrix. Expressed as rotation error angle, express The sine function, express The cosine function, s represents translation error, and s represents scale error. This represents the true distance to the i-th cooperative target at time k. This represents the radar's measured distance to the i-th cooperative target at time k. This represents the true perspective of the i-th cooperative objective at time k. Let represent the angle measured by the radar for the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the rigid body transformation model. This represents the radar's measured position information of the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the scaling transformation model.

[0020] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the step of discriminating the error dimension at the current moment based on the true value and measured value information of the cooperative target includes:

[0021] Obtain the AIS data of the cooperative target at every moment as the true value of the target trajectory;

[0022] The radar's filtered estimate of the cooperative target is obtained as the measurement value. The residuals are calculated separately, and an error fitting function is constructed. The fitting result is compared with the error threshold, and the error model is detected and matched online.

[0023] The error matching results are fed back to the radar for filtering and estimation of non-cooperative observation targets.

[0024] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the step of performing error registration of the observed target at different scales includes:

[0025] Based on the feedback information, the error model at the current moment is obtained, and different scales of error registration are performed for the three error models.

[0026] The additive bias model performs registration by estimating the error of the cooperative objective;

[0027] The rigid transformation model derives error variables through singular value decomposition for error registration.

[0028] The scaling transformation model constructs the error variables as target state variables for registration.

[0029] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the registration through error estimation of cooperative targets includes:

[0030] Calculate the residuals based on the current position values ​​provided by AIS and the measured values ​​from radar data for each cooperative target:

[0031] ;

[0032] ;

[0033] in, Represents the distance residual. represents the angle residual, and wrap represents the angle normalization function;

[0034] Based on the confidence levels of different cooperation objectives, an error objective function is constructed, expressed as:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] Where i represents the i-th cooperative objective, and atan represents the arctangent function. Weighted centroids for the measured values.

[0041] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the step of deriving error variables through singular value decomposition for error registration is expressed as follows:

[0042] ;

[0043] Where ||| represents the L2 norm;

[0044] The weighted centroid of the measured and true values ​​of the objective function is calculated as follows:

[0045] ;

[0046] ;

[0047] in, The weighted centroids represent the measured values ​​of the objective function. The weighted centroids represent the true values ​​of the objective function;

[0048] Centralized measured values ​​and true values ​​are represented as follows:

[0049] ;

[0050] in, Represents a centered measurement value. Represents the centralized real value;

[0051] The covariance matrix is ​​calculated and expressed as:

[0052] ;

[0053] Singular value decomposition (SVD) decomposes the covariance matrix into three singular matrices, represented as:

[0054] ;

[0055] in, express Orthogonal basis in the spatial direction of the cooperation point, express Orthogonal basis along the principal axis direction, express An orthogonal basis in the spatial direction of the target point;

[0056] Solve and , is represented as:

[0057] ;

[0058] ;

[0059] As a preferred embodiment of the feedback-based error dimension discrimination and multi-scale registration method for port targets described in this invention, the step of constructing error variables as target state variables and performing registration is expressed as follows:

[0060] ;

[0061] in, Represents the target error state variable;

[0062] The process model is represented as follows:

[0063] ;

[0064] in, Let k be the system state vector at time k. and These are the state transition matrix and the noise distribution matrix, respectively. This is the process noise vector. Let k+1 be the system state vector.

[0065] The observation model is represented as follows:

[0066] ;

[0067] in, Let k be the system state measurement vector at time k. For the measurement matrix, To observe the true value of the target, This is the measurement noise vector.

[0068] Secondly, the present invention provides a feedback-based error dimension discrimination and multi-scale registration system for port targets, comprising:

[0069] The radar measurement error modeling module models the errors in different dimensions during the radar measurement process using the target position information measured by the radar.

[0070] The error dimension discrimination module determines the error dimension at the current moment based on the true value and measured value information of the cooperative target.

[0071] The error registration module performs error registration on the observed targets at different scales based on the feedback information of the error dimension of the cooperative targets.

[0072] Thirdly, the present invention provides a computer device, comprising:

[0073] Memory and processor;

[0074] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a feedback error dimension discrimination and multi-scale registration method for port targets.

[0075] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the aforementioned feedback error dimension discrimination and multi-scale registration method for port targets.

[0076] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0077] By establishing error models in different dimensions during radar measurement, the error characteristics and variation patterns of radar in different dimensions such as range, azimuth, and elevation can be precisely characterized. Compared with single overall error modeling, this method can more accurately reflect the anisotropy and coupling relationship of errors, providing a more accurate theoretical basis and mathematical foundation for subsequent error analysis and compensation, thereby improving the completeness of error characterization.

[0078] By comparing the known high-precision true values ​​of the cooperative target with the actual radar measurements, the main error dimensions and error states at the current moment can be identified in real-time or near real-time. This dynamic discrimination mechanism can effectively identify which dimension causes the greatest deviation in the radar system under specific environmental or operating conditions, achieving precise location of error sources and rapid extraction of fault characteristics, thus enhancing the system's adaptability and perception capabilities to changes in the measurement environment.

[0079] Based on the error dimension feedback information of cooperative targets, this method performs error registration at different scales on the observed targets, enabling differentiated correction strategies to be adopted according to the actual error distribution. This method avoids the "overcorrection" or "undercorrection" problems that may be caused by traditional uniform registration strategies, and can focus on correcting the dimensions with significant errors, thereby significantly improving the measurement accuracy and data fusion quality of non-cooperative observed targets, and ensuring the accuracy and robustness of target tracking in complex scenarios. Attached Figure Description

[0080] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0081] Figure 1 A flowchart of a feedback-based error dimension discrimination and multi-scale registration method for port targets;

[0082] Figure 2 This is a schematic diagram comparing tracking errors in a non-maneuvering target scenario, which is a feedback-based error dimension discrimination and multi-scale registration method for port targets.

[0083] Figure 3 This is a schematic diagram illustrating error model matching in a non-maneuverable target scenario, which is a feedback-based error dimension discrimination and multi-scale registration method for port targets.

[0084] Figure 4 This is a schematic diagram comparing the tracking errors of a feedback-based error dimension discrimination and multi-scale registration method for port targets in a maneuvering target scenario.

[0085] Figure 5 This diagram illustrates the error model matching in a maneuvering target scenario using a feedback-based error dimension discrimination and multi-scale registration method oriented towards port targets. Detailed Implementation

[0086] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0087] Example 1, as Figure 1 As shown, this is an embodiment of the present invention, providing a feedback-based error dimension discrimination and multi-scale registration method for port targets, including:

[0088] This invention addresses the problem that system errors, spatiotemporal registration errors, and interference errors can cause significant deviations in the accuracy of radar target tracking and positioning during radar detection. It provides a feedback-based error dimension discrimination and multi-scale registration method for port targets, effectively utilizing auxiliary information from cooperative targets, accurately matching error models, and performing multi-scale error registration. This invention assumes the presence of several cooperative targets within the detection area. During the tracking and filtering process, it first acquires auxiliary information about the port cooperative targets, models three different error dimensions, and then matches the current error model with the AIS information and radar measurement information of the cooperative targets. The error matching result is fed back to the filtering estimation process of the target to be observed. Based on the error model at the current moment, error registration at different scales is performed, thereby improving the efficiency of system error registration and the accuracy of target tracking.

[0089] In the tracking and filtering process, auxiliary information of the port cooperation target is acquired. First, three error models of different dimensions are modeled. Based on the AIS information and radar measurement information of the cooperation target, the current error model is estimated and matched. The error matching result is fed back to the filtering estimation process of the target to be observed. Based on the error model at the current moment, error registration at different scales is performed, thereby improving the system error registration efficiency and target tracking accuracy. Specifically:

[0090] S1: Model the errors in different dimensions during the radar measurement process by measuring the target position information using radar.

[0091] Three different dimensional errors in the radar measurement process are modeled. Specifically, it is assumed that the radar is on a two-dimensional plane (sea surface), the coordinate system is the radar's own coordinate system, and the target's position information at time k is used... It means that, among them, This represents the target's position information at time k. Indicates the target distance at any given time. Let be the target angle at time k, and T be the matrix transpose; let be the true position of the i-th cooperative target at time k (in the radar coordinate system) given by AIS. The radar measurement information of the i-th cooperative target at time k is: Three error models are constructed based on the error type and free quantities:

[0092] A. Additive Bias Model: Radar range and angle measurements have a fixed bias, independent of the target's position. The model parameters include two free quantities: range bias and azimuth bias. The error model is expressed as:

[0093] ;

[0094] B. Rigid Body Transformation Model: Due to rotation and translation errors in the radar system, there are three free quantities. The relationship between the measured and true values ​​is as follows: The error model is expressed as:

[0095] ;

[0096] ;

[0097] C. Scale Transformation Model: Based on B, due to radar interference from the target, scale error occurs, resulting in four free quantities. The measurement error exhibits a non-linear change with the target distance. The relationship between the measured value and the true value is as follows:

[0098] ;

[0099] in, This indicates the distance error offset. This indicates the angular error offset. Represents the rotation error matrix. Expressed as rotation error angle, express The sine function, express The cosine function, s represents translation error, and s represents scale error. This represents the true distance to the i-th cooperative target at time k. This represents the radar's measured distance to the i-th cooperative target at time k. This represents the true perspective of the i-th cooperative objective at time k. Let represent the angle measured by the radar for the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the rigid body transformation model. This represents the radar's measured position information of the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the scaling transformation model.

[0100] S2: Determine the error dimension at the current moment based on the true value and measured value information of the cooperation target.

[0101] It should be noted that during actual radar tracking of unknown targets, the three constructed error models are fitted using the AIS information of the cooperative target and the radar's tracking filtering information for the cooperative target, thereby selecting the optimal error matching model for the current moment. This process includes the following steps:

[0102] The system obtains AIS data of N cooperative targets at each moment as the true value of the target trajectory, and by default ensures that time alignment and outliers are removed.

[0103] The filtered estimate of the cooperative target by the radar is obtained as the measurement value. The residuals are calculated separately, and functions are fitted to three error models. The fitting results are compared with the error threshold, and online error model detection and matching are performed. Specifically:

[0104] Calculate the residual vector:

[0105] ;

[0106] Define rotation error function and scaling error function :

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] in, The average distance between the centroids, Let be the weighted centroid of the measured value, and let ‖‖ be the L2 norm.

[0113] Define rotation error threshold and scale error threshold , The time represents the rotational error, and the error matching model is B; and The time represents the scaling error, and the error matching model is C; and At that time, the error matching model is A. The error matching result is fed back to the radar for filtering and estimation of non-cooperative observation targets.

[0114] S3: Perform error registration on the observed target at different scales based on the feedback information of the cooperative target error dimension.

[0115] It should be noted that the error model at the current moment needs to be obtained based on the feedback information, corresponding to the error registration methods at different scales (a, b, c) for the three error models (A, B, C). Specifically:

[0116] For additive bias model A: Type A registration is performed by estimating the error of the cooperative objective, specifically including the following steps:

[0117] Calculate the residuals based on the actual position values ​​provided by AIS and the measured values ​​from radar data for each cooperative target at the current time k:

[0118] ;

[0119] ;

[0120] in, For distance residuals, This represents the angular residual.

[0121] Based on the confidence levels of different cooperation objectives, the error objective function is constructed as follows:

[0122] ;

[0123] in, Let be the angle normalization function. Solve , :

[0124] ;

[0125] ;

[0126] For rigid body transformation model B: Type B error registration is performed by deriving error variables through singular value decomposition, specifically including the following steps:

[0127] The objective function is constructed as follows:

[0128] ;

[0129] Calculate the weighted centroid of the measured and true values ​​of the objective function:

[0130] ;

[0131] ;

[0132] Centralized measured values ​​and true values:

[0133] ;

[0134] Calculate the covariance matrix:

[0135] ;

[0136] Singular value decomposition (SVD) decomposes the covariance matrix into three singular matrices.

[0137] ;

[0138] in, express Orthogonal basis in the direction of "cooperation point space", express Orthogonal basis along the principal axis direction, express Orthogonal basis in the direction of the "target point space".

[0139] Solving the rotation error matrix Translation error vector

[0140] ;

[0141] ;

[0142] For the scaling transformation model C: The error variables are constructed as target state variables, and type C registration is performed:

[0143] ;

[0144] in, The target error state variable is represented, and the error variable is estimated online through filtering, as shown below:

[0145] The process model is as follows:

[0146] ;

[0147] in, The system state vector at time k (containing ), and These are the state transition matrix and the noise distribution matrix, respectively. Let be the process noise vector, which follows a zero-mean Gaussian distribution.

[0148] Observation model:

[0149] ;

[0150] in, Let k be the system state measurement vector at time k. For the measurement matrix, To observe the true value of the target, The measurement noise vector follows a zero-mean Gaussian distribution.

[0151] It should be noted that the classic algorithm—the unscented Kalman filter—is selected for filtering estimation of discrete-time dynamic systems, thereby achieving online error estimation and compensation.

[0152] Example 2, as Figure 2 and Figure 3 As shown, this is an embodiment of the present invention, providing a simulation experiment of a feedback-based error dimension discrimination and multi-scale registration method for port targets.

[0153] This application presents a simulation experiment comparing the tracking performance of a traditional algorithm (an unscented Kalman filter algorithm without error registration) with that of a non-maneuvering target in a scenario where the observed target is a non-maneuvering target.

[0154] The sampling time interval during the simulation is 1 second, and the simulation time is 100 seconds. The initial target position is (1000, 2000) m, and it moves at a constant speed of (10, 10) m / s for 100 seconds. The number of cooperative targets N=3, with initial positions of (300, 500) m, (1500, 800) m, and (2000, 1200) m, respectively, and constant speeds of (6, 8) m / s, (10, 10) m / s, and (12, 13) m / s. The system's true error model switches between three error models: A, B, and C. Specifically, error model A is used from 0 to 5 seconds; error model B is used from 5 to 30 seconds and 80 to 100 seconds; and error model C is used from 30 to 80 seconds. This application matches the error models, and the matching results are as follows: Figure 2 This application and traditional algorithms (unscented Kalman filtering algorithm without error registration) use the OSPA distance evaluation metric for evaluation. The tracking and comparison results are as follows: Figure 3 As shown in Table 1.

[0155] Table 1

[0156]

[0157] Example 3, as Figures 4-5 As shown, this is an embodiment of the present invention, providing a simulation experiment of a feedback-based error dimension discrimination and multi-scale registration method for port targets.

[0158] This application presents a simulation experiment comparing the tracking performance of a traditional algorithm (an unscented Kalman filter algorithm without error registration) with that of a maneuvering target in a scenario where the observed target is a maneuvering target.

[0159] During the simulation, the sampling time interval is 1s, the simulation time is 100s, the initial position of the target is (400, 500)m, the target moves at a constant speed of (6, 8)m / s from 0 to 20s, turns clockwise at an angular velocity of -3 (°) / s from 20 to 70s, and turns counterclockwise at an angular velocity of 3 (°) / s from 70 to 100s. The number of cooperative targets N=3, with initial positions of (300, 500)m, (1500, 800)m, and (2000, 1200)m respectively, and moves at a constant speed of (6, 8)m / s, (10, 10)m / s, and (12, 13)m / s respectively. The system's true error model switches between three error models A, B, and C, specifically: error model A is used from 0 to 5s; error model B is used from 5 to 30s and 80 to 100s; and error model C is used from 30 to 80s. This application matches the error model, and the matching result is as follows: Figure 4 This application and traditional algorithms (unscented Kalman filtering algorithm without error registration) use the OSPA distance evaluation metric for evaluation. The tracking and comparison results are as follows: Figure 5 As shown in Table 2.

[0160] Table 2

[0161]

[0162] The error model matching results of this application under the two implementation scenarios are shown in Table 3:

[0163] Table 3

[0164]

[0165] In summary, the feedback-based error dimension discrimination and multi-scale registration method for port targets proposed in this application achieves a 48.66% improvement in tracking performance and a 94.00% error model matching accuracy compared to traditional algorithms in non-maneuvering target experimental simulation scenarios; and a 58.82% improvement in tracking performance and a 90.00% error model matching accuracy compared to traditional algorithms in maneuvering target experimental simulation scenarios. This invention addresses the problems of low efficiency and poor performance of conventional error registration methods caused by measurement deviations in different dimensions due to system errors, calibration errors, and target interference during target observation by maritime observation radar. By performing real-time error model matching and error registration at different scales, it improves the efficiency of system error registration and the accuracy of target tracking.

[0166] Example 4, illustrative of Example 1, is a feedback-based error dimension discrimination and multi-scale registration method for port targets. It should be noted that the technical solution of the system for feedback-based error dimension discrimination and multi-scale registration method for port targets in Example 4 belongs to the same concept as the technical solution of the feedback-based error dimension discrimination and multi-scale registration method for port targets in Example 1. Details not described in detail in this example can be found in the description of the technical solution of the feedback-based error dimension discrimination and multi-scale registration method for port targets in Example 1.

[0167] This embodiment also provides a feedback-based error dimension discrimination and multi-scale registration system for port targets, including:

[0168] The radar measurement error modeling module models the errors in different dimensions during the radar measurement process using the target position information measured by the radar.

[0169] The error dimension discrimination module determines the error dimension at the current moment based on the true value and measured value information of the cooperative target.

[0170] The error registration module performs error registration on the observed targets at different scales based on the feedback information of the error dimension of the cooperative targets.

[0171] This embodiment also provides a computer device applicable to a feedback-based error dimension discrimination and multi-scale registration method for port targets, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the feedback-based error dimension discrimination and multi-scale registration method for port targets as proposed in the above embodiment.

[0172] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a feedback error dimension discrimination and multi-scale registration method for port targets as proposed in the above embodiment.

[0173] The computer-readable storage medium proposed in this embodiment and the method for implementing a feedback error dimension discrimination and multi-scale registration for port targets proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0174] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A feedback-based error dimension discrimination and multi-scale registration method for port targets, characterized in that, include: By measuring the target's location information using radar, we model the errors in different dimensions during the radar measurement process. The error dimension at the current moment is determined based on the actual and measured values ​​of the cooperation objective. Based on the feedback information of the cooperative target error dimension, error registration is performed on the observed target at different scales; The modeling of errors in different dimensions during radar measurement includes: additive bias model, rigid body transformation model, and scale transformation model. The additive bias model is expressed as: ； The rigid body transformation model is represented as follows: ； ； The scaling transformation model is expressed as follows: ； in, This indicates the distance error offset. This indicates the angular error offset. Represents the rotation error matrix. Expressed as rotation error angle, express The sine function, express The cosine function, s represents translation error, and s represents scale error. This represents the true distance to the i-th cooperative target at time k. This represents the radar's measured distance to the i-th cooperative target at time k. This represents the true perspective of the i-th cooperative objective at time k. Let represent the angle measured by the radar for the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the rigid body transformation model. This represents the radar's measured position information of the i-th cooperative target at time k. This represents the true position of the i-th cooperative target at time k under the scaling transformation model; The step of determining the error dimension at the current moment based on the true value and measured value information of the cooperation target includes: Obtain the AIS data of the cooperative target at every moment as the true value of the target trajectory; The radar's filtered estimate of the cooperative target is obtained as the measurement value. The residuals are calculated separately, and an error fitting function is constructed. The fitting result is compared with the error threshold, and the error model is detected and matched online. The error matching results are fed back to the radar for filtering and estimation of non-cooperative observation targets; The error registration of the observed target at different scales includes: Based on the feedback information, the error model at the current moment is obtained, and different scales of error registration are performed for the three error models. The additive bias model performs registration by estimating the error of the cooperative objective; The rigid transformation model derives error variables through singular value decomposition for error registration; The scaling transformation model constructs the error variables as target state variables for registration.

2. The feedback-based error dimension discrimination and multi-scale registration method for port targets as described in claim 1, characterized in that: The target location information is represented as follows: ； in, This represents the target's position information at time k. Indicates the target distance at any given time. Let be the target angle at time k, and T be the matrix transpose.

3. The feedback-based error dimension discrimination and multi-scale registration method for port targets as described in claim 1, characterized in that: The registration process using error estimation of the cooperative objective includes: Calculate the residuals based on the current position values ​​provided by AIS and the measured values ​​from radar data for each cooperative target: ； ； in, Represents the distance residual. represents the angle residual, and wrap represents the angle normalization function; Based on the confidence levels of different cooperation objectives, an error objective function is constructed, expressed as: ； ； ； ； Where i represents the i-th cooperative objective, and atan represents the arctangent function. Weighted centroids for the measured values.

4. The feedback-based error dimension discrimination and multi-scale registration method for port targets as described in claim 3, characterized in that: The method of deriving error variables through singular value decomposition for error registration is expressed as follows: ； Where ||| represents the L2 norm; The weighted centroid of the measured and true values ​​of the objective function is calculated as follows: ； ； in, The weighted centroids represent the measured values ​​of the objective function. The weighted centroids represent the true values ​​of the objective function; Centralized measured values ​​and true values ​​are represented as follows: ； in, Represents a centered measurement value. Represents the centralized true value; The covariance matrix is ​​calculated and expressed as: ； Singular value decomposition (SVD) decomposes the covariance matrix into three singular matrices, represented as: ； in, express Orthogonal basis in the spatial direction of the cooperation point, express Orthogonal basis along the principal axis direction, express An orthogonal basis in the spatial direction of the target point; Solve and , is represented as: 。 5. The feedback-based error dimension discrimination and multi-scale registration method for port targets as described in claim 3, characterized in that: The process of constructing the error variable into a target state variable and performing registration is expressed as follows: ； in, Represents the target error state variable; The process model is represented as follows: ； in, Let k be the system state vector at time k. and These are the state transition matrix and the noise distribution matrix, respectively. This is the process noise vector. Let k+1 be the system state vector. The observation model is represented as follows: ； in, Let k be the system state measurement vector at time k. For the measurement matrix, To observe the true value of the target, This is the measurement noise vector.

6. A feedback-based error dimension discrimination and multi-scale registration system for port targets, employing the method described in any one of claims 1-5, characterized in that, include: The radar measurement error modeling module models the errors in different dimensions during the radar measurement process using the target position information measured by the radar. The error dimension discrimination module determines the error dimension at the current moment based on the true value and measured value information of the cooperative target. The error registration module performs error registration on the observed targets at different scales based on the feedback information of the error dimension of the cooperative targets.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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