Real-time measuring and calculating method for target tracking precision of boat-mounted photoelectric searching and aiming system

Abstract

The invention provides a real-time measurement and calculation method for target tracking precision of an airship-borne photoelectric searching and aiming system, which belongs to the technical field of aviation photoelectricity and specifically comprises the following steps: acquiring image data of a tracking target in real time by an optical sensor of the airship-borne photoelectric searching and aiming system; preprocessing the image data; segmenting the preprocessed target image based on an objective function fuzzy C-means clustering algorithm FCM; a genetic algorithm is adopted to optimize image processing parameters, and a globally optimal solution of target features is solved; and matching each frame of extracted target features with a previous frame of target feature template in the database, calculating the offset of the target in the image, calculating the offset of the target in the image by using a preset error analysis model, and calculating the precision in the target tracking process in combination with image acquisition parameters. Through the processing scheme provided by the invention, the system inertial stability control performance of the system can be evaluated in real time so as to adapt to complex and changeable flight environments and high-precision target tracking requirements.

Description

Technical Field

[0001] The present application relates to the field of aviation optoelectronics, and in particular to a method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system. Background Art

[0002] The boat-borne electro-optical search and aiming system is an external electro-optical stabilized tracking system. It is a multi-sensor electro-optical system integrating laser, infrared and television. It not only needs to complete the system control, detection and startup of the search, aiming and indication system, but more importantly, it needs to ensure that the infrared and television sensors can identify and track the search target when the search, aiming and indication system is working, that is, the sensor must have a stable and clear external target video image.

[0003] In modern aviation optoelectronic systems, target tracking accuracy is one of the key factors that determine system performance and is the core indicator of the control system, which directly affects the stability of the aiming line, tracking accuracy, and illumination accuracy. Traditional target tracking accuracy measurement methods usually rely on ground equipment or offline data analysis. These methods cannot provide real-time tracking accuracy information and are difficult to meet the needs of real-time feedback in dynamic battlefield environments.

[0004] In addition, the boat-borne optoelectronic search and aiming system is a complex control system. During the process of identifying and tracking the search target, factors such as the unbalanced torque and friction inside the system and the external factors such as the carrier vibration and wind resistance torque will affect the stability and accuracy of the target video image, thereby affecting the stability and accuracy of target tracking.

[0005] Therefore, it is urgent to develop a method that can measure the target tracking accuracy of the aviation electro-optical search and aiming system in real time to adapt to the complex and changeable flight environment and the needs of high-precision target tracking. Summary of the invention

[0006] In view of this, the present application provides a real-time measurement method for the target tracking accuracy of a boat-borne electro-optical search and aiming system, which evaluates the system's inertial stability control performance in real time to adapt to complex and changeable flight environments and the needs of high-precision target tracking.

[0007] The present application provides a method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system, which adopts the following technical solution:

[0008] A method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system comprises the following steps:

[0009] The optical sensor of the onboard optoelectronic search and sighting system collects image data of the tracked target in real time;

[0010] Preprocess the image data;

[0011] The preprocessed target image is segmented based on the objective function fuzzy C-means clustering algorithm FCM;

[0012] Genetic algorithm is used to optimize the parameters of image processing and calculate the global optimal solution of target features;

[0013] The extracted target features of each frame are matched with the target feature template of the previous frame in the database, the offset of the target in the image is calculated, the offset of the target in the image is calculated using the preset error analysis model, and the accuracy of the target tracking process is calculated in combination with the image acquisition parameters.

[0014] Optionally, the method for extracting target features in the objective function fuzzy C-means clustering algorithm FCM includes:

[0015] The discrete difference Sobel operator is used to process the approximate value of the gradient of the image brightness function, thereby extracting the edge data of the image;

[0016] The local binary pattern (LBP) is used to eliminate the influence of light and shadow changes in complex environments;

[0017] Combined with color features, HSV color space is used for multi-feature fusion.

[0018] Optionally, the FCM objective function in the objective function fuzzy C-means clustering algorithm FCM is:

[0019] Initialize the input image data membership matrix and objective function value in Matlab, add regularized spatial constraints to the objective function, use similar membership for adjacent pixels, calculate the update vector center, and update the objective function and membership matrix.

[0020] Optionally, the objective function-based fuzzy C-means clustering algorithm FCM dynamically adjusts the fuzzy factor parameter value according to the complexity of the image, so that the fuzzy factor parameter value has different weights in different scene areas.

[0021] Optionally, the steps of preprocessing the image data include:

[0022] Gaussian filtering is used to remove noise from image data;

[0023] And adaptive histogram equalization is used to enhance the contrast of the image.

[0024] Optionally, during the process of the optical sensor collecting image data of the tracking target in real time, the control algorithm of the synthetic aperture imaging system of the optical sensor lens of the target of the ship-borne electro-optical search and aiming system adopts deep learning to perform image restoration.

[0025] In summary, this application includes the following beneficial technical effects:

[0026] This application can measure target tracking accuracy in real time, rather than post-processing methods such as ground equipment or offline data analysis. These methods cannot provide tracking accuracy information in real time and it is difficult to meet the demand for real-time feedback in a dynamic battlefield environment.

[0027] For target images in complex image environments, this application uses an improved objective function-based fuzzy C-means clustering algorithm FCM to better segment target images and achieve real-time precision measurement. It can better evaluate the aiming line stability performance, tracking accuracy, and illumination accuracy to adapt to complex and changeable flight environments and the needs of high-precision target tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 This is a flow chart of a method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0031] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0032] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an 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 described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0033] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show the components related to the present application rather than being drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0034] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0035] The embodiment of the present application provides a method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system.

[0036] like Figure 1 As shown, a method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system comprises the following steps:

[0037] Collecting image data: The optical sensor of the boat-borne optoelectronic search and aiming system collects and tracks the image data of the target in real time.

[0038] Preprocess the image data.

[0039] Target image segmentation: The preprocessed target image is segmented based on the objective function fuzzy C-means clustering algorithm FCM, where the full name of FCM is fuzzy c-means.

[0040] Optimize image processing parameters: Use genetic algorithms to optimize image processing parameters, increase convergence speed and improve calculation efficiency, avoid local optimal solutions, and calculate the global optimal solution of target features to avoid erroneous classification results.

[0041] Target offset accuracy measurement: Match the extracted target features of each frame with the target feature template of the previous frame in the database, calculate the offset of the target in the image, use the preset error analysis model to calculate the offset of the target in the image, and calculate the accuracy of the target tracking process in combination with the image acquisition parameters.

[0042] Specific to each step:

[0043] In the process of optical sensors collecting image data of tracking targets in real time, the control algorithm of the synthetic aperture imaging system of the optical sensor lens of the target of the ship-borne optoelectronic search and aiming system uses deep learning for image restoration, which helps to optimize the image restoration effect, improve the imaging quality, and reduce the difficulty of filtering and denoising in image data preprocessing.

[0044] The steps of preprocessing the image data include: using Gaussian filtering to remove noise from the image data to improve the image quality; and using adaptive histogram equalization to enhance the contrast of the image.

[0045] The target feature extraction method in the fuzzy C-means clustering algorithm FCM based on the objective function includes:

[0046] The discrete difference Sobel operator is used to process the approximate value of the gradient of the image brightness function, so as to extract the edge data of the image; considering the feature analysis of the local texture of the complex image, the local binary pattern LBP is used to eliminate the influence of light and shadow changes in complex environments, where the full name of LBP is Local Binary Pattern; combined with color features, the HSV color space is used for multi-feature fusion.

[0047] The FCM objective function in the fuzzy C-means clustering algorithm FCM based on the objective function is: initialize the input image data membership matrix and the objective function value in Matlab, add a regularized spatial constraint term to the objective function, use similar membership for adjacent pixels to reduce the impact on the clustering results, calculate and update the vector center, and update the objective function and membership matrix.

[0048] In the embodiment of the present application, for target images in complex image environments, an improved objective function-based fuzzy C-means clustering algorithm FCM is designed to better segment the target images.

[0049] Based on the objective function, the fuzzy C-means clustering algorithm FCM dynamically adjusts the fuzzy factor parameter value according to the complexity of the image, so that the fuzzy factor parameter value has different weights in different scene areas.

[0050] The present application also discloses a real-time calculation system for target tracking accuracy of a boat-borne optoelectronic search and aiming system, the system comprising:

[0051] Optical sensors: including television, infrared and laser, used to collect external target video images, distance measurement, etc., and used to collect image data of tracking targets in real time.

[0052] Image processing module: completes image data preprocessing, target image segmentation, optimizes image processing parameters and other functions.

[0053] Error analysis module: calculates the error in the target tracking process in real time and calculates the accuracy in the target tracking process.

[0054] Database module: store and read target feature templates and other related data.

[0055] Integrated display system: displays target position, tracking accuracy and other information.

[0056] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system, characterized in that: The steps include: The optical sensor of the onboard optoelectronic search and sighting system collects image data of the tracked target in real time; Preprocess the image data; The preprocessed target image is segmented based on the objective function fuzzy C-means clustering algorithm FCM; Genetic algorithm is used to optimize the parameters of image processing and calculate the global optimal solution of target features; The extracted target features of each frame are matched with the target feature template of the previous frame in the database, the offset of the target in the image is calculated, the offset of the target in the image is calculated using the preset error analysis model, and the accuracy of the target tracking process is calculated in combination with the image acquisition parameters.

2. The method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system according to claim 1 is characterized in that: The target feature extraction method in the fuzzy C-means clustering algorithm FCM based on the objective function includes: The discrete difference Sobel operator is used to process the approximate value of the gradient of the image brightness function, thereby extracting the edge data of the image; The local binary pattern (LBP) is used to eliminate the influence of light and shadow changes in complex environments; Combined with color features, HSV color space is used for multi-feature fusion.

3. The real-time calculation method for target tracking accuracy of a boat-borne optoelectronic search and aiming system according to claim 2 is characterized in that: Based on the objective function, the FCM objective function in the fuzzy C-means clustering algorithm FCM is: Initialize the input image data membership matrix and objective function value in Matlab, add regularized spatial constraints to the objective function, use similar membership for adjacent pixels, calculate the update vector center, and update the objective function and membership matrix.

4. The method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system according to claim 3 is characterized in that: Based on the objective function, the fuzzy C-means clustering algorithm FCM dynamically adjusts the fuzzy factor parameter value according to the complexity of the image, so that the fuzzy factor parameter value has different weights in different scene areas.

5. The method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system according to claim 1 is characterized in that: The steps of image data preprocessing include: Gaussian filtering is used to remove noise from image data; And adaptive histogram equalization is used to enhance the contrast of the image.

6. The method for real-time calculation of target tracking accuracy of a boat-borne optoelectronic search and aiming system according to claim 1 is characterized in that: While the optical sensor is collecting image data of the tracking target in real time, the control algorithm of the synthetic aperture imaging system of the optical sensor lens of the target of the ship-borne optoelectronic search and aiming system uses deep learning to restore the image.

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