Bullet firing real-time analysis system based on laser tracking and image analysis

Abstract

The invention relates to the technical field of live firing real-time analysis, discloses a live firing real-time analysis system based on laser tracking and image analysis, and aims to solve the problems of insufficient tracking precision, poor real-time performance and poor environmental adaptability due to the fact that a traditional firing system often depends on a single sensor. The fuzzy problem in high-speed movement of the target is successfully solved, accurate fusion of data and clear tracking of the target are ensured, a real-time feedback module of the system provides timely and accurate analysis results for shooting training, the efficiency and precision of shooting training are remarkably improved, and the series of technical innovations have good application prospects. The system can provide more accurate analysis and feedback under a more complex environment and a higher-speed target, and the response ability and the shooting hit rate of trainees are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of live firing real-time analysis technology, in particular to a live firing real-time analysis system based on laser tracking and image analysis. BACKGROUND

[0002] In order to improve the effect and safety of shooting training, modern shooting training systems begin to combine laser tracking technology and image analysis technology, so as to accurately capture the target motion trajectory, gun stability and hitting effect in the shooting process. Such systems can provide accurate shooting analysis for the trainer by real-time acquisition of target three-dimensional coordinates, speed, shooting angle and other information, and can feedback the shortcomings in training to help the shooter quickly improve skills and improve training effect.

[0003] Although modern shooting training systems have made great progress in some basic functions, the existing shooting real-time analysis system based on laser tracking and image analysis still has certain limitations. Most current systems rely on a single data source, which leads to misjudgment or tracking distortion of target position when facing fast-moving targets. For example, laser tracking devices may lose accurate tracking of the target due to changes in light or weather conditions in complex lighting environments. In addition, image analysis technology is often affected by motion blur at high speeds, and cannot provide clear target images. Moreover, the existing systems have difficulties in fusing different sensor data, and the integration and synchronization accuracy of data is low, resulting in errors in the analysis of target position and motion trajectory, and failing to achieve high-precision training feedback effect.

[0004] Therefore, we propose a live firing real-time analysis system based on laser tracking and image analysis to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a live firing real-time analysis system based on laser tracking and image analysis to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solution: a live firing real-time analysis system based on laser tracking and image analysis, comprising a laser tracking module, a multi-view acquisition module, an image enhancement module, a deblurring algorithm module, a data fusion module and a real-time feedback module. The laser tracking module is used to continuously track the three-dimensional position and speed of the target. The multi-view acquisition module is used to simultaneously acquire target images at different angles through a plurality of camera arrays. The image enhancement module is used to improve image clarity by optimizing image brightness, contrast and color restoration ability. The deblurring algorithm module is used to eliminate image jitter in real time through image stabilization technology, and reduce blur caused by rapid target movement. The data fusion module is used to eliminate image jitter in real time through image stabilization technology, and reduce blur caused by rapid target movement. The real-time feedback module is used to feed back the target motion analysis results in real time.

[0007] Preferably, the laser tracking module includes a laser emission unit, a reflected signal receiving unit, and a data processing unit. The laser emission unit is responsible for emitting laser beams to the target, and adjusts the emission frequency and power of the laser to adapt to the characteristics of different targets and environmental conditions, so as to ensure the effective transmission and reception of laser signals. The reflected signal receiving unit is responsible for receiving the laser signals reflected from the target and converting them into digital signals. Through high-precision sensors, this unit can accurately calculate the position of the target relative to the laser emission source, thereby obtaining the position information of the target. The data processing unit receives data from the reflected signal receiving unit, analyzes and processes the data in real time, and calculates the three-dimensional position and speed of the target in real time through data processing algorithms.

[0008] Preferably, the multi-view acquisition module includes a camera configuration unit, an image acquisition unit, and an image synchronization unit. The camera configuration unit is responsible for setting and adjusting the position, angle, and field of view of multiple cameras to ensure that the motion trajectory of the target can be captured comprehensively from different angles. The image acquisition unit is responsible for acquiring image data from each camera in real time and transmitting the acquired image data. The image synchronization unit synchronizes and integrates the image data from multiple cameras through time alignment and image fusion technology, generating complete and continuous three-dimensional target images.

[0009] Preferably, the image enhancement module includes an image preprocessing unit, a motion blur removal unit, and a high dynamic range enhancement unit. The image preprocessing unit is used to handle the blur phenomenon caused by high-speed target motion or camera jitter, and uses advanced deblurring algorithms to restore image clarity, ensuring that target details can be accurately presented. The motion blur removal unit is used to improve the brightness and contrast range of the image, enhance the detail performance of the image under extreme lighting conditions, make the light and dark transition of the image smoother, and ensure the visibility of the target under different lighting environments. The high dynamic range enhancement unit is used to improve the brightness and contrast range of the image, enhance the detail performance of the image under extreme lighting conditions, make the light and dark transition of the image smoother, and ensure the visibility of the target under different lighting environments.

[0010] Preferably, the deblurring algorithm module includes a motion estimation unit, a deconvolution algorithm unit, and an image detail enhancement unit. The motion estimation unit is used to estimate the cause of image blurring by analyzing the pixel changes of the image, and calculate the motion trajectory. The deconvolution algorithm unit corrects the blurred part of the image by inversely calculating the blurring effect in the motion process using the motion information provided by the motion estimation unit, thereby restoring a clearer image. The image detail enhancement unit is used to enhance the details of the deblurred image, improve the clarity and texture of the image.

[0011] Preferably, the data fusion module includes a data synchronization unit, a data fusion algorithm unit, and a data calibration unit. The data synchronization unit is responsible for ensuring the consistency of data from different modules in time. The data fusion algorithm unit uses Kalman filtering, multi-sensor fusion, and other algorithms to comprehensively process data from laser tracking, image analysis, and other sensors based on the synchronized data, to generate the comprehensive motion trajectory and state information of the target. The data calibration unit is responsible for error correction and optimization of the fused data, and detects and corrects the system error, deviation, and noise of the sensor, and adjusts the fusion result.

[0012] Preferably, the data fusion algorithm unit performs data fusion through the following steps: S1, pre-process the data from different sensors, including denoising, filling missing values, and data normalization, to ensure that the data from each sensor can be accurately aligned in time, providing a consistent time reference for subsequent fusion; S2, combine the laser tracking, image analysis, and other sensor data with the motion model of the target, and use Kalman filtering algorithm to update the motion information of the target in real time, and correct the state prediction error through Kalman filtering, thereby ensuring that the trajectory of the target can be tracked with high precision; S3, based on the data from different sensors, use weighted average or probability-based fusion algorithm to generate accurate target positioning and motion analysis; S4, based on the results of Kalman filtering and multi-sensor fusion, the data fusion algorithm predicts the future motion trajectory of the target in real time and continuously updates the current motion state of the target, and by continuously updating the target state, the system generates the historical trajectory and future prediction path of the target. S5, the data fusion algorithm automatically identifies and eliminates these abnormal data and performs automatic elimination.

[0013] Compared with the prior art, the beneficial effects of the present application are: 1. Through the integrated application of laser tracking, multi-angle acquisition, image enhancement, deblurring algorithm and data fusion modules, the system successfully solves the blurring problem in high-speed target motion, ensuring accurate data fusion and clear tracking of the target. The real-time feedback module of the system provides timely and accurate analysis results for shooting training, significantly improving the efficiency and accuracy of shooting training. This series of technical innovations enables the system to provide more accurate analysis and feedback in more complex environments and at higher target speeds, greatly improving the reaction ability and shooting accuracy of the trainees.

[0014] 2. Through the optimization of the data fusion algorithm unit, especially the combination of data preprocessing, Kalman filtering and multi-sensor fusion algorithm, the system can accurately track the target's motion trajectory, predict the target's future position, and automatically correct data errors, ensuring high precision and stability of target positioning during training. These improvements greatly improve the real-time performance, reliability and accuracy of the system, especially in dynamic shooting training, providing more accurate shooting feedback to the trainees to help them improve their shooting skills and reaction ability. At the same time, automatic abnormal data identification and elimination further improve the stability of the system, enabling it to consistently provide high-quality analysis results in complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The system flowchart of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] Embodiment one: please refer to Figure 1 A real-time analysis system for live shooting based on laser tracking and image analysis, comprising a laser tracking module, a multi-angle acquisition module, an image enhancement module, a deblurring algorithm module, a data fusion module and a real-time feedback module. The laser tracking module is used to continuously track the three-dimensional position and speed of the target. The multi-angle acquisition module is used to simultaneously acquire target images at different angles through a plurality of camera arrays. The image enhancement module is used to improve the image clarity by optimizing the brightness, contrast, and color restoration ability of the image. The deblurring algorithm module is used to eliminate image jitter in real time and reduce blurring caused by rapid target movement through image stabilization technology. The data fusion module is used to eliminate image jitter in real time and reduce blurring caused by rapid target movement through image stabilization technology. The real-time feedback module is used to provide real-time feedback on target movement analysis results.

[0018] In this embodiment, the laser tracking module can monitor the movement state of the target in real time by continuously tracking the three-dimensional position and speed of the target. This module uses laser technology to obtain accurate position and speed data of the target through laser beam emission and reflection measurement, ensuring accurate tracking of the target. Through high-frequency tracking of the target position and movement, the laser tracking module significantly improves the response speed and accuracy of the system to target movement, especially in complex or high-speed movement scenarios, providing high-precision target positioning and providing strong data support for subsequent shooting analysis and decision-making.

[0019] The multi-view acquisition module simultaneously acquires target images from different angles through multiple camera arrays. This module effectively compensates for the limitations of single-view cameras and can comprehensively and continuously obtain the movement of the target in three-dimensional space. Through the cooperation of multiple cameras, the system can accurately capture the target from any angle, reducing the problem of image occlusion or loss caused by a single view. This module greatly improves the tracking stability and real-time performance of the target, providing more complete and accurate visual information for subsequent data processing and analysis.

[0020] The image enhancement module effectively improves the clarity of the image by optimizing the brightness, contrast, and color restoration ability of the image. For different lighting environments and target movement states, the image enhancement module uses advanced image processing algorithms to automatically adjust the visual performance of the image, ensuring that the target remains clear and visible in various environments. By enhancing the detail performance of the image, this module improves the recognition ability and accuracy of the system for the target, especially in low-light, high-light, or complex background environments, ensuring that image quality is not affected and ensuring the reliability of target data.

[0021] The deblurring algorithm module uses image stabilization techniques to eliminate blur caused by rapid target movement in real time. This module uses advanced deblurring algorithms and motion estimation techniques to analyze the blur caused by movement or camera shake in the image, restoring the clarity of the image. Through real-time processing and adjustment, the deblurring algorithm ensures that even in high-speed or large-amplitude motion, the details of the target can still be accurately presented, greatly improving the accuracy and real-time performance of target analysis. This module provides more stable image input for the system, reduces errors in image processing, and improves the system's ability to capture high-speed dynamic targets.

[0022] The data fusion module eliminates image jitter and reduces blur caused by rapid movement by fusing data from multiple sensors in real time. This module processes laser tracking data, image data, and other sensor data, using advanced data fusion algorithms to accurately calculate the target's motion trajectory and correct errors between different data sources. Through precise data integration, the data fusion module ensures that information from each data source can be complementary, providing stable and high-precision target position and motion data. This module effectively improves the system's tracking and analysis capabilities for targets in complex environments, ensuring data consistency and accuracy.

[0023] The real-time feedback module is responsible for real-time feedback of target motion analysis results and presents the analysis data to the operator or user through a suitable interface. This module provides a fast and intuitive feedback interface, allowing operators to obtain key information about target motion, such as position, speed, and trajectory, in a short time. The real-time feedback module helps trainees quickly identify shortcomings in the shooting process, optimize shooting strategies, and adjust shooting angles or force in a timely manner, greatly improving the effectiveness and efficiency of shooting training. This module provides immediate decision support for users, improving their reaction speed and hit rate during shooting training.

[0024] Traditional shooting systems often rely on a single sensor, which has problems such as insufficient tracking accuracy, poor real-time performance, and poor environmental adaptability. However, this system successfully solves the blur problem in high-speed target motion through the integration of laser tracking, multi-view acquisition, image enhancement, deblurring algorithms, and data fusion modules, ensuring accurate data fusion and clear tracking of targets. The real-time feedback module of the system provides timely and accurate analysis results for shooting training, significantly improving the efficiency and accuracy of shooting training. This series of technological innovations enables the system to provide more accurate analysis and feedback in more complex environments and at higher speeds, greatly improving the reaction ability and shooting hit rate of trainees.

[0025] Example Two: Please refer to Figure 1The real-time analysis system for live shooting based on laser tracking and image analysis includes a laser tracking module, an image analysis module, and a data processing module. The laser emission unit is responsible for emitting laser beams towards the target. By adjusting the emission frequency and power of the laser, it can adapt to different target characteristics and environmental conditions, ensuring effective propagation and reception of the laser signal. The reflected signal receiving unit is responsible for receiving the laser signals reflected from the target and converting them into digital signals. Through high-precision sensors, it can accurately calculate the position of the target relative to the laser emission source, thus obtaining the target's position information. The data processing unit receives data from the reflected signal receiving unit, performs real-time analysis and processing of the data, and calculates the three-dimensional position and speed of the target in real time through data processing algorithms.

[0026] In this embodiment, the laser emission unit can dynamically adjust the emission frequency and power of the laser according to the characteristics of different targets and environmental conditions, ensuring effective propagation and accurate reception of the laser signal. This improvement enables the system to maintain high-precision tracking of the target in complex environments, especially at long distances or in unstable weather conditions.

[0027] The reflected signal receiving unit receives the laser signals reflected from the target through high-precision sensors and converts them into digital signals. This technology can accurately calculate the relative position between the target and the laser emission source, thus obtaining accurate position information of the target. This improvement ensures that the system can stably and accurately obtain the three-dimensional position of the target in the case of rapid movement or change, improving the stability and real-time performance of the tracking.

[0028] The data processing unit can analyze and process the data obtained from the reflected signal receiving unit in real time through advanced data processing algorithms, quickly calculating the three-dimensional position and speed of the target. This improvement greatly improves the response speed of the system to target motion, enabling the system to provide rapid feedback in shooting training, helping trainees to adjust shooting strategies in real time and improve shooting accuracy.

[0029] Through the combination of the laser emission unit, the reflected signal receiving unit, and the data processing unit, the laser tracking module can provide accurate, stable, and real-time target position and motion data. Compared with traditional shooting training systems, this module can still maintain high-precision and high-stability target tracking ability in complex environmental conditions, ensuring that the system can provide accurate shooting analysis data under variable conditions. These improvements effectively improve the accuracy and real-time performance in civilian shooting training, helping trainees to improve their shooting response speed and hit rate.

[0030] Example Three: Please refer to Figure 1, the multi-view acquisition module includes a camera configuration unit, an image acquisition unit, and an image synchronization unit; The camera configuration unit is responsible for setting and adjusting the positions, angles, and fields of view of multiple cameras to ensure that the motion trajectory of the target can be captured comprehensively from different angles. The image acquisition unit is responsible for acquiring image data from each camera in real time and transmitting the acquired image data. The image synchronization unit synchronizes and integrates the image data from multiple cameras through time alignment and image fusion technology to generate a complete and continuous three-dimensional target image.

[0031] In this embodiment, through the camera configuration unit, the positions, angles, and fields of view of multiple cameras can be flexibly adjusted to ensure that the motion trajectory of the target can be captured comprehensively from different angles. This improvement can make up for the limitations of a single-view camera in a complex environment, avoiding information loss due to target occlusion, changes in viewing angle, or changes in target motion direction. The multi-view capture capability enables the system to more comprehensively monitor the state of the target, providing more abundant data support for target analysis.

[0032] The image acquisition unit is responsible for acquiring image data from each camera in real time and transmitting the acquired image data.

[0033] The image synchronization unit synchronizes and integrates the image data from multiple cameras through time alignment and image fusion technology to generate a complete and continuous three-dimensional target image. This improvement enables seamless fusion of data provided by multiple cameras, avoiding target position errors caused by time misalignment or asynchronous data, ensuring that the system can accurately and stably reconstruct the motion trajectory of the target in front of a fast-moving target.

[0034] Through the integration of the multi-view acquisition module, the camera configuration unit, the image acquisition unit, and the image synchronization unit work together to effectively improve the system's comprehensive capture and precise synchronization capabilities for targets. Compared with traditional shooting training systems, this module can acquire and synchronize image data from multiple angles in a dynamic environment, ensuring accurate tracking and analysis of target motion by the system. This improvement improves image quality and feedback speed in shooting training, helping trainees better understand target motion patterns and make faster and more accurate shooting responses.

[0035] Embodiment Four: Please refer to Figure 1, the image enhancement module includes an image preprocessing unit, a motion blur removal unit, and a high dynamic range enhancement unit; The image preprocessing unit is used to handle the blur phenomenon caused by high-speed movement of the target or camera shake, restore the image clarity using advanced deblurring algorithms, and ensure that the target details are accurately presented. The motion blur removal unit is used to improve the brightness and contrast range of the image, enhance the details of the image under extreme lighting conditions, make the light-dark transition of the image smoother, and ensure the visibility of the target under different lighting environments. The high dynamic range enhancement unit is used to improve the brightness and contrast range of the image, enhance the details of the image under extreme lighting conditions, make the light-dark transition of the image smoother, and ensure the visibility of the target under different lighting environments.

[0036] In this embodiment: through the image preprocessing unit, the module can effectively handle the blur phenomenon caused by high-speed movement of the target or camera shake. Using advanced deblurring algorithms, the unit can restore the details in the image, ensuring that the target does not lose important information during movement. This improvement greatly improves the system's image processing capability for dynamic targets, especially in fast-moving shooting training, which can clearly present every detail of the target, improving the trainer's ability to identify and judge the target.

[0037] The motion blur removal unit improves the performance of the image under extreme lighting conditions by improving the brightness and contrast range of the image. This unit can balance the light-dark contrast of the image, enhance the details in strong light or shadow environment, and ensure that the target is clearly visible in any environmental condition. This improvement is crucial for shooting training under various lighting conditions, especially in scenes with large changes in lighting such as daytime and nighttime, ensuring that the target can always be clearly presented, reducing visual interference caused by lighting problems.

[0038] The high dynamic range enhancement unit further enhances the brightness and contrast range of the image, improving the details of the image under extreme lighting conditions, making the light-dark transition of the image smoother. This improvement helps the system maintain visual balance of the image in cases of large lighting differences, avoiding overexposure or excessive shadow, so that the target can be clearly captured and analyzed in different environments, providing more realistic and accurate target images.

[0039] The image enhancement module greatly improves the image quality and stability of the system in dynamic environments through image preprocessing, motion blur removal, and high dynamic range enhancement. Compared with traditional systems, this module effectively eliminates the blur problem in target motion, enhances the detail performance of images under various lighting conditions, and significantly improves the visibility and recognizability of the target in complex environments. This improvement enables the shooting training system to work efficiently in more real-world environments, improves training accuracy and effectiveness, and provides clearer and more reliable training feedback for shooters.

[0040] Embodiment Five: Please refer to Figure 1 The deblurring algorithm module includes a motion estimation unit, a deconvolution algorithm unit, and an image detail enhancement unit. The motion estimation unit estimates the cause of image blurring by analyzing pixel changes in the image and calculates the motion trajectory. The deconvolution algorithm unit corrects the blurred part of the image using the motion information provided by the motion estimation unit by inversely propagating the blurring effect during motion, thereby restoring a clearer image. The image detail enhancement unit enhances the details of the deblurred image to improve the clarity and texture of the image.

[0041] In this embodiment, the motion estimation unit can accurately estimate the cause of image blurring and calculate the motion trajectory of the target by analyzing the changes in image pixels. The introduction of this module enables the system to identify the specific cause of blurring during target motion, not only providing key information for subsequent deblurring processing, but also accurately capturing the impact of high-speed motion or camera shaking on image clarity. This improvement is crucial in shooting training, enabling the system to customize image processing for different motion states, thereby more accurately restoring image details.

[0042] The deconvolution algorithm unit corrects the blurred part of the image by inversely propagating the blurring effect during motion, combined with the motion information provided by the motion estimation unit. This unit uses advanced deconvolution algorithms to repair blurred parts caused by target motion or camera shaking, restoring a clearer image. Compared with traditional image processing methods, deconvolution technology can more accurately restore image details, especially in high-speed motion scenarios, ensuring that every detail of the target is clearly presented, greatly improving image quality.

[0043] The image detail enhancement unit enhances the details of the deblurred image, further improving the clarity and texture representation of the image. By strengthening the edges, textures, and colors of the image, the enhancement unit ensures that the target appears clearer and sharper in the deblurred image. Especially in complex backgrounds or low-contrast situations, image detail enhancement can effectively improve the recognizability of the target, providing more accurate visual feedback for the trainer.

[0044] The deblurring algorithm module significantly improves the clarity and accuracy of the image in high-speed motion scenarios through the combination of motion estimation, deconvolution, and detail enhancement techniques. Compared with traditional systems, the deblurring algorithm module can accurately repair the specific conditions of target motion, thereby eliminating motion blur and improving the quality and visibility of the image. Especially in shooting training, this module can effectively restore the details of the target, helping the trainer to more clearly identify the target, thereby improving the training effect, reducing visual errors, and improving the shooting hit rate.

[0045] Embodiment six: please refer to Figure 1 The data fusion module includes a data synchronization unit, a data fusion algorithm unit, and a data calibration unit. The data synchronization unit is responsible for ensuring the consistency of data from different modules in time. The data fusion algorithm unit uses Kalman filtering, multi-sensor fusion, and other algorithms to process data from laser tracking, image analysis, and other sensors based on synchronized data, generating comprehensive motion trajectories and state information of the target. The data calibration unit is responsible for error correction and optimization of the fused data. By detecting and correcting the system error, bias, and noise of the sensor, the data calibration unit adjusts the fusion results.

[0046] In this embodiment: the introduction of the data synchronization unit ensures the consistency of data from different modules in time. This module uses high-precision time alignment algorithms to accurately match data from various sensors, avoiding positioning errors or data inconsistency caused by time deviation. In the fast-changing shooting training scenario, the motion trajectory and position of the target must be accurately synchronized, and the data synchronization unit effectively solves this problem, improving the real-time performance and accuracy of the entire system.

[0047] The data fusion algorithm unit processes data from laser tracking, image analysis, and other sensors through advanced algorithms such as Kalman filtering and multi-sensor fusion, generating comprehensive motion trajectories and state information of the target. This module can extract useful information from multi-dimensional data sources, optimize target state prediction through algorithms, and ensure accurate and stable target positioning and motion trajectory capture and calculation in dynamic shooting training. The introduction of data fusion algorithms effectively improves target positioning accuracy in shooting training, providing more reliable motion data and feedback for trainers.

[0048] The data calibration unit is responsible for error correction and optimization of the fused data. By detecting and correcting sensor system errors, biases, and noise, this unit ensures the accuracy and reliability of the final fusion results. In a multi-sensor system, raw data may be affected by errors, biases, or environmental interference from different sensors. The data calibration unit corrects these errors to ensure more accurate final motion trajectories and positions. This improvement is particularly important in complex shooting environments, eliminating data differences between multiple sensors and improving system stability and accuracy.

[0049] Example six: please refer to Figure 1 The data fusion algorithm unit performs data fusion through the following steps: S1, pre-process data from different sensors, including noise removal, missing value filling, and data normalization, to ensure accurate temporal alignment of sensor data and provide a consistent time reference for subsequent fusion; S2, combine laser tracking, image analysis, and other sensor data with the target's motion model, and use Kalman filtering algorithm to update the target's motion information in real time, and correct state prediction errors through Kalman filtering to ensure high-precision tracking of the target's trajectory; S3, based on data from different sensors, use weighted average or probability-based fusion algorithms to generate accurate target positioning and motion analysis; S4, based on the results of Kalman filtering and multi-sensor fusion, the data fusion algorithm predicts the future motion trajectory of the target in real time and continuously updates the current motion state of the target, and by continuously updating the target state, the system generates the historical trajectory and future prediction path of the target; S5, the data fusion algorithm automatically identifies and removes these abnormal data and performs automatic removal.

[0050] In this embodiment: The preprocessing process in S1 step, including denoising, filling missing values and data normalization, ensures that data from different sensors can be accurately aligned in time, providing a consistent time reference for subsequent fusion. This step eliminates errors caused by time synchronization or data inconsistency, providing stable and high-quality input data for the system, making subsequent analysis more accurate, especially in rapidly changing target scenarios, ensuring the timeliness and accuracy of each data point.

[0051] The Kalman filter algorithm used in S2 step combines sensor data such as laser tracking and image analysis with the target's motion model, enabling real-time updates to the target's motion information and correcting prediction errors. This technology can correct errors in time when the target is moving at high speed or suddenly changing direction, ensuring high-precision tracking of the target's trajectory. This improvement enables the system to provide more accurate target position and trajectory in dynamic shooting training, helping trainees better understand the target's motion rules and improve shooting strategies.

[0052] In S3 step, the weighted average or probability-based fusion algorithm dynamically adjusts the data weight of different sensors, combining the advantages of each data source to generate accurate target positioning and motion analysis. According to the reliability and accuracy of the sensor, the data information provided by each sensor can be fused in the most suitable way, further improving the accuracy of target positioning and motion analysis. Especially in a multi-sensor environment, this method can effectively optimize the contribution of each data source, avoiding the impact of missing or inaccurate single sensor data on the overall performance of the system.

[0053] Real-time target trajectory prediction in S4 step combines Kalman filtering and multi-sensor fusion results to ensure that the system can continuously update the current motion state of the target and provide real-time target positioning based on historical trajectory and predicted path. This function is particularly beneficial to dynamic shooting training systems, which can provide real-time target prediction during training to help trainees adjust shooting strategies and improve hit rates.

[0054] In S5 step, the data fusion algorithm can automatically identify and eliminate abnormal data, preventing false data from negatively affecting the final results. This function is particularly important in training, as sensors may be disturbed or malfunction in complex environments, resulting in abnormal data. Automatic detection and elimination of abnormal data ensures the reliability and accuracy of system output, reduces the need for human intervention, and improves the stability and automation level of the system.

[0055] Through the optimization of the data fusion algorithm unit, especially the combination of data preprocessing, Kalman filtering and multi-sensor fusion algorithm, the system can accurately track the motion trajectory of the target, predict the future position of the target, and automatically correct data errors, ensuring the high precision and stability of target positioning in training. These improvements greatly improve the real-time performance, reliability and accuracy of the system, especially in dynamic shooting training, it can provide more accurate shooting feedback for the trainees, help them improve their shooting skills and reaction ability. At the same time, automatic abnormal data identification and elimination further improve the stability of the system, so that it can continuously provide high-quality analysis results in complex environments.

[0056] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0057] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions recorded in the foregoing embodiments or make equivalent replacements for part of the technical features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A live fire real-time analysis system based on laser tracking and image analysis, characterized in that: The laser tracking module, the multi-view acquisition module, the image enhancement module, the deblurring algorithm module, the data fusion module, and the real-time feedback module are included. The laser tracking module is used for continuously tracking the three-dimensional position and speed of the target. The multi-view acquisition module is used for simultaneously acquiring target images at different angles through a plurality of camera arrays. The image enhancement module is used for improving image clarity by optimizing the brightness, contrast, and color restoration capability of the image. The deblurring algorithm module is used for eliminating image jitter in real time and reducing blurring caused by rapid target movement through image stabilization technology. The data fusion module is used for eliminating image jitter in real time and reducing blurring caused by rapid target movement through image stabilization technology. The real-time feedback module is used for real-time feedback of target motion analysis results.

2. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 1, characterized in that: The laser tracking module includes a laser emission unit, a reflected signal receiving unit, and a data processing unit. The laser emission unit is responsible for emitting a laser beam to the target, adjusting the emission frequency and power of the laser to adapt to the characteristics of different targets and environmental conditions, thereby ensuring the effective transmission and reception of the laser signal. The reflected signal receiving unit is responsible for receiving the laser signal reflected from the target and converting it into a digital signal. Through high-precision sensors, this unit can accurately calculate the position of the target relative to the laser emission source, thereby obtaining the position information of the target. The data processing unit receives data from the reflected signal receiving unit, performs real-time analysis and processing of the data, and calculates the three-dimensional position and speed of the target in real time through data processing algorithms.

3. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 2, characterized in that: The multi-view acquisition module includes a camera configuration unit, an image acquisition unit, and an image synchronization unit. The camera configuration unit is responsible for setting and adjusting the position, angle, and field of view of multiple cameras to ensure that the motion trajectory of the target can be captured comprehensively from different angles. The image acquisition unit is responsible for acquiring image data from each camera in real time and transmitting the acquired image data. The image synchronization unit synchronizes and integrates the image data from multiple cameras through time alignment and image fusion technology to generate complete and continuous three-dimensional target images.

4. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 3, characterized in that: The image enhancement module includes an image preprocessing unit, a motion blur removal unit, and a high dynamic range enhancement unit. The image preprocessing unit is used to handle the blurring phenomenon caused by high-speed target motion or camera jitter, restore image clarity using advanced deblurring algorithms, and ensure that target details are accurately presented. The motion blur removal unit is used to improve the brightness and contrast range of the image, enhance the detail performance of the image under extreme lighting conditions, and make the light-dark transition of the image smoother, ensuring the visibility of the target under different lighting environments. The high dynamic range enhancement unit is used to improve the brightness and contrast range of the image, enhance the detail performance of the image under extreme lighting conditions, and make the light-dark transition of the image smoother, ensuring the visibility of the target under different lighting environments.

5. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 4, characterized in that: The deblurring algorithm module includes a motion estimation unit, a deconvolution algorithm unit, and an image detail enhancement unit. The motion estimation unit is configured to estimate the cause of image blur by analyzing the pixel changes of the image and to calculate the motion trajectory; The deconvolution algorithm unit is configured to correct the blurred part of the image using the motion information provided by the motion estimation unit by inversely deducing the blurring effect in the motion process, so as to restore a clearer image; The image detail enhancement unit is configured to perform detail enhancement on the deblurred image to improve the clarity and texture of the image.

6. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 5, characterized in that: The data fusion module comprises a data synchronization unit, a data fusion algorithm unit and a data calibration unit; The data synchronization unit is responsible for ensuring the consistency of data from different modules in time; The data fusion algorithm unit uses Kalman filtering, multi-sensor fusion and other algorithms to comprehensively process the data from laser tracking, image analysis and other sensors according to the synchronized data, to generate the comprehensive motion trajectory and state information of the target; The data calibration unit is responsible for error correction and optimization of the fused data, and detects and corrects the system error, deviation and noise of the sensor, and adjusts the fusion result.

7. The real-time analysis system for live firing based on laser tracking and image analysis according to claim 6, characterized in that: The data fusion algorithm unit performs data fusion through the following steps: S1, pre-process the data from different sensors, including denoising, filling missing values and data normalization processing, to ensure that the data of each sensor can be accurately aligned in time, and to provide a consistent time reference for subsequent fusion; S2, combine the laser tracking, image analysis and other sensor data with the motion model of the target, and use Kalman filtering algorithm to update the motion information of the target in real time, and correct the state prediction error through Kalman filtering, so as to ensure that the trajectory of the target can be tracked with high precision; S3, according to the data of different sensors, use weighted average or probability-based fusion algorithm to generate accurate target positioning and motion analysis; S4, based on the results of Kalman filtering and multi-sensor fusion, the data fusion algorithm predicts the future motion trajectory of the target in real time and continuously updates the current motion state of the target, and by continuously updating the target state, the system generates the historical trajectory and future prediction path of the target; S5, the data fusion algorithm automatically identifies and eliminates these abnormal data and performs automatic elimination.

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