High-dynamic target real-time tracking and positioning method and system and medium

Abstract

The invention relates to the technical field of shooting assistance and target tracking, and discloses a high-dynamic target real-time tracking and positioning method and system and a medium, and the method comprises the following steps: obtaining visual features of a target in sighting telescope division, combining a known target physical size, and utilizing data assistance of a sensor and an accelerometer to carry out distance measurement and calculation; based on the measured distance and environmental parameters, predicting a target trajectory through a self-adaptive interaction multi-model, and correcting a trajectory compensation value through strong tracking volume Kalman filtering; and the ballistic data card and a cosine compensation method are combined, and the sighting telescope adjustment amount is output to guide shooting. By constructing a sensing and classical wireless filtering integrated directional framework, accurate trajectory tracking and dynamic compensation of a high-speed moving target in a complex electromagnetic environment are supported. According to the scheme, the shooting hit rate and the navigation precision under complex terrains and dynamic confrontation scenes are improved, and the method has extremely high robustness and shooting environment adaptability.

Description

Technical Field

[0001] This invention relates to the field of shooting assistance and target tracking technology, and more specifically, to a method, system and medium for real-time tracking and positioning of highly dynamic targets. Background Technology

[0002] In modern tactical shooting and long-range sniping missions, marksmen often need to accurately engage high-speed or non-uniformly moving targets in complex environments. Traditional rangefinding methods mainly rely on active optical rangefinders such as laser rangefinders. For snipers, using active optical equipment generates light spots, which may alert the target and even prematurely reveal their position, significantly reducing mission success rates. Furthermore, in field environments, snipers often fire from downward or upward angles, making it difficult for ballistic calculators and laser rangefinders to measure the firing angle, thus affecting accuracy. Optical rangefinding based on mil-dot or angle-of-arc (MOA) methods is widely used in field combat scenarios without electronic support. Multifunctional slide rules, as a power-free analog calculation tool, can quickly estimate distance by the ratio of the target's physical dimensions to the mil-dot reading, and provide aiming correction references when combined with preset ballistic data cards. However, these tools are typically only applicable to static targets and fixed environmental parameters, lacking the ability to predict the motion state of highly dynamic targets, and also unable to integrate multi-source environmental data such as wind speed, temperature, air pressure, and firing angle for comprehensive compensation in real time. Furthermore, existing methods struggle to achieve accurate ballistic calculations and continuous tracking when facing situations such as firing on slopes or when targets suddenly change speed or direction.

[0003] With the development of intelligent algorithms, some high-end targeting systems have begun to introduce algorithms such as Kalman filtering and interactive multiple model (IMM) to improve the accuracy of target trajectory prediction. However, these systems mostly rely on electric drive and complex sensor integration, resulting in problems such as high cost, poor reliability, and weak anti-interference capabilities, making it difficult to operate stably in extreme environments. Especially in battlefield environments with no network, low power consumption, or severe electromagnetic interference, electronic equipment is prone to failure, while purely mechanical computing tools cannot meet the real-time correction requirements of dynamic targets.

[0004] Therefore, how to combine the robustness of mechanical computing tools with the predictive power of modern intelligent filtering algorithms to construct a comprehensive tracking and positioning solution that can operate in an environment without electricity and can cope with the influence of highly dynamic targets and complex terrain has become an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a method, system, and medium for real-time tracking and positioning of high dynamic targets, which solves the technical problems in the prior art of inaccurate ranging of traditional sniper auxiliary methods in complex environments, ballistic compensation lag, and inability to adapt to terrain changes and target mobility.

[0006] This invention provides a method, system, and medium for real-time tracking and positioning of highly dynamic targets, comprising: Firstly, a method for real-time tracking and localization of highly dynamic targets includes: Acquire multiple target observation data and firing environment data; the target observation data includes mil or arc fraction values ​​measured through the sight and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle; Based on the mil or minute angle value and the known physical size, the mil or minute angle value occupied by the target is read through the reticle of the scope; the scale is aligned with the known or estimated physical size of the target; the target size scale on the left side of the scale is aligned with the mil or minute angle scale on the right side; after alignment, the calculated distance of the target is directly read from the distance scale window of the scale; if the target size or mil value exceeds the scale range of the scale, a scaling method is used; wherein, the scaling method divides both the target size and the mil value by an integer multiple to make them fall within the scale range; the scaled value is aligned with the scale and the intermediate distance is read; the intermediate distance is multiplied by an integer multiple to obtain the calculated distance of the target; Based on the calculated distance and the shooting environment data, a ballistic drop compensation value is calculated using a ballistic compensation algorithm. The ballistic compensation algorithm predicts the target state and obtains a prediction result. The prediction result is then corrected. The target state includes the target's real-time coordinates in three-dimensional space, the target's velocity vector (magnitude and direction), and the target's acceleration changes. These are used to determine whether the target is maneuvering and to dynamically identify the target's motion type using multiple models (such as uniform speed, coordinated turning, and current statistical models). Based on the ballistic drop compensation value, a corresponding scope adjustment is generated through operation, and a correction command is output to guide the shooter in completing the next shooting task. The correction instructions include muzzle elevation or wind drift correction values ​​in mils or minutes of arc; target trajectory: a two-dimensional / three-dimensional trajectory display of historical position and predicted future position; predicted aiming point: an aiming point with lead calculated based on the future position of the target; ballistic drop compensation value: a vertical correction amount after considering gravity, environment, and angle; and wind drift correction amount: a lateral correction amount calculated based on wind speed and wind direction.

[0007] Furthermore, alignment is achieved by manipulating the distance calculation scale on the slide rule, including: acquiring multi-source observation datasets from positioning devices, sensors, radar, or inertial measurement units, and performing data preprocessing, timestamp alignment, and coordinate system operations to obtain a unified observation dataset; based on the unified observation dataset, the mil value or MOA value of the target in the reticle is read using the crosshair method, coarse reticle method, or mil-dot method; when the target is in motion, causing dynamic blurring of the reticle image, the processor calls image enhancement to deblur the observation image, and improves the accuracy of mil value reading through multi-frame image superposition noise reduction and edge sharpening algorithms, ensuring that the mil value measurement error is controlled within ±5mil (approximately ±0.127mm) in scenarios where the target's lateral or longitudinal movement speed does not exceed 10m / s-35km / h. Within the range; when the target's physical size and the measured mil value or angle fraction value exceed the scale range of the slide rule, a scaling method is used, that is, the target's physical size and mil value are simultaneously divided by the same integer multiple to make them fall within the scale range for calculation, and the intermediate distance is obtained, which is then multiplied by the integer multiple to obtain the final target measurement distance; the processor is configured to receive the intermediate data generated during the scaling process and verify the accuracy of the final target measurement distance; when the target's physical size is known, the ratio of the visual size to the physical size is obtained, and the target distance is obtained through the mil distance measurement formula or MOA distance measurement formula; when the target's physical size is unknown, a reference object at the same distance from the target is selected as the physical size benchmark, and the distance is calculated through the mil distance measurement formula or MOA distance measurement formula to complete the target distance measurement.

[0008] Furthermore, the firing angle is obtained through an angle measurement device, which includes a thin rope mechanism with a pendant. The device measures the angle between the aiming line and the horizontal plane using the principle of gravity perpendicularity. This angle data is combined with the target state predicted by AIMM to determine the firing geometry. Based on firing environment data, including wind speed, wind direction, temperature, air pressure, and key factors affecting the ballistics such as the firing angle, the firing environment dataset, target state prediction results, and firing angle data together constitute a complete set of input parameters for ballistic calculation, outputting the ballistic calculation result. Based on the ballistic calculation result, the horizontal distance is calculated using a cosine compensation method. The horizontal distance is equal to the slope distance multiplied by the cosine of the firing angle, used to correct deviations in the ballistic calculation results when firing uphill or downhill. This horizontal distance is combined with the target position information predicted by AIMM. The cosine-compensated horizontal distance is fused with the environment dataset to form a parameter set. This parameter set is then input into a strong pursuit volumetric Kalman filter algorithm for accurate calculation of the ballistic compensation value.

[0009] Furthermore, the ballistic compensation algorithm predicts the target state through adaptive interactive multi-model analysis, including: calculating the matching probability of the kinematic model in real time based on a fuzzy logic controller; the input of the fuzzy logic controller includes the target acceleration rate of change and the filter information sequence; according to the matching probability, the predicted states and covariances of multiple models are interactively mixed, and a weighted and comprehensive target state prediction result is output for prospective compensation of ballistic motion; by establishing a model set, including at least a uniform velocity CV model, a coordinated turning CT model, and a current statistical CA model, the model selection is dynamically optimized based on the shooting environment data; in each filtering cycle, the likelihood function and posterior probability of each model are calculated, and the influence weights of the shooting angle and shooting environment dataset on the target motion prediction are fused; when the target motion mode changes, the weights of each model are adjusted to achieve rapid adaptation to different modes.

[0010] Furthermore, the strong tracking capacitive Kalman filter algorithm introduces a time-varying fading factor to force the filter residual sequence to remain orthogonal in real time, dynamically adjusting the filter gain when abnormal observation data occurs or model mismatch occurs, ensuring the stable output of the ballistic drop compensation value; based on fuzzy logic-based adaptive adjustment of model probability, a fuzzy logic controller is designed, using the acceleration change rate and information sequence statistical characteristics as inputs, and outputting an adjustment factor for the motion trajectory probability, intelligently adjusting the weight coefficients in the model set; the information sequence is monitored in real time, and when model mismatch is detected, a strong tracking mechanism is activated to ensure reliable information on the target distance and state required for ballistic compensation calculation; the predictive covariance matrix is ​​dynamically adjusted through the fading factor to enhance the filter's ability to track abrupt signals, and this tracking ability affects the accuracy of ballistic compensation value query and the calculation accuracy of the scope adjustment amount.

[0011] Furthermore, it also includes the establishment and retrieval of ballistic data cards: a ballistic data card is pre-set on the slide rule, and the ballistic data card records the baseline ballistic drop at different distances; based on the ballistic drop compensation value, the baseline drop in the ballistic data card is corrected in real time based on environmental data and target dynamics; the actual ballistic performance is tested at multiple known distances through live-fire shooting, and the model weight adjustment results are combined during the test; the ballistic drop at each distance point is recorded, and a relationship table corresponding to distance and drop is established. The relationship table is combined with the target state prediction results adjusted by fuzzy logic to compensate for the accurate ballistic data; when the shooting equipment model, ammunition batch, or shooting accessories are changed, live-fire shooting verification is performed again and the ballistic data card is updated. During the update process, the parameter settings of the fuzzy logic controller are calibrated simultaneously.

[0012] Furthermore, the scope adjustment amount is converted into instructions applicable to different reticle units, including mils and minutes of arc or MOA; the adjustment amount is displayed to the user through a display unit integrated on a prefabricated slide rule, or an electronic signal is generated to drive the scope's automatic adjustment mechanism; when the target's physical size exceeds the slide rule's scale range, a scaling method is used to divide the target's physical size and corresponding mil value by the same factor to make them fall within the scale range for calculation, and then the calculation result is multiplied by the corresponding factor to obtain the actual distance; the actual distance is input into the trajectory fusion algorithm as the observation input for state estimation; when the target mil reading is less than 1 mil, a fine measurement method with 0.1 mil increments is used, utilizing 0.3-4.A 5-mil scale is used for accurate ranging of distant targets. The distance data obtained from the fine ranging result and the scaling method together constitute the input parameter set of the multi-model fusion algorithm. It supports mutual conversion between mil and MOA (Mean Interval) units. Based on the mapping relationship between the distance measurement result and the mil or MOA conversion coefficient, the reticle adjustment amount corresponding to the ballistic drop compensation value is calculated. During the calculation of the adjustment amount, the target motion velocity vector and angular velocity parameters are simultaneously introduced to achieve dynamic reticle correction. When the target is in a high-speed lateral maneuver, the mil or MOA conversion result is compensated for lead using a feedforward control algorithm. The compensation amount is positively correlated with the target maneuver acceleration. Dynamic calibration is performed using the target position deviation predicted by Kalman filtering. This calibration result serves as a benchmark reference for acquiring visual size data during subsequent continuous tracking. Target visual size data and environmental data are continuously acquired. The accuracy of the visual size data acquisition is directly affected by the calibration result of the aforementioned feedforward control algorithm, ensuring measurement accuracy under high-speed maneuver conditions. The processor cyclically executes the ballistic compensation algorithm, dynamically updating the ballistic drop compensation value based on the continuously acquired visual size data and environmental data. The update process and the compensation results of the feedforward control algorithm form a closed-loop feedback mechanism. Changes in the scale of the calculation ruler or the numerical changes in the display unit reflect real-time correction guidance based on dynamically updated ballistic compensation values, providing the shooter with an aiming reference synchronized with the target's maneuvering state. Based on the ballistic compensation values ​​obtained from the cyclically executed ballistic compensation algorithm, combined with the target state information predicted by AIMM, the offset of the aiming point relative to the target center is determined. The offset calculation process incorporates the target position uncertainty information from the strong tracking filter and forms a coordinated aiming scheme with the real-time correction guidance. Considering the influence of the shooting angle obtained through the angle measurement device on the ballistic trajectory, when the shooting angle is greater than a preset angle of 15 degrees, the ballistic compensation value is recalculated using the horizontal distance calculated based on the cosine compensation method. The recalculated ballistic compensation value is combined with the offset calculation result to obtain the ballistic accuracy calculation for shooting uphill or downhill. The wind deflection correction amount calculated by combining real-time collected environmental datasets such as wind speed and direction is integrated with the angle compensation and ballistic compensation results. Based on the comprehensive output of all the aforementioned steps, the final three-dimensional coordinates of the aiming point are calculated to ensure that the ballistic trajectory accurately matches the target position predicted by AIMM.

[0013] Furthermore, it outputs corrective instructions for shooting guidance to help the shooter complete the next shooting task. This includes: outputting the optimal state estimate after multi-model fusion and strong tracking filtering to generate a target trajectory containing position, velocity, and acceleration information; providing real-time trajectory display functionality, supporting two-dimensional and three-dimensional visualization, and displaying content including the target's historical trajectory and aiming reference information after reticle unit conversion; and predicting the target's trajectory by predicting the target's position over a future period based on the current fused state estimate to guide the shooter in completing the next shooting task.

[0014] Secondly, a high-dynamic target real-time tracking and positioning system includes: Data acquisition module: used to acquire multiple target observation data and firing environment data; the target observation data includes mil values ​​or angle fractions measured through the scope and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle; Distance Calculation Module: Based on mil or angle fraction and known physical dimensions, it aligns the distance calculation scale on a pre-made slide rule to obtain the calculated distance to the target. Aiming correction module: used to calculate the ballistic drop compensation value based on the measured distance and the shooting environment data through a ballistic compensation algorithm; the ballistic compensation algorithm uses an adaptive interactive multi-model to predict the target state and uses a strong tracking volumetric Kalman filter to correct the prediction results; Command output module: Based on the ballistic drop compensation value, it generates the corresponding scope adjustment amount by operating the ballistic compensation scale on the pre-made calculation ruler, and outputs correction commands to guide the shooter to complete the next shooting task.

[0015] Thirdly, a computer-readable storage medium is provided for storing computer-readable instructions that, when read by a computer, enable the execution of the high-dynamic target real-time tracking and positioning method.

[0016] The beneficial effects of this invention are as follows: By combining the rapid ranging capability of a mechanical slide rule with the intelligent prediction mechanism of Adaptive Interactive Multiple Model (AIMM) and Strong Tracking Cumulative Kalman Filter (STCKF), this invention achieves efficient closed-loop processing for distance calculation and ballistic compensation of high-speed moving targets in an unpowered environment. The use of a scaling mechanism expands the applicability of the slide rule, solving the problem of over-range ranging; the cosine compensation method corrects errors caused by inclined firing, improving the hit rate on slopes; and the introduction of fuzzy logic control and a time-varying fading factor enhances model switching sensitivity and filtering stability, significantly improving the system's robustness under sudden target changes or environmental disturbances. Therefore, this method balances portability, real-time performance, and high accuracy, making it particularly suitable for applications with extremely high reliability requirements. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a high dynamic target real-time tracking and positioning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a high dynamic target real-time tracking and positioning system module provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating a high-dynamic target real-time tracking and positioning distance compensation method provided in an embodiment of the present invention. Detailed Implementation

[0018] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples. Example

[0019] The system design described in this invention is flexible and designed to adapt to a variety of application scenarios, ranging from completely electricity-free to fully electronic. Therefore, we clearly define its two core operating modes: Mode 1 (Basic Mode): The core of this mode is a pre-built, non-electric mechanical slide rule. In this mode, the system does not rely on any electronic sensors or processors. The shooter observes the target through the optical sight, manually reads the mil value, and aligns it on the slide rule based on the known physical dimensions of the target, directly reading the calculated distance. Subsequently, the shooter obtains environmental data (such as wind speed and firing angle) based on experience or simple tools, and again operates the ballistic compensation scale on the slide rule to read the final sight adjustment. This mode has the highest environmental robustness and is suitable for special situations such as electromagnetic silence, extreme environments, or when the device's battery is depleted, facilitating the sniper's concealment and firing data processing.

[0020] Mode 2 (Enhanced Mode): This mode builds upon the basic mode's architecture by introducing an electronic processing unit, sensor array (such as a weather sensor, tilt sensor, and image sensor), and display unit. In this mode, the sensors automatically collect shooting environment data from the basic mode. The processing unit executes complex algorithms such as Adaptive Interactive Multimodel (AIMM) and Strong Tracking Cumulative Kalman Filter (STCKF) to intelligently correct and dynamically update the slide rule measurements from Mode 1. The final results are visually presented through the electronic display unit and can even generate signals to drive the automatic adjustment mechanism of the sight. This mode aims to achieve the highest level of accuracy and automation.

[0021] At least one embodiment of the present invention discloses a method, system, and medium for real-time tracking and positioning of high dynamic targets, comprising the following steps: Step 1: Acquire multiple target observation data and firing environment data; the target observation data includes mil values ​​or angle fractions measured through the scope and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle; Step 2: Based on the mil value or angle fraction and the known physical dimensions, align the distance calculation scale on the pre-made slide rule to obtain the measured distance of the target; Step 3: Based on the measured distance and the shooting environment data, calculate the ballistic drop compensation value using a ballistic compensation algorithm; the ballistic compensation algorithm uses an adaptive interactive multi-model to predict the target state and uses a strong tracking capacitive Kalman filter to correct the prediction results; Step 4: Based on the ballistic drop compensation value, generate the corresponding scope adjustment amount by operating the ballistic compensation scale on the pre-made calculation ruler, and output the correction command to guide the shooter to complete the next shooting task.

[0022] Acquiring multiple target observation data and firing environment data refers to the process by which the shooter observes the target using an optical sight and collects relevant information in conjunction with external sensors or experience. Target observation data includes the mil or minute of arc (MOA) value of the target measured through the reticle of the sight, as well as the known or estimable actual physical dimensions of the target (e.g., a human height of approximately 1.7 meters, a car length of approximately 4.5 meters, etc.). Firing environment data covers key external conditions affecting the ballistic trajectory, including but not limited to wind speed (unit: m / s), wind direction (angle relative to the firing direction), atmospheric temperature (°C), air pressure (hPa), and the angle between the barrel axis and the horizontal plane, i.e., the firing angle (°). This data can be obtained through a handheld weather instrument, tilt sensor, or manual input. The acquisition of this type of data provides the basic input for subsequent distance calculation and trajectory correction. Based on mil or minute angle values ​​and known physical dimensions, the calculated target distance is obtained by aligning the distance calculation scale on a pre-designed slide rule. This involves using a multi-functional slide rule—a simulation calculation device with a preset scale relationship—to proportionally match the actual size of the target with the angular unit it occupies in the scope's image, thereby deriving the slant distance. This slide rule is typically a card-shaped structure made of hard plastic or metal, with two sets of sliding alignment windows on the front: the left side aligns the target's physical dimensions (units: centimeters or meters), and the right side corresponds to the mil or MOA scale. After aligning the known target physical dimensions with the measured mil / MOA value on the scale, the estimated target distance (units: meters) can be directly read along a specific mark. For example, if the target height is 80 centimeters and occupies 1.5 mils in the scope, aligning 80 with 1.5 will yield a distance of approximately 534 meters on the main scale. This process is battery-free, has a rapid response, and is suitable for electromagnetically silent or extreme environments.

[0023] The calculation of ballistic compensation values ​​based on the measured distance involves an intelligent ballistic solution module integrating advanced control theory. The core of this algorithm lies in its combination of adaptive interactive multi-model and strong-tracking capacitive Kalman filtering. The adaptive interactive multi-model component constructs multiple motion models (such as uniform linear motion (CV), coordinated turning motion (CT), and current statistical motion (CA) models) and dynamically adjusts the weights of each model according to changes in the target's motion pattern, achieving forward-looking prediction of the future position of highly dynamic targets. Strong-tracking capacitive Kalman filtering is used to handle state estimation problems in nonlinear systems. When model mismatch or abnormal observation noise exists, a time-varying fading factor is introduced to force residual orthogonality, enhancing filter stability and thus improving the robustness and convergence speed of the ballistic compensation values. Based on the measured distance and firing environment data, the ballistic drop compensation value is obtained by comprehensively considering the vertical drop caused by gravity during bullet flight, as well as the lateral drift caused by wind deflection, air density, temperature, and other factors. The compensation value is calculated based on the measured distance, calling the baseline drop curve stored in the pre-established ballistic data card, and interpolating and correcting it in conjunction with measured environmental parameters. For example, under high temperature and low pressure conditions, air resistance decreases, the projectile decelerates more slowly, and the actual drop is less than the standard data, requiring a corresponding reduction in elevation; conversely, it is increased. The firing angle also participates in this compensation process. The slant distance is converted into an equivalent horizontal distance (horizontal distance = slant distance × cosα, where α is the firing elevation or depression angle) using the cosine compensation method, ensuring that the ballistic calculation is based on the actual effective distance. Based on the ballistic drop compensation value, the corresponding scope adjustment is generated by operating the ballistic compensation scale on the slide rule. This means inputting the ballistic drop (unit: cm) after environmental and dynamic corrections into the ballistic compensation scale area on the right side of the slide rule. This area is marked with the mil or MOA adjustment value corresponding to different drop amounts. Users align the calculated drop value (e.g., 60 cm) with the corresponding scale, and can then read the required scope adjustment (e.g., 3.3 MOA or 0.96 mil) in another window. This adjustment can be used to manually raise the aiming point or adjust the elevation angle via a knob, ensuring the bullet impact point accurately lands on the target's center. The entire process is visualized and intuitive, significantly reducing cognitive load. The output provides corrective instructions to guide the shooter in completing the next shooting task. This includes not only providing the shooter with the calculated distance information of the current target but also generating a trajectory prediction based on continuously observed and filtered target state estimates, including position, velocity, and acceleration. This helps the shooter predict the target's future position and pre-set the aiming point. This output can be directly presented through the scale or combined with an additional display unit for digital prompts, supporting 2D / 3D trajectory visualization and enhancing situational awareness.

[0024] Through the above technical solution, this application achieves the functions of rapid, stable, and high-precision target ranging, ballistic correction, and firing guidance under complex conditions such as no power supply, high interference, and high-speed target maneuvering. By employing an architecture that combines a mechanical slide rule with an intelligent filtering algorithm, it solves the problems of electronic equipment failure, slow response, and inability to adapt to unsteady targets in existing technologies, thus improving the shooter's independent combat capability and first-shot hit rate in real combat environments. In particular, by embedding adaptive interactive multi-model and strong tracking volumetric Kalman filtering into the ballistic compensation process, it enhances the adaptability to sudden target maneuvers. Combined with cosine compensation and environmental correction mechanisms, it effectively reduces errors caused by shooting on slopes and weather disturbances.

[0025] In a preferred embodiment of the invention, when the physical size or mil value of the target exceeds the scale range of the slide rule, traditional distance measurement methods based on fixed scale alignment cannot directly complete the ranging operation. For example, in long-range shooting scenarios, small targets occupy less than 0.3 mils in the scope reticle, which is below the minimum readable scale of the slide rule; while in the case of close-range or large targets, the target occupies more than 10 mils, exceeding the upper limit of the conventional ranging scale. In addition, the physical size of the target itself may exceed 300 cm, making it impossible to perform effective size and mil alignment operations. The above situations cause ranging interruption or a serious decrease in accuracy, affecting the accuracy and real-time performance of subsequent ballistic compensation calculations. This problem is particularly prominent in highly dynamic battlefield environments, because the target types are diverse and their movement states are complex, making it difficult for shooters to determine in advance whether the target is within the range of the equipment. This application proposes a preferred solution, comprising: acquiring multiple target observation data and firing environment data; the target observation data including mil-dot or arc-minute values ​​measured through a scope and the known physical dimensions of the target; the firing environment data including at least one of wind speed, wind direction, temperature, air pressure, and firing angle; based on the mil-dot or arc-minute values ​​and the known physical dimensions, aligning the distance calculation scale on a pre-made slide rule to obtain the calculated distance of the target; based on the calculated distance, calculating the ballistic compensation value by executing a ballistic compensation algorithm, wherein the ballistic compensation algorithm predicts the target state through an adaptive interactive multi-model and corrects the ballistic compensation value through a strong tracking volumetric Kalman filter; obtaining the ballistic drop compensation value according to the calculated distance and firing environment data; based on the ballistic drop compensation value, generating a corresponding scope adjustment amount by manipulating the ballistic compensation scale on the slide rule for reading, and outputting a correction command to guide the shooter to complete the next firing task.

[0026] Alignment is achieved by manipulating the distance calculation scale on the slide rule, including: acquiring multi-source observation datasets from positioning devices, sensors, radar, or inertial measurement units, and performing data preprocessing, timestamp alignment, and coordinate system unification to obtain a unified observation dataset; based on the unified observation dataset, the mil value or MOA value of the target in the reticle is read using the crosshair method, coarse reticle method, or mil point method; when the target is in motion causing dynamic blurring of the reticle image, the processor calls image enhancement to deblur the observation image, and improves the mil value reading accuracy through multi-frame image superposition noise reduction and edge sharpening algorithms, ensuring that the mil value measurement error is controlled within ±0.5mil in scenarios where the target's lateral or longitudinal movement speed does not exceed 10m / s; when the target's physical size When the measured mil value or angle fraction exceeds the scale range of the slide rule, a scaling method is used. This involves dividing both the target physical size and the mil value by the same integer multiple to bring them within the scale range for calculation. The intermediate distance is then multiplied by the integer multiple to obtain the final target distance. The processor is configured to receive the intermediate data generated during the scaling process and verify the accuracy of the final target distance. When the target physical size is known, the ratio of the visual size to the physical size is obtained, and the target distance is obtained using the mil distance measurement formula or the MOA distance measurement formula. When the target physical size is unknown, a reference object at the same distance from the target is selected as the physical size benchmark, and the distance is calculated using the mil distance measurement formula or the MOA distance measurement formula to complete the target distance calculation.

[0027] Through the above technical solution, this application achieves dynamic normalization of the original ranging parameters, enabling ranging calculations to be completed even when the target's physical size or mil reading exceeds the original range of the slide ruler, by mathematical transformation mapping it to the effective scale range. Specifically, multi-source observation data, after preprocessing, time synchronization, and coordinate alignment, forms a consistent input dataset, ensuring the comparability of information from different sensor sources on a spatiotemporal reference, providing a data foundation for subsequent accurate reading of mil or angle fractions. The visual size data of the target presented in the scope reticle is obtained through the crosshair method, coarse reticle method, or mil method, depending on the shooter's operating habits and the clarity of the target features. When the detected target physical size X (unit: cm) is greater than 300 or the measured mil value Y is greater than 10 (for a standard 1-10 mil scale), or Y is less than 0.3 (applicable to a 0.3-4.5 mil subdivision scale area), it is determined that it exceeds the applicable scale range, and the scaling process is initiated.

[0028] The core of the scaling method lies in compressing the original parameters to an operable range while keeping the size-to-mil ratio constant. Specifically, a positive integer N is chosen as the scaling factor, such that the intermediate size X1 = X / N falls within the 10-300 cm range, and the intermediate mil value or intermediate angle fraction Y1 = Y / N falls within the 0.3-10 mil range. For example, if the actual target size is 500 cm and the measured mil value is 8, then N=4 is chosen, resulting in X1=125 cm and Y1=2 mil, both within the coverage range of the conventional scale. Subsequently, 125 and 2 are aligned on the slide rule, and the corresponding distance L1 is read as approximately 156 meters. This intermediate distance is then multiplied by the scaling factor N to obtain the true target distance L=156×4=624 meters. This process utilizes the linearity of the ranging formula D=(S×1000) / M (D is the distance in meters; S is the target size in centimeters; M is the mil value). Since the S / M ratio remains constant before and after scaling, the calculation results are unaffected.

[0029] This scaling mechanism applies not only to mil-based systems but also to anisodometric systems. In anisodometric measurements, the distance calculation formula is D=(S×95.5) / MOA, whose mathematical structure also satisfies proportional invariance; therefore, the same scaling strategy can be used to extend the measurement range. During implementation, the scaling factor N can be set according to automatic discrimination logic, such as prioritizing commonly used multiples like N=2, 4, and 5 until X1 and Y1 simultaneously fall within the valid range; alternatively, it can be manually selected by the shooter, combining experience to quickly complete the normalization operation.

[0030] Furthermore, the distance calculation scale on the slide rule includes multiple sub-regions: the 1-10 mil zone is used for routine rangefinding, the 0.3-4.5 mil zone supports long-range, small target measurement, and the 1-15 MOA zone adapts to the reticle systems of different types of sights. All zones share the same sliding scale for synchronized readings, ensuring consistency and ease of operation. During data preprocessing, multi-source observation data from optical sensors, laser rangefinders, or other observation systems are sampled at a unified frequency and clock deviations are calibrated to avoid misreading due to asynchronous acquisition. The coordinate system ensures that all spatial information is expressed based on the same geographic or relative reference frame, preventing calculation errors caused by coordinate confusion.

[0031] Specifically, determining whether the target physical size or mil value exceeds the scale range of the slide rule involves the following steps: When the target physical size or mil value exceeds the scale range of the slide rule, a scaling method is used to convert the out-of-range value into an equivalent value within the readable range of the slide rule; when the target physical size is smaller than the smallest scale division of the slide rule, the target physical size is multiplied by a preset magnification factor, and the corresponding mil value is also multiplied by the same factor, maintaining the proportional relationship; when the target physical size is larger than the largest scale division of the slide rule, the target physical size is divided by a preset reduction factor, and the corresponding mil value is also... Divide by the same factor to maintain the proportional relationship; when the mil value is less than the smallest readable scale of the slide rule, estimate the corresponding distance value through interpolation algorithm, or calculate the distance directly using mathematical formula; when the mil value is greater than the largest scale of the slide rule, decompose the mil value into multiple components within the readable range, calculate them separately, and then synthesize the final distance result; align the converted target physical size with the mil value on the slide rule and read the corresponding distance value; perform reverse conversion on the read distance value to restore the actual target distance; verify the rationality of the measured distance, and if the distance value is abnormal, remeasure and recalculate.

[0032] Through the above technical solution, this application achieves effective ranging capability when the target's physical size or mil value exceeds the range of the calculation ruler scale. Because a scaling mechanism is introduced, error accumulation caused by ranging failures or forced estimations is avoided; furthermore, because the scaling process maintains the proportional relationship between size and angle measurements, the mathematical correctness and physical consistency of the ranging results are guaranteed. Therefore, it can operate stably in a wider range of combat scenarios, improving the responsiveness and adaptability to highly dynamic and uncertain targets, and enhancing the robustness and practicality of the overall tracking and positioning method.

[0033] In a preferred embodiment of the invention, during real-time tracking and positioning of highly dynamic targets, especially when performing long-range precision shooting missions in complex terrain conditions, traditional ballistic calculation methods based on slant range are insufficient to accurately reflect the actual trajectory of the bullet. Since gravity primarily affects the ballistic trajectory horizontally, using slant range as the fundamental distance parameter for trajectory calculation when shooting angles are uphill or downhill will lead to overestimation or underestimation of bullet drop, resulting in significant hit deviations. Furthermore, in dynamic environments, environmental data such as wind speed, temperature, and air pressure, along with the shooting angle, collectively influence ballistic characteristics; relying solely on a static ballistic model cannot achieve high-precision compensation. Particularly in uneven terrain such as mountains and hills, the lack of an effective correction mechanism for tilt angles severely reduces the system's adaptability and reliability. Therefore, a technical solution is urgently needed that can scientifically correct the slant range by incorporating shooting angle information and integrate multi-source environmental factors for precise ballistic calculations to improve the accuracy of trajectory prediction in tilted shooting scenarios.

[0034] This application proposes the following: Obtain the firing angle; based on firing environment data, construct an input parameter set for ballistic calculation using the firing environment dataset, target state prediction results, and firing angle data; output the ballistic calculation results; based on the ballistic calculation results, correct the horizontal distance of the ballistic calculation results using a cosine compensation method; wherein the horizontal distance is equal to the slope distance multiplied by the cosine of the firing angle, used to correct deviations in the ballistic calculation results when firing uphill or downhill, and the horizontal distance is combined with the target position information predicted by AIMM; fuse the cosine-compensated horizontal distance with the environment dataset to form a parameter set; input the parameter set into the XFPatch Cumulative Kalman Filter algorithm for ballistic solution.

[0035] Through the above technical solution, this application achieves high-precision dynamic correction of ballistic parameters under inclined firing conditions, effectively improving the accuracy and stability of ballistic prediction in complex terrain environments.

[0036] This embodiment provides a ballistic compensation method for inclined firing scenarios. The method first collects firing angle data and integrates it with firing environment data such as wind speed, wind direction, temperature, and air pressure, as well as results from a target state prediction module, to form a comprehensive input parameter set for ballistic calculation. This ensures the ballistic model has sufficient input dimensions to reflect the real physical environment. Based on this, a cosine compensation method is used to geometrically correct the originally calculated slant range, calculating the actual horizontal projection distance affecting ballistic drop. Subsequently, this corrected horizontal distance is reintegrated into the environmental dataset to form a new input parameter set, which is then fed into a strong-tracking capillary Kalman filter algorithm for nonlinear ballistic solution, further suppressing estimation errors caused by model mismatch or observation noise.

[0037] Obtaining the firing angle refers to acquiring the tilt information of the barrel axis relative to the horizontal plane using an external measuring device or integrated sensor. This angle can be obtained in various ways, such as using a mechanical angle measuring device with a combination of a pendant and a scale (such as the plumb line angle measuring method described in the technical disclosure), or using electronic devices such as digital tilt sensors, inertial measurement units, or electronic compasses to acquire data in real time. This angle data is usually expressed in degrees, covering the entire quadrant from -90° (vertically downward) to +90° (vertically upward), but in practical applications it is mainly used for common slope firing scenarios within ±45°. The accuracy of the firing angle measurement directly affects the accuracy of subsequent distance correction, therefore the measuring device is required to have good stability and anti-interference capabilities.

[0038] Firefighting environment data refers to external environmental parameters collected by the system, including but not limited to wind speed, wind direction, atmospheric temperature, air pressure, and humidity, which affect ballistic performance. This data can be provided by portable weather instruments, built-in environmental sensors, or remote weather stations. The role of environmental data is to correct for the effects of changes in air density, drag coefficient, and sound speed on projectile trajectory, especially in long-range firing where even small differences in temperature and pressure can lead to impact point deviations of tens of centimeters. Environmental data, along with the firing angle, participates in ballistic modeling, ensuring that the input parameter set includes not only spatial geometric information but also atmospheric physical state information, enhancing the model's realism and adaptability.

[0039] Combining the shooting environment dataset, target state prediction results, and shooting angle data into the input parameter set for ballistic calculation means that all key variables are uniformly organized into a multi-dimensional vector, serving as the driving input to the ballistic calculation model. The target state prediction results originate from the preceding adaptive interactive multi-model algorithm, containing information such as the target's position, velocity, acceleration, and probability distribution of its motion patterns. The construction of this input parameter set achieves multi-source information fusion, enabling ballistic calculation to move beyond isolated reliance on range parameters and instead be built upon a joint framework that comprehensively considers target dynamic behavior, environmental disturbances, and terrain attitude, significantly improving the system's overall perception capability and predictive foresight.

[0040] The output ballistic calculation results refer to the preliminary estimation of key ballistic parameters such as ballistic drop, flight time, and drift angle under current conditions, based on the aforementioned input parameter set and by calling a pre-calibrated ballistic mathematical model or using a lookup table method. This stage of calculation can be completed based on classical external ballistic equations or simplified empirical formulas, and its output serves as the basis for subsequent corrections. Since calculations may still be based on uncorrected slant ranges at this point, further processing is required to eliminate systematic biases caused by angles.

[0041] like Figure 3 As shown, correcting the horizontal distance of the calculated trajectory using the cosine compensation method is one of the key steps in this embodiment. The cosine compensation method, based on trigonometric relationships, combines the measured slant distance with the firing angle to calculate the horizontal distance that truly determines the vertical drop of the ballistic trajectory. For example, when the measured target slant distance is 625 meters and the firing angle is 30°, the horizontal distance is approximately 625 × cos30° meters. This correction process fundamentally changes the traditional calculation logic that only uses the slant distance as a benchmark, making the trajectory drop compensation value closer to actual needs, especially suitable for close-range slope firing or large-angle long-range firing scenarios below zero distance.

[0042] The cosine-compensated horizontal distance is fused with the environmental dataset to form a parameter set. This means that the updated horizontal distance replaces the original slant range and is recombined with other environmental parameters to form a new input vector. This fusion process can be achieved through data concatenation, weighted fusion, or feature mapping, with the aim of ensuring that all subsequent calculations are based on distance parameters that best approximate the actual ballistic path. This parameter reconstruction mechanism enhances the feedback loop capability of the entire system and avoids cascading errors caused by initial distance misjudgment.

[0043] Inputting the parameter set into the strong-tracking ductile Kalman filter (STRK) algorithm for ballistic calculation refers to using STRK to estimate the state and suppress errors in a nonlinear ballistic system. STRK is an improved nonlinear filtering method that combines the high-order accuracy of ductile Kalman filtering with the robustness of the strong-tracking mechanism. By introducing a time-varying fading factor, it forces the filter residual sequence to maintain orthogonality, and can dynamically adjust the filter gain in the event of observational anomalies or model mismatches, preventing filter divergence. In this embodiment, the STRK receives the fused parameter set as the input for both observations and predictions, continuously iteratively updates the ballistic state estimate, and outputs stable ballistic drop compensation values ​​and aiming correction suggestions, thereby achieving rapid response and precise control in complex dynamic environments.

[0044] Through the above technical solution, this application achieves high-precision dynamic compensation for ballistic drop under uphill or downhill shooting conditions. By employing a cosine compensation mechanism based on the shooting angle, it effectively eliminates ballistic calculation deviations caused by ignoring terrain inclination. Simultaneously, by fusing the corrected horizontal distance with multi-dimensional environmental parameters and inputting it into the ultimate tracking volumetric Kalman filter algorithm for nonlinear calculation, the robustness and stability of the system under model mismatch or sudden environmental changes are significantly improved. For example, in a typical scenario with a slant distance of 625 meters and a shooting angle of 30°, the system corrects the effective range to approximately 541 meters through cosine compensation, and accordingly retrieves the corresponding ballistic drop compensation value (e.g., 55 cm instead of 60 cm), further smoothing the output through STCKF filtering, ultimately generating more accurate scope adjustment commands. This method is not only applicable to conventional scenarios such as hunting and competitive shooting, but can also be extended to mission environments with extremely high precision requirements, such as military reconnaissance and counter-terrorism sniping, significantly improving the combat effectiveness of high-dynamic target tracking and positioning systems in complex terrain.

[0045] In a preferred embodiment of the invention, highly dynamic targets often exhibit violent and unpredictable motion behaviors in complex battlefield or field environments, such as sudden acceleration, sharp turns, or variable-speed maneuvers. Traditional ballistic prediction methods typically rely on fixed motion models (such as uniform linear models), which are ill-suited to the rapid mode switching of such targets, leading to lag or inaccuracy in predicted trajectories, thus affecting the accuracy and real-time performance of ballistic compensation values. Especially in long-range shooting scenarios, even small prediction errors can be significantly amplified, causing aiming deviations or even misses. Furthermore, there is an urgent need for an intelligent prediction mechanism that can adaptively adjust model weights based on real-time changes in the target's motion state and incorporate the influence of external environmental factors to improve the tracking accuracy and response speed for highly dynamic targets.

[0046] This application proposes the following: A fuzzy logic controller is used to calculate the matching probability of the kinematic model in real time. The inputs to the fuzzy logic controller include the target acceleration rate of change and the filtered information sequence. Based on the matching probability, the predicted states and covariances of multiple models are interactively mixed, and a weighted and integrated target state prediction result is output for forward-looking trajectory compensation. A model set is established, including at least a uniform velocity CV model, a coordinated turning CT model, and a current statistical CA model. Model selection is dynamically optimized based on firing environment data. In each filtering cycle, the likelihood function and posterior probability of each model are calculated, while the influence weights of the firing angle and firing environment dataset on target motion prediction are fused. When the target motion pattern changes, the influence weights of each model are adjusted to achieve rapid adaptation to different patterns.

[0047] The fuzzy logic controller is a rule-based nonlinear control system that automatically assesses the system state and outputs corresponding decision weights based on the changing trends of input variables. In this embodiment, the two core inputs of the fuzzy logic controller are the target acceleration change rate and the filtered information sequence. The target acceleration change rate refers to the increment of the target acceleration per unit time, reflecting whether the target is undergoing a drastic speed adjustment, such as sudden start-up or braking; this parameter can be obtained by continuously observing the target position information and performing two numerical differentiations, or it can be directly provided by sensors. The filtered information sequence refers to the residual between the actual observed value and the filter predicted value, used to measure the degree of fit of the current model to the target state. If the information continues to increase, it indicates that the current dominant model may no longer be applicable. These two input quantities together constitute the key basis for judging the degree of abnormality in the target behavior.

[0048] The fuzzy logic controller has multiple preset fuzzy rules. For example, if the rate of change of acceleration is large and the information sequence grows rapidly, the target is likely in a state of violent maneuvering; if both are small, the target is still in a stable motion phase. The input is mapped to a fuzzy set (such as low, medium, and high) through a membership function, and then after fuzzy inference and defuzzification, the matching probability of each kinematic model is finally output.

[0049] The established model set includes at least three typical kinematic models: constant velocity (CV) model, coordinated turning (CT) model, and current statistical (CA) model. The CV model assumes the target moves at a constant speed along a straight line, suitable for the phase of a target's smooth movement; the CT model describes the process of a target changing direction while maintaining constant speed, particularly suitable for simulating the turning maneuvers of aircraft or ground vehicles; the CA model introduces a stochastic process of acceleration, allowing acceleration to jump within a finite range, suitable for situations where the target experiences sudden acceleration or deceleration. The model selection is not statically configured but dynamically optimized based on real-time acquired firing environment data. For example, in areas with strong winds or significant terrain undulations, the target is more likely to perform evasive maneuvers, in which case the system can pre-increase the initial weights of the CT and CA models; while in open, flat areas, the priority of the CV model is correspondingly increased. This environment-aware model initialization strategy helps shorten the system convergence time and improve initial prediction accuracy.

[0050] Within each filtering cycle, the system performs the following operations: First, it calculates the likelihood function of each model based on the model probability distribution of the previous cycle, i.e., the conditional probability density of the current observation data under each model; then, it updates the posterior probability of each model by combining the matching probability output by fuzzy logic, forming a new weight allocation. During this process, the firing angle and environmental dataset (such as wind speed, temperature, and air pressure) are also incorporated as influencing factors into the weight calculation. For example, when the firing angle is large (uphill / downhill), the influence of gravity on the trajectory is enhanced, and the system will appropriately increase the weight of the CA model to better capture changes in acceleration in the vertical direction; under strong crosswind conditions, it may trigger CT-type maneuver warnings, prompting the CT model to obtain higher confidence.

[0051] When a significant change in the target's motion pattern is detected, such as a shift from constant speed to a sharp turn, the fuzzy logic controller can respond rapidly and drastically adjust the influence weights of each model. Specifically, once the information sequence exceeds a threshold and the rate of acceleration change increases sharply, the system determines this as a mode switching event, immediately reducing the weight of the CV model while increasing the contribution ratio of the CT or CA model, thereby achieving rapid tracking of the new motion state. This process does not require manual setting of the transition probability matrix; it is entirely data-driven and possesses stronger self-learning and adaptive capabilities.

[0052] Through the above technical solution, this application achieves accurate modeling and prediction of target motion states in complex dynamic environments. By employing a fuzzy logic controller to calculate the model matching probability in real time, the system can autonomously identify changes in target behavior characteristics without prior transfer information. Furthermore, by fusing shooting angle and environmental data to adjust model weights, the consistency between prediction results and actual physical conditions is further improved. For example, when a target suddenly transitions from a constant velocity state to a right turn, the filtered information rapidly increases, and the rate of acceleration change significantly increases. Based on this, the fuzzy logic outputs a high matching probability to the CT model, allowing it to dominate the state mixture, thus ensuring that the predicted trajectory promptly follows the target's actual path and guaranteeing the accuracy of subsequent ballistic compensation values. This not only improves the reaction speed of the firing system but also enhances the hit probability in highly dynamic combat scenarios.

[0053] In a preferred embodiment of the invention, when the observed data is abnormal or the system model does not match the actual dynamics, the strong tracking volumetric Kalman filter may fail to respond promptly to sudden state changes, causing the filter residual sequence to deviate from orthogonality. This leads to filter divergence, resulting in drastic fluctuations or even failure of the ballistic drop compensation value. This problem is particularly prominent in high-dynamic target tracking, such as when the target suddenly maneuvers to evade, the sensor is subjected to instantaneous interference, or environmental parameters change abruptly. Traditional filtering algorithms struggle to maintain stable output, thus affecting the continuity and accuracy of shooting corrections.

[0054] This application proposes a strong-tracking capillary Kalman filter algorithm. By introducing a time-varying fading factor, the algorithm forces the filter residual sequence to remain orthogonal in real time, dynamically adjusting the filter gain when observation data becomes abnormal or the model mismatch occurs, ensuring a stable output of the ballistic drop compensation value. The strong-tracking capillary Kalman filter algorithm is a nonlinear filtering method based on a Bayesian estimation framework. Its core lies in approximating the mean and covariance of the nonlinear system through a volume rule, making it suitable for handling ballistic prediction problems with strong nonlinearity. Compared to extended Kalman filtering and unscented Kalman filtering, this algorithm exhibits higher numerical stability and estimation accuracy in high-dimensional nonlinear systems, making it particularly suitable for state tracking of high-speed moving targets.

[0055] The time-varying fading factor is a key adjustment mechanism embedded in the strong-pursuit capillary Kalman filter algorithm framework, used to dynamically adjust the weights of the process noise covariance matrix. This factor is not a fixed constant but is calculated in real-time based on the statistical characteristics of the current filtered residual sequence. Specifically, the system continuously monitors the difference between the covariance of the information sequence and its theoretical expectation, and constructs an orthogonality test index based on this. When this index exceeds a preset threshold, it indicates that the old model information has too much influence on the current estimate, and the system automatically increases the fading factor, thereby enhancing the weight of new observation data in state updates.

[0056] Maintaining orthogonality in real-time forced filtering residual sequences means adjusting the fading factor to make the information sequence statistically approach white noise characteristics, i.e., the residuals are uncorrelated and have a mean of zero. This orthogonality condition is a necessary prerequisite for the optimality of Kalman filtering. In practical implementation, the system constructs a weighted covariance update formula, multiplying the original prediction error covariance by a time-varying coefficient greater than or equal to 1 (i.e., the fading factor) to forget outdated state information and improve the filter's sensitivity to sudden changes.

[0057] Anomalies in observational data, including but not limited to jumps in rangefinding data from sights, sudden increases in wind speed sensor readings, abrupt changes in temperature and air pressure, or brief interruptions in communication links, can cause measurement residuals to deviate significantly from their normal distribution. If left unchecked, these anomalies will lead to misjudging the target's state trend by the filter. In such cases, the time-varying decay factor will rapidly rise to a high level (e.g., 1.5-3.0), forcing the filter to reduce its confidence in historical model predictions and instead rely more on the latest available observations for state correction.

[0058] Model mismatch refers to a discrepancy between the target's actual motion pattern and the currently used set of dynamic models (such as a uniform linear motion (CV) model, a coordinated turn (CT) model, or a current statistical (CA) model). For example, if a target suddenly transitions from uniform flight to a sharp turn before the filter has completed its model switch, the predicted trajectory will lag significantly. In this situation, the strong tracking mechanism identifies mismatch risks early by detecting residual growth trends and accelerates the expansion of state covariance using a fading factor, enabling the filter to receive new observation information more quickly and reducing response delay.

[0059] Dynamically adjusting the filter gain refers to the process of recalculating the Kalman gain matrix based on the time-varying fading factor during the filter update phase. Since the gain directly determines the proportion of the contribution of the observation information to the state correction, this adjustment essentially achieves online control of the filter learning rate: maintaining a low gain in a steady state to suppress noise; and increasing the gain to accelerate convergence when abrupt events occur.

[0060] A stable output of bullet drop compensation means that even under complex conditions, the system can still provide continuous, smooth, and reliable vertical correction suggestions, avoiding frequent aiming point jumps caused by filter oscillations. This is crucial for shooter decision-making, especially in long-range precision shooting missions, where any unnecessary correction fluctuations can mislead operational judgments.

[0061] In one implementation, the system sets the orthogonality test window length to 10 sampling periods, calculating the Mahalanobis distance between the actual and theoretical predicted values ​​of the information covariance matrix in each period. When this distance exceeds the confidence interval (e.g., the 95th quantile of the χ² distribution) for three consecutive periods, a fading factor update mechanism is triggered, using Newton's iteration method to solve for the minimum effective fading coefficient that satisfies the residual orthogonality condition. Subsequently, this coefficient is applied to the current state covariance prediction step, completing an adaptive correction. In another implementation, the initial value of the fading factor is set to 1.0 (i.e., standard capacitive Kalman filter mode), and the system performs a residual analysis every 50 milliseconds. If the information norm growth rate is detected to exceed a set threshold (e.g., 200% / s), the fading factor is immediately increased to above 1.8 and gradually decays back to the baseline value in subsequent periods, forming a pulse response mechanism that can quickly respond to sudden changes while preventing new oscillations caused by over-response.

[0062] Through the above technical solution, this application achieves stable output of ballistic drop compensation values ​​even under conditions of abnormal observation data or model mismatch. Because a time-varying fading factor that can be adjusted in real time according to residual orthogonality is introduced, the filter's adaptability to sudden state changes is enhanced, solving the technical problem of easy divergence in traditional filtering methods under high dynamic environments. Therefore, it achieves the technical effect of improving system robustness and the continuity of ballistic correction.

[0063] In one preferred embodiment of the invention, different combinations of firearm models, ammunition batches, and shooting accessories exhibit significantly different ballistic characteristics in actual use. A generalized ballistic data table is insufficient to meet the personalized needs of high-precision real-time tracking and positioning scenarios. Especially in long-range, highly dynamic target engagement missions, ballistic drop is affected by multiple factors such as barrel wear, differences in ammunition charge, and fluctuations in temperature and pressure. Relying on unverified theoretical ballistic models will lead to calculation errors in aiming corrections, resulting in misses. Furthermore, with combat unit rotations or equipment updates, existing ballistic data becomes invalid, and the system lacks the ability to quickly adapt to new weapon-ammunition combinations, limiting the continuous availability and cross-platform deployment capabilities of the equipment. Therefore, it is urgent to establish a data-driven mechanism based on real shooting performance to ensure that ballistic compensation parameters always remain consistent with the physical performance of the current weapon system, improving the robustness and adaptability of the method in complex combat environments.

[0064] This application proposes the following: It also includes the establishment and retrieval of a ballistic data card: a ballistic data card is pre-set on a slide rule, and the ballistic data card records the baseline ballistic drop at different distances; based on the ballistic drop compensation value, the baseline drop in the ballistic data card is corrected in real time based on environmental data and target dynamics; actual ballistic performance is tested at multiple known distances through live-fire exercises; based on the actual ballistic performance, the ballistic drop at each distance point is recorded, and a relationship table corresponding to distance and drop is established; the relationship table is combined with the target state prediction result adjusted by fuzzy logic to further compensate the ballistic data; when the shooting equipment model, ammunition batch, or shooting accessories are changed, live-fire verification is performed again and the ballistic data card is updated, with the parameter settings of the fuzzy logic controller being calibrated synchronously during the update process.

[0065] This application achieves highly personalized and long-term availability of ballistic compensation parameters by constructing a dynamically updatable closed-loop management mechanism for ballistic data. This mechanism uses live-fire testing as the data source, combining environmental perception and target motion state prediction to perform multi-level corrections on the initial ballistic model, thereby ensuring stable and accurate aiming correction guidance under different combat conditions. The ballistic data card refers to a replaceable information carrier fixed to the surface or back of the slide rule, used to store baseline ballistic drop data for specific firearms and ammunition combinations at typical distances (e.g., 100 meters to 1000 meters, in 50-meter intervals) under standard conditions. Its physical form can be a paper label, waterproof film, e-ink screen, or embedded RFID chip. Paper cards that are easy to fill in manually and read visually can be selected, with a size suitable for the reserved area of ​​the XQF-2 (160×64mm) or XQF-3 (140×50mm) slide rule. The ballistic data card is independently entered by the shooter based on the actual shooting performance of their own equipment, without relying on external databases or general ballistic software output, ensuring the authenticity and exclusivity of the data source. Its purpose is to provide a localized, visualized ballistic reference benchmark for direct use in subsequent compensation calculations.

[0066] Pre-set ballistic data cards on slide rules refer to the process of installing pre-filled ballistic data cards in a designated location, usually on the back of the slide rule, for easy viewing, before the slide rule leaves the factory or is deployed. This operation can be achieved through methods such as pasting, inserting into a slot, or magnetic fixation, ensuring that it is not easily dislodged or damaged in the field. The pre-setting process signifies that the slide rule has been bound to a specific weapon-ammunition system, becoming a dedicated auxiliary tool. Baseline ballistic drop refers to the vertical drop of a bullet from a zeroing distance (e.g., 100 meters or 200 meters) relative to the line of sight at the target distance under standard meteorological conditions (e.g., sea level, temperature 15°C, air pressure 760 mmHg, no wind) and horizontal firing conditions. The unit is usually centimeters or inches. This data serves as the initial input for ballistic calculations, reflecting the basic ballistic performance of the weapon system.

[0067] Based on ballistic drop compensation values, the system performs real-time corrections to the baseline drop in the ballistic data card based on environmental data and target dynamics. This means that after acquiring the calculated distance, the system does not directly use the original values ​​from the ballistic data card. Instead, it combines currently collected environmental data such as wind speed, wind direction, temperature, air pressure, and firing angle, as well as the target's motion state (such as acceleration and turning trends) predicted through adaptive interactive multi-model analysis, to dynamically adjust the baseline drop. For example, in high-temperature, low-pressure environments, air density decreases, drag decreases, and the actual drop is less than the baseline value. Furthermore, when the target is moving laterally at high speed, lead compensation is required, indirectly affecting the effective ballistic path. Such corrections can be achieved through lookup table interpolation combined with empirical formulas, or automatically executed by a built-in algorithm.

[0068] Testing actual ballistic performance at multiple known distances through live-fire exercises is a crucial step in establishing a highly reliable ballistic model. In practice, shooters select several fixed distances (e.g., 300m, 500m, 700m, 900m) at the firing range and conduct multiple test shots using the firearm and ammunition to be calibrated. The average deviation of the impact point relative to the aiming point is recorded, especially the vertical drop. Testing should be conducted under conditions as close to actual combat as possible, including simulating different environmental parameters, and repeated multiple times to eliminate random errors. This process generates first-hand measured data, forming the basis of the personalized ballistic model. Establishing a relationship table between distance and drop is a structured dataset formed based on live-fire testing. This dataset can take the form of a discrete numerical list, a fitted curve, or a multidimensional lookup table. This table not only includes drop but can also be expanded to record parameters such as wind drift and time-of-flight. Its function is to replace the traditional single ballistic function, providing higher resolution and a more realistic compensation basis. The relationship table is combined with the target state prediction results adjusted by fuzzy logic. The ballistic correction is shown to be divided into two stages using secondary compensated ballistic data: the first stage performs static correction based on the measured relationship table, and the second stage integrates the dynamic weights output by the fuzzy logic controller to further optimize the compensation value. The fuzzy logic controller judges the target's maneuver intensity based on the target's acceleration change rate and the filtered information sequence. If a drastic trajectory change is identified, the forward-looking compensation factor is enhanced, making the ballistic solution more predictive. This dual compensation mechanism improves the system's ability to deal with non-uniform targets. When changing the firing equipment model, ammunition batch, or firing accessories, live-fire verification and updating the ballistic data card are required to emphasize that the system's sustainable operation depends on a periodic calibration process. Once the barrel, scope, ammunition type, or ammunition production batch is changed, the original ballistic characteristics may deviate significantly. The aforementioned live-fire testing process must be repeated to rebuild a new distance-drop relationship table and overwrite the original ballistic data card content. This operation ensures that the system always reflects the true performance of the current equipment. The synchronous calibration of the fuzzy logic controller's parameter settings during the update process indicates that not only the ballistic data needs to be updated, but the intelligent prediction module that works in conjunction with it should also be adjusted accordingly. For example, new munitions have higher initial velocities and shorter target response times, requiring corresponding optimization of the thresholds and membership functions in the fuzzy rules to match the new dynamic characteristics. This linked calibration mechanism ensures the overall consistency and optimal performance of the entire tracking and positioning system.

[0069] Through the above technical solutions, this application achieves a fundamental shift in ballistic compensation parameters from general assumptions to measurement-driven approaches. By employing a distance-drop relationship table based on live-fire verification as the data foundation, it solves the aiming inaccuracy problem caused by deviations between theoretical models and actual trajectories. By combining measured data with fuzzy logic prediction results for secondary compensation, it improves the system's response accuracy in high-dynamic scenarios. Furthermore, by establishing a ballistic data card update mechanism linked to controller parameter calibration, it solves the technical challenge of rapid system adaptation after equipment replacement. Therefore, without adding complex electronic equipment, this application significantly improves the practicality, reliability, and cross-platform compatibility of high-dynamic target tracking and positioning methods, extending the service life of slide rule-based auxiliary tools in modern precision shooting missions.

[0070] In a preferred embodiment of the invention, a highly dynamic target continuously moves in a complex environment, with its position, velocity, and acceleration constantly changing. Simultaneously, external firing conditions such as wind speed, wind direction, temperature, air pressure, and firing angle fluctuate in real time. Traditional ranging and ballistic compensation methods typically rely on static calculations based on single observation data, failing to adapt to the continuous displacement of the target and the dynamic evolution of environmental parameters. This leads to ballistic prediction failures and severe aiming point deviations during subsequent firing, especially at long distances, on slopes, or in strong winds, resulting in a significant decrease in hit rate. Furthermore, existing systems lack a mechanism for quantifying the uncertainty of target motion. During the tracking of highly dynamic targets, shooters face difficulties in accurately judging the movement trend and future position of rapidly moving or non-uniformly moving targets, leading to unreasonable aiming lead settings, delayed firing reactions, and impacting the hit probability. Especially in complex battlefield environments or variable weather conditions, relying solely on experience for prediction is insufficient to meet real-time and accuracy requirements. Moreover, the lack of an intuitive way to present trajectory information limits the shooter's understanding of target motion patterns, hindering tactical decision-making and information sharing in multi-person collaborative operations. Existing systems typically only provide static compensation value outputs, lacking the ability to predict future states and provide visualization assistance, making it difficult to support the effective execution of continuous firing missions in dynamic environments.

[0071] This application proposes the following: outputting corrective commands for guiding shooting to instruct the shooter to complete the next shooting task, including: outputting the optimal state estimate after multi-model fusion and strong tracking filtering to generate a target trajectory containing position, velocity, and acceleration information; providing a real-time trajectory display function, supporting two-dimensional and three-dimensional visualization, displaying content including the target's historical trajectory and aiming reference information after reticle unit conversion; predicting the target's trajectory, predicting the target's predicted position within a future period based on the current fused state estimate to guide the shooter to complete the next shooting task; converting the scope adjustment amount into commands applicable to different reticle units, including mils and anatomy units or MOA; and displaying the target's trajectory using a display integrated on a pre-fabricated calculation scale. The display unit shows the adjustment amount to the user or generates an electronic signal to drive the automatic adjustment mechanism of the sight. When the target's physical size exceeds the range of the scale, a scaling method is used to divide both the target's physical size and its corresponding mil value by the same factor to bring them within the scale range for calculation. The result is then multiplied by the corresponding factor to obtain the actual distance. When the target's mil reading is less than 1 mil, a fine measurement method with 0.1 mil increments is used, employing a scale of 0.3-4.5 mils for accurate ranging of distant targets. It supports mutual conversion between mils and MOA (Mean Interval). Based on the mapping relationship between the distance calculation result and the mil or MOA conversion coefficient, the adjustment amount corresponding to the ballistic drop compensation value is calculated. During the calculation of the adjustment amount... The target's motion velocity vector and angular velocity parameters are synchronously introduced to achieve dynamic reticle correction. When the target is in a high-speed lateral maneuver, a feedforward control algorithm is used to compensate for the lead of the mil or MOA conversion results. The compensation amount is positively correlated with the target's maneuver acceleration, and dynamic calibration is performed using the target position deviation predicted by Kalman filtering. Target visual size data and environmental data are continuously acquired, with the accuracy of the visual size data acquisition directly affected by the calibration results of the aforementioned feedforward control algorithm, ensuring measurement accuracy under high-speed maneuver conditions. The processor cyclically executes the ballistic compensation algorithm, dynamically updating the ballistic drop compensation value based on the continuously acquired visual size data and environmental data. This update process forms a closed loop with the compensation results of the feedforward control algorithm. The system includes a feedback mechanism; real-time feedback based on dynamically updated ballistic compensation values ​​is provided to the shooter through changes in the scale of the slide rule or the numerical changes in the display unit, offering aiming reference synchronized with the target's maneuvering state; the ballistic compensation value obtained by cyclically executing the ballistic compensation algorithm is combined with the target state information predicted by AIMM to determine the offset of the aiming point relative to the target center; considering the influence of the shooting angle obtained by the angle measurement device on the ballistics, when the shooting angle is greater than a preset angle of 15 degrees, the ballistic compensation value is recalculated using the horizontal distance calculated based on the cosine compensation method; the wind deflection correction amount calculated by combining real-time collected environmental datasets such as wind speed and wind direction is integrated with the angle compensation and ballistic compensation results, and the final three-dimensional coordinates of the aiming point are calculated based on the comprehensive output.Through the above technical solutions, this application achieves comprehensive modeling and forward guidance of the motion behavior of highly dynamic targets, improving the shooter's situational awareness and tactical response efficiency in complex scenarios.

[0072] The output of corrective instructions to guide the shooter in completing the next firing mission means that the system not only outputs the currently calculated distance data, but also integrates the ballistic calculation results with the target motion state analysis to form comprehensive guidance information for the next firing cycle. This output is no longer limited to single numerical feedback, but constructs a complete information chain from historical observation to current state and future prediction, providing shooters with highly operable and low-cognitive-load decision support. This output mechanism breaks through the technical limitations of traditional ranging equipment that only provides static parameters, giving the entire tracking and positioning process continuity and strategic guidance in the time dimension. Outputting the optimal state estimate after multi-model fusion and strong tracking filtering means that the system uses the optimal target state estimate obtained by combining an adaptive interactive multi-model algorithm with strong tracking capacitive Kalman filtering as the output basis. This optimal state estimate is the result of dynamic weighted fusion of multiple motion models (such as uniform linear motion CV model, coordinated turning CT model, current statistical CA model, etc.) according to the actual target motion pattern, and the STCKF applies a time-varying fading factor to the filtered residual sequence to ensure stable tracking even when the target maneuvers abruptly or the observation noise is abnormal. This state estimate contains the target's precise position coordinates in three-dimensional space, instantaneous velocity vector, and rate of change of acceleration, forming the data source for subsequent trajectory generation and prediction. For example, in a long-range sniping mission, when the target suddenly changes from uniform speed to a sharp turn, the system can quickly identify this maneuvering feature and switch the dominant model weights, outputting an updated high-confidence state estimate to avoid prediction bias caused by model mismatch.

[0073] Generating a target motion trajectory containing position, velocity, and acceleration information means that the system records and organizes the target's historical motion states in a time-series manner, forming a continuous spatial path curve. This trajectory not only reflects the target's past movement path but also supplements missing intermediate points through interpolation and extrapolation algorithms, improving trajectory completeness. Trajectory data can be stored locally in a cache or uploaded to the command terminal for post-battle review and analysis. For example, in urban counter-terrorism operations, the system can replay the walking trajectory of a suspect over the past 25-30 seconds to help commanders determine their intentions; simultaneously, the velocity and acceleration information carried in the trajectory can be used to identify whether the target is running, stationary, or feigning a slow walk, enhancing situational understanding. Providing real-time trajectory display functionality and supporting 2D and 3D visualization means the system is equipped with a graphical user interface module, capable of transforming abstract state data into intuitive and visual image output. The 2D view is suitable for overlaying conventional planar maps for quick positioning; the 3D view, combined with terrain elevation data, realistically recreates the target's three-dimensional movement path in complex terrain, particularly suitable for long-range sniping missions in mountainous, jungle, or desert environments. In addition to the basic trajectory line, the displayed content includes key time node markers, movement direction arrows, velocity heatmaps, and aiming reference scale rings converted from reticle units (such as mils or arcminutes / MOA). For example, the system can overlay a virtual lead circle on the electronic display screen to indicate how many mils in front of the target's current position the shooter should aim, thereby reducing the burden of manual calculation.

[0074] The displayed content includes the target's historical trajectory and aiming reference information converted from reticle units, emphasizing practicality in the human-computer interaction design. The historical trajectory helps the shooter build spatial memory and identify possible escape routes or evasion patterns of the target; while the reticle unit conversion function directly aligns with the operating habits of the scope, automatically converting complex physical displacements into familiar adjustment units (such as 1 / 4 MOA per click), achieving a WYSIWYG operating experience. For example, when the system calculates that an upward correction of 2.8 mils is needed, the interface will simultaneously highlight the corresponding scale interval and indicate +2.8mil up, reducing the risk of misreading. The system predicts the target's trajectory, estimating its predicted position within a certain timeframe based on the current fusion state, demonstrating its forward-looking capabilities. The prediction process uses a state extrapolation algorithm based on the AIMM-STCKF framework, combining the target's current movement trend with environmental constraints (such as prior knowledge of boundary obstacles and road directions) to extrapolate its possible path within the next few seconds. The prediction time window can be set according to task requirements, typically ranging from 1 to 5 seconds. For example, when hunting large wild animals, the system predicts that the wild boar will cross diagonally to the right at a speed of about 6 m / s and is expected to reach the edge of a bush in 3 seconds. Based on this, the system recommends that the shooter move the aiming point 1.5 mil to the right in advance, which significantly improves the success rate of the first shot.

[0075] Converting the scope adjustment values ​​into instructions applicable to different reticle units, including mils and megohms (MOA), means the system possesses an automatic conversion capability across multiple reticle units to accommodate the operating habits of different scope models. This conversion process is based on precise mathematical formulas, and the system's built-in conversion algorithm can perform real-time bidirectional unit conversions. For example, when a 2.5 mil upward adjustment is calculated, the system can simultaneously output the corresponding 8.6 MOA adjustment value for shooters using different reticle systems. This multi-point support function eliminates operational barriers caused by equipment differences, improving the system's versatility and combat adaptability. The adjustment values ​​are displayed to the user via a pre-installed slide rule integrated display unit, or electronic signals are generated to drive the scope's automatic adjustment mechanism, reflecting the system's dual design philosophy in human-computer interaction. For traditional mechanical slide rules, the system intuitively presents numerical adjustment suggestions via an LCD or LED display; for intelligent aiming systems, standardized electronic control signals can be output to directly drive the electric adjustment mechanism inside the scope to complete automatic calibration. This dual-mode output method maintains compatibility with traditional equipment while reserving interfaces for the integration of future intelligent weapon systems, achieving a smooth transition in technological evolution. When the physical size of the target exceeds the scale range of the slide rule, a scaling method is used. The physical size of the target and its corresponding mil value are simultaneously divided by the same factor to bring it within the scale range for calculation. The calculated result is then multiplied by the corresponding factor to obtain the actual distance, overcoming the limitations of traditional slide rules when dealing with extremely large or small targets. For example, when observing a large aircraft with a wingspan of 30 meters, if the maximum scale of the slide rule only supports targets of 10 meters, the system automatically divides 30 meters and the corresponding mil reading by 3 simultaneously. After completing the distance calculation within the scale range, the result is multiplied by 3 to obtain the true distance. This scaling mechanism significantly expands the system's applicability, enabling it to handle various targets from small UAVs to large vehicles.

[0076] When the target mil reading is less than 1 mil, a fine measurement method with 0.1 mil increments is used, employing a scale of 0.3-4.5 mils for accurate ranging of distant targets. This method is specifically optimized for ultra-long-range shooting scenarios. In traditional ranging methods, readings less than 1 mil often lead to significant distance errors. However, the fine measurement method, through subdivided scales and interpolation algorithms, improves ranging accuracy to the sub-mil level. For example, when a target occupies only 0.6 mils at a distance of 1200 meters, the system can accurately identify and calculate the precise distance, providing reliable data support for ultra-long-range sniping. The system supports conversion between mils and MOA (Mean Interval), ensuring compatibility with mainstream international aiming equipment. This conversion function not only supports static numerical conversion but also allows real-time switching of display units during dynamic tracking, meeting the operational needs of multinational joint operations or the use of imported equipment. The conversion accuracy reaches two decimal places, ensuring that no additional errors are introduced by unit conversion in high-precision shooting missions. Based on the mapping relationship between distance calculation results and mil or MOA conversion coefficients, the reticle adjustment amount corresponding to the ballistic drop compensation value is calculated. During the calculation of the adjustment amount, the target motion velocity vector and angular velocity parameters are simultaneously introduced to achieve dynamic reticle correction, extending static ballistic calculation into a dynamic compensation mechanism. This process not only considers ballistic drop caused by gravity but also incorporates the influence of the target's motion state on the aiming point, deriving a comprehensive correction amount through a vector synthesis algorithm. For example, for a target moving laterally at a speed of 15 m / s, the system automatically superimposes a lateral lead while calculating vertical drop compensation, outputting a composite adjustment command containing both horizontal and vertical components.

[0077] When the target is in a high-speed lateral maneuver, a feedforward control algorithm compensates for the lead of the mil or MOA conversion results. The compensation amount is positively correlated with the target's maneuvering acceleration. Dynamic calibration is performed using the target position deviation predicted by a Kalman filter, enabling proactive prediction of highly maneuvering targets. Based on the target's current acceleration trend, the feedforward control algorithm estimates its displacement during the bullet's flight time and converts this estimate into a corresponding reticle adjustment. Simultaneously, the Kalman filter continuously evaluates the prediction accuracy and automatically calibrates when a systematic deviation is detected, ensuring the accuracy and stability of the lead calculation. Target visual dimension data and environmental data are continuously acquired. The accuracy of the visual dimension data acquisition is directly affected by the calibration results of the aforementioned feedforward control algorithm, ensuring measurement accuracy under high-speed maneuvering conditions and forming a positive feedback loop between data acquisition and algorithm optimization. High-precision calibration results improve the reliability of visual dimension measurements, while accurate dimension data provides high-quality input for subsequent algorithm iterations. This self-optimization mechanism enables the system to demonstrate learning and adaptability during long-term use. The processor cyclically executes the ballistic compensation algorithm, dynamically updating the ballistic drop compensation value based on continuously acquired visual size data and environmental data. This update process, together with the compensation result of the feedforward control algorithm, forms a closed-loop feedback mechanism, ensuring the system's real-time response capability and long-term stability. The cyclic execution frequency can be adaptively adjusted according to the target's maneuverability; for high-speed targets, it can be increased to 10-20 updates per second, while for relatively stationary targets, it is reduced to 2-3 updates per second, to balance computational load and response speed.

[0078] By adjusting the scale of the slide rule or the numerical changes on the display unit, the system provides real-time guidance based on dynamically updated ballistic compensation values, offering the shooter an aiming reference synchronized with the target's movement. This achieves seamless integration between the algorithm output and the user interface. Whether it's the deflection of the mechanical slide rule pointer or the numerical fluctuations on the electronic display, the system responds instantly to the algorithm's calculations, providing the shooter with continuous and smooth operational guidance. Based on the ballistic compensation values ​​obtained from the cyclically executed ballistic compensation algorithm, combined with the target state information predicted by AIMM, the offset of the aiming point relative to the target center is determined, achieving a deep fusion of ballistic physics and target kinematics. This offset not only includes traditional gravity compensation components but also incorporates multi-dimensional factors such as target motion prediction and environmental disturbance correction, forming a comprehensive aiming solution. Considering the impact of the firing angle obtained through angle measurement equipment on the ballistics, when the firing angle exceeds a preset angle of 15 degrees, the ballistic compensation value is recalculated using the horizontal distance calculated based on the cosine compensation method, specifically addressing the ballistic correction problem in inclined firing scenarios. The cosine compensation method eliminates the interference of angle factors on ballistic calculations by projecting the slant range as an equivalent horizontal distance, ensuring shooting accuracy even in complex terrain environments such as mountains and tall buildings. The wind drift correction calculated using real-time collected environmental datasets such as wind speed and direction is integrated with the angle and ballistic compensation results. Based on the comprehensive output, the final three-dimensional coordinates of the aiming point are calculated, constructing a complete multi-factor ballistic correction system. This three-dimensional coordinate system not only considers ballistic drop in the vertical plane but also includes the influence of wind drift and tilt angle correction in the horizontal plane, providing shooters with comprehensive aiming guidance. This guides shooters to complete the next firing mission, indicating that the predicted results are not merely for observation but directly participate in the next round of firing preparation. The system can automatically map the predicted position to the ballistic compensation calculation module, generate corresponding scope adjustment suggestions in advance, and transmit them to the shooter through voice broadcast, vibration alerts, or HUD (Heads-Up Display). This closed-loop mechanism shortens the time delay of the observation-decision-action chain, making it particularly suitable for tactical scenarios requiring continuous engagement of multiple dynamic targets.

[0079] Through the above technical solutions, this application proposes a preferred embodiment in which a high-precision, interference-resistant target trajectory is generated by fusing multi-source state information, solving the problem that traditional ranging tools cannot reflect the dynamic characteristics of targets. Secondly, by using two-dimensional and three-dimensional visualization methods, complex data is transformed into an easy-to-understand graphical interface, reducing the cognitive load on the shooter. Thirdly, future position prediction is performed based on fused state estimation, providing a scientific basis for lead setting and effectively addressing the challenges posed by highly maneuverable targets. Finally, the prediction results are deeply integrated with aiming reference information to form an active guidance mechanism for the next firing mission, significantly improving overall combat effectiveness. For example, in a simulated combat exercise, the shooter successfully hit a simulated enemy personnel running at high speed in a zigzag pattern using this system, achieving a hit rate of 78-82%, an improvement of more than 40-45% compared to the traditional mode without prediction function. This embodiment verifies the substantial progress of this application in improving the ability to strike dynamic targets. This provides a real-time tracking and positioning method for highly dynamic targets, aiming to construct a dynamic closed-loop system with adaptive feedback capabilities. Through continuous perception of target visual features and environmental parameters, the ballistic calculation module is driven to run periodically, thereby continuously optimizing the ballistic compensation value and ultimately outputting the optimal aiming point coordinates in three-dimensional space. The core of this embodiment lies in upgrading the traditional one-time ranging-compensation mode to an iterative tracking process of perception, calculation, feedback, and re-perception. This enables the system to maintain accurate modeling and correction capabilities of the ballistic state throughout the target's movement, improving the overall hit probability under continuous firing conditions. Continuously acquiring target visual size data and environmental data refers to continuously reading mil values ​​or angular fractions from the optical reticle system at a preset sampling frequency (e.g., 5-20 frames per second) through a data acquisition unit integrated into the aiming device, while simultaneously acquiring environmental parameters such as wind speed, wind direction, temperature, air pressure, and firing angle provided by a sensor array. Visual size data is derived from the angular span occupied by the target in the scope reticle. Combined with known or estimated target physical dimensions (e.g., a human height of approximately 1.7 meters, a vehicle width of approximately 1.8 meters), this forms the basic input for distance calculation. Environmental data can be acquired through embedded weather sensors or external wireless transmission devices, ensuring data timestamp alignment with the coordinate system and avoiding the accumulation of compensation errors caused by asynchronous acquisition. By repeatedly executing the ballistic compensation algorithm, the ballistic drop compensation value is dynamically updated based on continuously acquired visual size and environmental data. This means the system no longer relies on a single measurement result to complete the entire firing decision process, but instead repeatedly calls the ballistic calculation logic at fixed intervals (e.g., every 200 milliseconds). Each iteration uses the previous state estimate as the initial condition, combines the latest observation data, and re-executes adaptive interactive multi-model prediction and strong tracking volumetric Kalman filter correction to generate the optimal ballistic drop compensation value for the current moment.This process supports proactive adjustments to the flight path of long-range munitions with extended flight times (such as those with a range of over 800 meters), effectively addressing maneuvering behavior of the target within the time window before impact.

[0080] The real-time feedback of corrections based on dynamically updated ballistic compensation values, achieved through changes in the scale of the slide rule or the numerical changes in the display unit, demonstrates innovative design at the human-computer interaction level. Pre-fabricated analog slide rules can directly present the required elevation (unit: mil or MOA) via a mechanical sliding structure, facilitating quick reading by the shooter. For smart slide rules equipped with electronic displays or mobile applications, recommended scope adjustments can be dynamically refreshed using digital overlay. Regardless of the method, the output responds instantly with each update of the ballistic compensation value, providing intuitive and continuous operational feedback to help the shooter maintain the correct aiming posture during target movement. Based on the ballistic compensation value obtained from the cyclically executed ballistic compensation algorithm, combined with the target state information predicted by the AIMM, the offset of the aiming point relative to the target center is determined, involving proactive prediction of the spatial relationship between the future impact point and the expected target position. The AIMM module maintains the probability weights of multiple motion models (such as uniform linear motion (CV), coordinated turning (CT), and current statistical motion (CA), identifies the changing trends of the target's motion pattern in real time, and outputs a fused state estimate including position, velocity, acceleration, and their covariance matrix. Building upon this, the target position uncertainty information (represented as the trace or main diagonal elements of the covariance matrix) assessed by the strong tracking capacitive Kalman filter is introduced to quantify the risk level of prediction bias. When the uncertainty is high, the system automatically expands the lead calculation range or fine-tunes the aiming point to the area in front of the target's direction of movement to improve the probability of hitting the target.

[0081] The ballistic compensation value is recalculated using the horizontal distance calculated based on the cosine compensation method, which can be measured by a built-in tilt sensor or an external plumb line device. This horizontal distance serves as a new input parameter for ballistic calculation, replacing the original slant range in subsequent drop lookup and compensation value generation, thereby eliminating overcompensation caused by the inconsistency between the direction of gravity and the ballistic projection. This step is particularly suitable for non-planar terrain scenarios such as mountain warfare and urban sniping, ensuring ballistic accuracy when firing from slopes. The wind deflection correction amount calculated by combining real-time collected environmental datasets such as wind speed and direction is integrated with the angle compensation and ballistic compensation results. Based on the comprehensive output, the final three-dimensional coordinates of the aiming point are calculated, realizing the collaborative modeling of multi-dimensional disturbance factors. The wind deflection correction amount is derived from classical ballistic formulas or lookup tables, taking into account factors such as lateral wind speed components, air density, and projectile size, generating corresponding lateral adjustment amounts (unit: mil or MOA). The lateral correction, together with the aforementioned vertical drop compensation and angle compensation, constitutes a complete two-dimensional aiming correction vector. Further, by combining the target depth information (i.e., distance), it can be expanded into the aiming point coordinates (x, y, z) in three-dimensional space, and matched with the target's future position predicted by AIMM to ensure that the ballistic trajectory endpoint and the target's motion trajectory intersect in the spatiotemporal dimension.

[0082] Through the above technical solution, this application achieves full-process dynamic tracking and multi-source compensation integration for highly dynamic targets. In a typical application scenario, a shooter faces a target running across an open area at varying speeds. After initially measuring the target's height to 2.1 mil and the ambient wind speed to 8 m / s (southeast wind), the system initiates initial ballistic calculations and outputs aiming correction suggestions for the first bullet. Subsequently, over the next 3 seconds, the system collects new data every 200 ms. If the target begins to turn left and the wind direction changes to southwest, the AIMM model automatically enhances the weights of the CT (Coordinated Turn) model, and the STCKF filter detects abrupt changes in the information sequence and triggers fading factor adjustments, thereby updating the predicted trajectory. Simultaneously, the vertical and horizontal correction values ​​on the slide rule display are updated synchronously, guiding the shooter to smoothly move the aiming point. When the fifth cycle is completed, the system determines that the optimal firing opportunity has arrived, and the recommended three-dimensional aiming point has accurately pointed to the spatial position where the target is about to pass, ultimately achieving a first-shot hit or continuous fire coverage. Because the system has the ability to continuously sense and dynamically update, it solves the problems of lag and bias caused by static calculation in traditional methods, thus significantly improving the accuracy and response speed of striking high-speed moving targets in complex dynamic environments.

[0083] In one preferred embodiment of the invention, during high-dynamic target tracking, shooters face difficulties in accurately judging the movement trend and future position of rapidly moving or non-uniformly moving targets. This leads to unreasonable aiming lead settings and delayed firing reaction, affecting the probability of a hit. Especially in complex battlefield environments or under variable weather conditions, relying solely on experience for prediction is insufficient to meet real-time and accuracy requirements. Furthermore, the lack of an intuitive way to present trajectory information limits the shooter's understanding of target movement patterns, hindering tactical decision-making and information sharing in multi-person collaborative operations. Existing systems typically only provide static compensation value outputs, lacking the ability to predict future states and providing visualization assistance, making it difficult to support the effective execution of continuous firing missions in dynamic environments.

[0084] This application proposes the following: outputting corrective instructions for guiding shooting to guide the shooter in completing the next shooting task. This includes: outputting the optimal state estimate after multi-model fusion and strong tracking filtering to generate a target trajectory containing position, velocity, and acceleration information; providing real-time trajectory display functionality, supporting two-dimensional and three-dimensional visualization, displaying the target's historical trajectory and aiming reference information after reticle unit conversion; and predicting the target's trajectory, predicting the target's future position within a certain period based on the current fused state estimate to guide the shooter in completing the next shooting task. Through the above technical solution, this application achieves comprehensive modeling and forward guidance of highly dynamic target motion behavior, improving the shooter's situational awareness and tactical response efficiency in complex scenarios. Specifically, outputting corrective instructions for guiding shooting to guide the shooter in completing the next shooting task means that the system not only outputs the currently calculated distance data but also further integrates ballistic calculation results with target motion state analysis to form comprehensive guidance information for the next shooting cycle. This output is no longer limited to single numerical feedback but constructs a complete information chain from historical observation to current state to future prediction, providing the shooter with highly operable and low cognitive load decision support. This output mechanism breaks through the technical limitations of traditional ranging devices that only provide static parameters, enabling the entire tracking and positioning process to have continuity in the time dimension and strategy guidance.

[0085] Outputting the optimal state estimate after multi-model fusion and strong tracking filtering means that the optimal target state estimate obtained by the system using an adaptive interactive multi-model algorithm combined with strong tracking capacitive Kalman filtering is used as the output basis. This optimal state estimate is the result of dynamic weighted fusion of multiple motion models (such as uniform linear motion CV model, coordinated turning CT model, current statistical CA model, etc.) according to the actual motion mode of the target. A time-varying fading factor is applied to the filter residual sequence through STCKF to ensure stable tracking even when the target maneuvers abruptly or the observation noise is abnormal. This state estimate contains the target's precise position coordinates, instantaneous velocity vector, and rate of change of acceleration in three-dimensional space, forming the data source for subsequent trajectory generation and prediction. For example, in a long-range sniping mission, when the target suddenly changes from uniform motion to a sharp turn, the system can quickly identify this maneuvering feature and switch the dominant model weights to output an updated high-confidence state estimate, avoiding prediction bias caused by model mismatch. Generating the target motion trajectory containing position, velocity, and acceleration information means that the system records and organizes the target's historical motion state in a time-series manner, forming a continuous spatial path curve. This trajectory not only reflects the target's past movement path but also supplements missing intermediate points through interpolation and extrapolation algorithms, improving trajectory completeness. Trajectory data can be stored locally or uploaded to the command terminal for post-battle analysis. For example, in urban counter-terrorism operations, the system can replay the suspect's walking trajectory over the past 25-30 seconds to help commanders determine their intentions; simultaneously, the speed and acceleration information carried in the trajectory can be used to identify whether the target is running, stationary, or feigning a slow walk, enhancing situational understanding. Real-time trajectory display is provided, supporting both 2D and 3D visualization, meaning the system is equipped with a graphical user interface module that can transform abstract state data into intuitive and visual image output. The 2D view is suitable for overlaying conventional planar maps for quick positioning; the 3D view, combined with terrain elevation data, realistically recreates the target's three-dimensional movement path in complex terrain, particularly suitable for long-range sniping missions in mountainous, jungle, or desert environments. In addition to the basic trajectory line, the displayed content includes key time node markers, movement direction arrows, velocity heatmaps, and aiming reference scale rings converted from mils or minutes of arc (MOA). For example, the system can overlay a virtual lead circle on the electronic display screen to indicate how many mils in front of the target's current position the shooter should aim, thereby reducing the burden of manual calculation.

[0086] The displayed content includes the target's historical trajectory and aiming reference information converted from reticle units, emphasizing practicality in the human-computer interaction design. The historical trajectory helps the shooter build spatial memory and identify possible escape routes or evasion patterns of the target; while the reticle unit conversion function directly aligns with the operating habits of the scope, automatically converting complex physical displacements into familiar adjustment units (such as 1 / 4 MOA per click), achieving a WYSIWYG operating experience. For example, when the system calculates that an upward correction of 2.8 mils is needed, the interface will simultaneously highlight the corresponding scale interval and indicate +2.8mil up, reducing the risk of misreading. The system predicts the target's trajectory, estimating its predicted position within a certain timeframe based on the current fusion state, demonstrating its forward-looking capabilities. The prediction process uses a state extrapolation algorithm based on the AIMM-STCKF framework, combining the target's current movement trend with environmental constraints (such as prior knowledge of boundary obstacles and road directions) to extrapolate its possible path within the next few seconds. The prediction time window can be set according to task requirements, typically ranging from 1 to 5 seconds. For example, when hunting large wild animals, the system predicts that a wild boar will cross diagonally to the right at a speed of approximately 6 m / s and reach the edge of a bush in about 3 seconds. Based on this, the system recommends that the shooter move their aiming point 1.5 mils to the right in advance, significantly improving the success rate of the first shot. This guides the shooter in completing the next firing task, demonstrating that the prediction is not merely for observation but directly participates in the preparation process for the next round of firing. The system can automatically map the predicted position to the ballistic compensation calculation module, generate corresponding scope adjustment suggestions in advance, and transmit them to the shooter through voice broadcast, vibration alerts, or a HUD (Heads-Up Display). This closed-loop mechanism shortens the time delay in the observation-decision-action chain, making it particularly suitable for tactical scenarios requiring continuous engagement of multiple dynamic targets.

[0087] like Figure 2 As shown, a high-dynamic target real-time tracking and positioning system includes: Data acquisition module: used to acquire multiple target observation data and firing environment data; the target observation data includes mil values ​​or angle fractions measured through the scope and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle; Distance Calculation Module: Based on mil or angle fraction and known physical dimensions, it aligns the distance calculation scale on a pre-made slide rule to obtain the calculated distance to the target. Aiming correction module: used to calculate the ballistic drop compensation value based on the measured distance and the shooting environment data through a ballistic compensation algorithm; the ballistic compensation algorithm uses an adaptive interactive multi-model to predict the target state and uses a strong tracking volumetric Kalman filter to correct the prediction results; Command output module: Based on the ballistic drop compensation value, it generates the corresponding scope adjustment amount by operating the ballistic compensation scale on the pre-made calculation ruler, and outputs correction commands to guide the shooter to complete the next shooting task.

[0088] A computer-readable storage medium is provided for storing computer-readable instructions that, when read by a computer, enable the execution of a high-dynamic target real-time tracking and positioning method. Furthermore, the computer-readable storage medium also supports digital modeling and retrieval functions for ballistic data cards. The ballistic data card is pre-stored in a local database or cloud server, recording the baseline ballistic drop at different distances for a specific firearm-ammunition combination. The system performs real-time correction of the baseline data based on measured environmental parameters (such as temperature, air pressure, wind speed, etc.) and target dynamic information, and performs secondary compensation by combining adaptive interactive multi-model prediction results to form a personalized, self-learning ballistic compensation curve. When the user changes the firearm model, ammunition batch, or key components (such as the barrel or scope), the system prompts for a re-performation live-fire test and automatically guides the acquisition, calibration, and updating of new ballistic data, while simultaneously optimizing the parameter configuration of the fuzzy logic controller to ensure algorithm adaptability.

[0089] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for real-time tracking and positioning of highly dynamic targets, characterized in that, include: Acquire multiple target observation data and firing environment data; the target observation data includes mil or arc fraction values ​​measured through the sight and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle; The distance is calculated by directly reading the known physical dimensions of the target and the mil value measured by the scope; if the value is outside the scale range, it is scaled proportionally before calculation to obtain the target's calculated distance. Based on the measured distance and the shooting environment data, the ballistic compensation algorithm calculates the ballistic drop compensation value; the ballistic compensation algorithm predicts the target state and obtains the target's real-time coordinates, the magnitude and direction of the target's velocity vector, the target's acceleration changes, and the target's motion type target state prediction results. The target state prediction result is then corrected. Based on the ballistic drop compensation value, a corresponding scope adjustment amount is generated, and correction instructions for the scope adjustment amount, target trajectory, predicted aiming point, ballistic drop compensation value, and wind drift correction amount are output to guide the shooter to complete the next shooting mission.

2. The high dynamic target real-time tracking and positioning method according to claim 1, characterized in that, Alignment is achieved by manipulating the distance calculation scale on the slide rule, including: Collect multi-source observation datasets, and perform data preprocessing, timestamp alignment, and coordinate system unification operations to obtain a unified observation dataset; Based on a unified observation dataset, read the mil value or angular fraction value of the target in the reticle of the aiming scope; When the physical size of the target and the measured mil or angle fractions exceed the scale range of the slide rule, a scaling method is used to obtain the final calculated target distance. The scaling method involves dividing the target physical size and the mil value or angle fraction by a uniform integer multiple to obtain the intermediate size and the intermediate mil value or angle fraction. The intermediate distance is calculated using a prefabricated slide rule based on the intermediate dimension and the intermediate mil value or intermediate angle fraction; the intermediate distance is then multiplied by the same integer multiple to obtain the final target measurement distance.

3. The high dynamic target real-time tracking and positioning method according to claim 1, characterized in that, The ballistic compensation algorithm includes: Based on the firing angle, the target's measured distance is corrected to a horizontal distance using the cosine compensation method, where horizontal distance = measured distance × cos firing angle; The horizontal distance, the shooting environment data, and the target state prediction result obtained through adaptive interactive multi-model prediction are all used as the input parameter set and input into the strong tracking capacitive Kalman filter algorithm to calculate the ballistic drop compensation value. The target state includes the target's real-time coordinates in three-dimensional space, the magnitude and direction of the target's velocity vector, and the changes in the target's acceleration. It is used to determine whether the target is maneuvering and to dynamically identify the target's motion type through a multi-model approach based on constant speed, coordinated turning, and current statistics.

4. The high dynamic target real-time tracking and positioning method according to claim 3, characterized in that, The target state prediction of the adaptive interactive multi-model includes: Establish a model set that includes a uniform velocity model, a coordinated turning model, and the current statistical model; The matching probability of each model in the model set is dynamically calculated by a fuzzy logic controller; the input of the fuzzy logic controller includes the target acceleration rate of change and the filter information sequence. Based on the matching probability, the predicted state and covariance of each model in the model set are interactively mixed and then weighted and fused to output the target state prediction result. In each filtering cycle, the likelihood function and posterior probability of each model are calculated, and the influence weights of the shooting angle and shooting environment data on the target motion prediction are fused. When the target motion pattern changes, the influence weights of each model are adjusted to achieve rapid adaptation to different patterns.

5. The high dynamic target real-time tracking and positioning method according to claim 4, characterized in that, The strong tracking volumetric Kalman filter algorithm introduces a time-varying fading factor to force the filter residual sequence to remain orthogonal, thereby dynamically adjusting the filter gain and ensuring the stable output of the ballistic drop compensation value.

6. The high dynamic target real-time tracking and positioning method according to claim 1, characterized in that, It also includes the creation and retrieval of ballistic data cards: A preset ballistic data card is mounted on the slide rule, and the ballistic data card records the reference ballistic drop at different distances; Based on the shooting environment data and target state prediction results, the baseline drop in the ballistic data card is corrected in real time. Establish a table showing the correspondence between distance and ballistic drop through live-fire tests; The relation table is combined with the target state prediction result adjusted by fuzzy logic for dynamic compensation of ballistic data; When the firing equipment, ammunition, or accessories are replaced, live-fire verification is performed again and the ballistic data card is updated, while the parameters of the fuzzy logic controller are calibrated.

7. The high dynamic target real-time tracking and positioning method according to claim 1, characterized in that, It executes continuously in a loop, continuously acquiring target visual size data and environmental data; Based on continuously acquired target observation data and firing environment data, the ballistic drop compensation value and the scope adjustment amount are dynamically updated. The changes in the scale of the slide rule or the numerical changes in the display unit reflect the correction guidance based on the dynamically updated ballistic drop compensation value and the scope adjustment amount in real time. Based on the correction guidance, the ballistic compensation algorithm is executed to obtain the ballistic compensation value. Combined with the target state information predicted by the adaptive interactive multi-model, the offset of the aiming point relative to the target center is obtained. The offset calculation incorporates the wind offset correction amount calculated using the target position corrected by strong tracking volume Kalman filtering and the wind speed and direction to obtain the fused result. Based on the fusion results, the final three-dimensional coordinates of the aiming point are calculated by comprehensively outputting the ballistic trajectory and the target position predicted by the adaptive interactive multi-model.

8. The high dynamic target real-time tracking and positioning method according to claim 1, characterized in that, Output corrective instructions for guided shooting to guide the shooter in completing the next shooting task, including: The target state estimate after processing by adaptive interactive multi-model and strong tracking capacitive Kalman filter is output to generate the target motion trajectory containing position, velocity and acceleration information. It provides real-time trajectory display, supports two-dimensional and three-dimensional visualization, and displays content including the target location's historical trajectory and aiming reference information after the conversion of division units; Predict the target's trajectory and predict its position over a future period based on the target's state estimate to guide the shooter in completing the next shooting mission; The correction commands include: scope adjustment: elevation or wind drift correction value in mils or minutes; target trajectory: target trajectory: two-dimensional or three-dimensional trajectory display of historical position and predicted future position; predicted aiming point: aiming point with lead calculated based on the target's future position; ballistic drop compensation value: vertical correction amount after considering gravity, environment, and angle; and wind drift correction amount: lateral correction amount calculated based on wind speed and direction.

9. A high-dynamic target real-time tracking and positioning system, used to execute a high-dynamic target real-time tracking and positioning method as described in any one of claims 1-8, characterized in that, include: Data acquisition module: used to acquire observation data of multiple targets and firing environment data; The target observation data includes mil or arc fraction values ​​measured through the aiming scope and the known physical dimensions of the target; the firing environment data includes at least one of wind speed, wind direction, temperature, air pressure and firing angle. Distance Calculation Module: Based on mil or angle fraction and known physical dimensions, it aligns the distance calculation scale on a pre-made slide rule to obtain the calculated distance to the target. Aiming correction module: used to calculate the ballistic drop compensation value based on the measured distance and the shooting environment data through a ballistic compensation algorithm; the ballistic compensation algorithm uses an adaptive interactive multi-model to predict the target state and uses a strong tracking volumetric Kalman filter to correct the prediction results; Command output module: Based on the ballistic drop compensation value, it generates the corresponding scope adjustment amount by operating the ballistic compensation scale on the pre-made calculation ruler, and outputs correction commands to guide the shooter to complete the next shooting task.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable instructions, which, when read by a computer, enable the execution of a high-dynamic target real-time tracking and positioning method as described in any one of claims 1-8.

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