A dual-sensor dead reckoning method and system for an ATP tracking platform

Abstract

The application discloses a double-sensor pre-estimation ranging method and system for an ATP tracking platform, which records coarse television miss distance and coarse infrared miss distance at different first and second distances when the ATP tracking platform first captures and tracks a target through a radar, and a double-sensor pre-estimator performs pre-estimation angle calculation to calibrate the field of view center overlapping position between the coarse television camera and the coarse infrared camera according to the miss distance record value; based on the obtained pre-estimation angle, when the ATP tracking platform stably tracks the tracking target, the double-sensor pre-estimator performs distance pre-estimation according to the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera, and performs logical judgment and prediction filtering processing on the pre-estimation result, to obtain the real-time distance corresponding to the tracking target, so that the hardware cost is effectively reduced, and the target distance can be accurately and high-frequency refreshed.

Description

Technical Field

[0001] This application relates to the field of ATP tracking control, and more particularly to a dual-sensor prediction and ranging method and system for ATP tracking platforms. Background Technology

[0002] As modern drone technology matures and its applications become more widespread, the task of monitoring drones is becoming increasingly demanding. Among these, the ATP tracking platform is an extremely effective drone monitoring platform, capable of accurately capturing, tracking, and monitoring drones at distances ranging from hundreds of meters to tens of kilometers. For high-precision tracking systems, a two-stage tracking system is typically used, with the detection component usually consisting of a primary visible light coarse television (CTV) and coarse infrared camera (ITV), and a secondary fine television.

[0003] The focusing of a secondary precision television is usually accomplished by an external translation stage. The focusing is completed by fitting the distance information fed back by the rangefinder with the distance travel formula, so a rangefinder is usually required.

[0004] However, in addition to the increased hardware costs, conventional rangefinders have a low ranging frequency, usually 1~10Hz. When facing highly mobile targets, they are prone to revealing distance gaps, resulting in blurred target images. They also cannot be kept on for extended periods. Furthermore, high-power rangefinders pose certain safety hazards and can easily cause irreversible damage to the human eye. Moreover, their ranging accuracy is extremely dependent on the initial axis alignment and tracking precision, resulting in low generation efficiency and stringent servo requirements.

[0005] Therefore, how to effectively reduce hardware costs and accurately and frequently refresh the target distance to further improve the overall clarity of the TV still needs further research. Summary of the Invention

[0006] The main objective of this application is to provide a dual-sensor ranging method and system for ATP tracking platforms. By comparing and calculating the miss distance of coarse television and coarse infrared, the actual distance to the target is estimated, thereby reducing hardware costs and enabling accurate and high-frequency refresh of target distance.

[0007] To achieve the above objectives, this application proposes a dual-sensor ranging prediction method for an ATP tracking platform, comprising the following steps:

[0008] When the ATP tracking platform first acquires the tracked target via radar, it records the coarse television miss and coarse infrared miss at different first and second distances, respectively, to obtain a first miss record value including the first coarse television miss record value and the first coarse infrared miss record value, and a second miss record value including the second coarse television miss record value and the second coarse infrared miss record value.

[0009] The dual-sensor estimator calculates the predicted angle based on the first and second miss distance records to calibrate the overlap position of the field of view center between the coarse television camera and the coarse infrared camera.

[0010] Based on the obtained predicted angle, when the ATP tracking platform is tracking the target stably, the dual-sensor predictor performs distance prediction based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera. The predictor then performs logical judgment and predictive filtering on the prediction results to obtain the real-time distance corresponding to the tracked target.

[0011] Furthermore, the dual-sensor predictor includes a distance calculation unit and a prediction filtering unit;

[0012] The distance calculation unit is used to estimate the distance based on the real-time coarse television miss distance and the real-time coarse infrared miss distance.

[0013] The prediction filtering unit is used to perform the logical judgment and prediction filtering processing on the prediction results.

[0014] Furthermore, methods for calculating the estimated angle include:

[0015] Based on the similar triangle theorem, the current estimated angle of the ATP tracking platform is obtained according to the first distance and the corresponding first coarse television miss value and the first coarse infrared miss value, as well as the second distance and the corresponding second coarse television miss value and the second coarse infrared miss value, to determine the field of view center deviation between the coarse television camera and the coarse infrared camera.

[0016] Furthermore, methods for distance estimation include:

[0017] Based on the principle of similar triangles, calculations are performed using the focal length and pixel size of the coarse television camera, the focal length and pixel size of the coarse infrared camera, and the real-time coarse television miss distance and the real-time coarse infrared miss distance.

[0018] Furthermore, the real-time distance, the deviation from the field of view center, the real-time coarse television miss distance, the real-time coarse infrared miss distance, and the focal length and pixel size of the corresponding camera satisfy a functional relationship determined by the similar triangle theorem.

[0019] Furthermore, the method for performing logical judgments and predictive filtering on the prediction results includes:

[0020] Calculate the difference between the current estimated distance and the historical distance value at the previous time.

[0021] When the difference is greater than the first threshold, the predicted value is used as the real-time distance;

[0022] When the difference is less than the second threshold, the estimated result is used as the real-time distance;

[0023] Wherein, the first threshold is greater than or equal to the second threshold.

[0024] Furthermore, the predicted value is obtained by a first-order Kalman filter based on the target's motion state.

[0025] Furthermore, the tracking target is a drone, and the refresh frequency of the real-time distance is greater than or equal to 100Hz.

[0026] Furthermore, by using a preset acquisition time interval, the coarse television miss distance and the coarse infrared miss distance are recorded to obtain the first miss distance record value corresponding to the first distance and the second miss distance record value corresponding to the second distance.

[0027] In addition, to achieve the above objectives, this application also proposes a dual-sensor ranging system for an ATP tracking platform, comprising:

[0028] The miss distance recording module is used to record the coarse television miss distance and coarse infrared miss distance at different first and second distances when the ATP tracking platform first acquires and tracks the target via radar. The module obtains a first miss distance recording value including the first coarse television miss distance recording value and the first coarse infrared miss distance recording value, and a second miss distance recording value including the second coarse television miss distance recording value and the second coarse infrared miss distance recording value.

[0029] A dual-sensor estimator is used to calculate the estimated angle based on the first and second miss distance records to calibrate the overlap position of the field of view centers between the coarse television camera and the coarse infrared camera.

[0030] Furthermore, based on the obtained predicted angle, when the ATP tracking platform is performing stable tracking of the target, it performs distance prediction based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera, and performs logical judgment and predictive filtering on the prediction results to obtain the real-time distance corresponding to the tracked target.

[0031] This application provides a dual-sensor distance estimation method and system for an ATP tracking platform. The method calculates the actual distance to the target by comparing the miss distance of coarse television and coarse infrared dual sensors. The dual-sensor estimator estimates the actual distance to the target without the need for an additional high-power rangefinder, which significantly reduces hardware and installation costs, while effectively avoiding the safety hazards caused by rangefinders.

[0032] Because the image frame rate is much higher than the rangefinder frequency, the distance refresh rate of this invention can reach 100Hz and above, which is much higher than the 1~10Hz of conventional rangefinders. This effectively eliminates the distance gap for highly maneuverable targets and improves the imaging clarity of the precision television.

[0033] Furthermore, through predictive filtering, the continuity and accuracy of distance output can still be maintained even when image recognition fails or tracking is abnormal, reducing the stringent requirements on the initial axis accuracy and servo system. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating an embodiment of the dual-sensor ranging method for the ATP tracking platform in this application.

[0037] Figure 2 This is a schematic diagram of the line of sight and distance at the center of the field of view provided in an embodiment of the dual-sensor range estimation method for the ATP tracking platform in this application;

[0038] Figure 3 This is a schematic diagram of experimental results provided for an embodiment of the dual-sensor distance estimation method for the ATP tracking platform in this application;

[0039] Figure 4 This is a schematic diagram of a dual-sensor ranging system for the ATP tracking platform in this application.

[0040] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0041] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0042] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] This application provides a dual-sensor ranging estimation method for an ATP tracking platform, referring to... Figure 1 This includes the following steps:

[0044] Step S1: When the ATP tracking platform first acquires and tracks the target via radar, it records the coarse television miss amount and coarse infrared miss amount at different first and second distances, respectively, to obtain a first miss amount record value including the first coarse television miss amount record value and the first coarse infrared miss amount record value, and a second miss amount record value including the second coarse television miss amount record value and the second coarse infrared miss amount record value.

[0045] Step S2: The dual-sensor estimator calculates the estimated angle based on the first and second miss distance records to calibrate the overlap position of the field of view centers of the coarse television camera and the coarse infrared camera.

[0046] Step S3: Based on the calibrated field-of-view center overlap position, when the ATP tracking platform is tracking the target stably, the dual-sensor estimator estimates the distance based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera, and obtains the real-time distance corresponding to the tracked target.

[0047] Furthermore, as a preferred embodiment, the tracking target is a drone, and the refresh frequency of the real-time distance is greater than or equal to 100Hz.

[0048] By using a preset acquisition time interval, the coarse television miss distance and the coarse infrared miss distance are recorded to obtain the first miss distance record value corresponding to the first distance and the second miss distance record value corresponding to the second distance.

[0049] Specifically, firstly, in order to ensure the real-time performance of the system, when the system is powered on and first acquires a target through radar, it will record the distance at two different distances and the corresponding coarse television and coarse infrared miss distances.

[0050] Secondly, the recorded values ​​are input into the dual-sensor predictor to calculate the predicted angle for subsequent prediction use;

[0051] Finally, once the target is stably tracked, the two miss distances are input into the dual-sensor predictor in real time to obtain the final real-time distance of the tracked target.

[0052] This implementation method not only saves hardware and installation production costs, but also effectively reduces safety accidents. Since the image frequency is much higher than that of the rangefinder, reaching 100Hz and above, the distance refresh rate of this solution can also reach 100Hz and above, which is much higher than the frequency of the rangefinder. This effectively eliminates the distance gap period for highly mobile targets and improves the imaging clarity of the precision television.

[0053] Furthermore, through predictive filtering, the continuity and accuracy of distance output can be maintained even when image recognition fails or tracking is abnormal, reducing the stringent requirements on the initial axis accuracy and servo system. In existing technologies, although UAV electro-optical reconnaissance equipment integrates laser rangefinders, various factors often cause the laser ranging information to be missing or discontinuous. This invention fundamentally solves this problem by using dual-sensor miss distance fusion ranging.

[0054] Furthermore, in an optional implementation, the dual-sensor predictor includes a distance calculation unit and a prediction filtering unit;

[0055] The distance calculation unit is used to estimate the distance based on the real-time coarse television miss distance and the real-time coarse infrared miss distance.

[0056] The prediction filtering unit is used to perform the logical judgment and prediction filtering processing on the prediction results.

[0057] Furthermore, methods for calculating the estimated angle include:

[0058] Based on the similar triangle theorem, the current estimated angle of the ATP tracking platform is obtained according to the first distance and the corresponding first coarse television miss value and the first coarse infrared miss value, as well as the second distance and the corresponding second coarse television miss value and the second coarse infrared miss value, to determine the field of view center deviation between the coarse television camera and the coarse infrared camera.

[0059] Furthermore, methods for distance estimation include:

[0060] Based on the principle of similar triangles, calculations are performed using the focal length and pixel size of the coarse television camera, the focal length and pixel size of the coarse infrared camera, and the real-time coarse television miss distance and the real-time coarse infrared miss distance.

[0061] Furthermore, the real-time distance, the deviation from the field of view center, the real-time coarse television miss distance, the real-time coarse infrared miss distance, and the focal length and pixel size of the corresponding camera satisfy a functional relationship determined by the similar triangle theorem.

[0062] Specifically, during production assembly and adjustment, the coarse television camera and the coarse infrared camera are aligned at infinity. However, in reality, due to hardware and image accuracy limitations, the overlapping fields of view centers of the two cameras will not be at infinity. A schematic diagram of the line of sight and distance between their field of view centers is shown below. Figure 2 As shown, h1 and h2 are the initial radar measurement distances. and The values ​​represent the center deviations of the two corresponding fields of view, with a measurement interval of 1 second and data acquisition at any distance. hx represents the distance to be estimated, and h represents the actual focusing distance between the two fields of view.

[0063] Let the miss distance of the coarse television camera be... focal length is Like the original size The crude infrared camera's miss distance is focal length is Like the original size ,but As shown in Equation 1:

[0064]

[0065] ,Depend on Figure 2 Using the similar triangle theorem, we obtain the proportional relationship as shown in Equation 2:

[0066]

[0067] That is, the distance h from the approximately infinite origin is shown in Equation 3:

[0068]

[0069] Therefore, the target distance at any given time can be obtained from equations 2 and 3 as shown in equation 4:

[0070]

[0071] Substituting Equation 1 into Equation 4, the distance prediction result is shown in Equation 5:

[0072]

[0073] As can be seen from Equation 5, when the miss distance of the two fields of view is obtained in real time, the target distance can be obtained. Since the image frame rate can reach 100Hz and above, the refresh frequency of the obtained target distance can also reach 100Hz and above, which greatly improves the real-time performance of the target distance.

[0074] In this implementation, the dual-sensor estimator is split into a distance calculation unit and a prediction filtering unit, so that the two units work independently yet collaboratively. The distance calculation unit focuses on real-time distance calculation based on geometric optics principles, while the prediction filtering unit focuses on anomaly detection and state estimation. This modular design not only reduces the computational complexity of each part and facilitates independent optimization and debugging, but also improves the maintainability and scalability of the system. When the tracking scene changes or the filtering algorithm needs to be upgraded, only the prediction filtering part needs to be replaced without modifying the distance calculation core, which significantly improves the engineering practicality of the system.

[0075] The method uses the similar triangle theorem to calculate the predicted angle. System calibration can be completed with only two records of the miss distance at different distances. The calibration process is simple and efficient, without the need for external precision calibration equipment or complex multi-point fitting algorithms. It makes full use of the inherent deviation characteristics of the coarse TV camera and coarse infrared camera when aligning at infinity. It transforms the actual assembly and adjustment error into a quantifiable field of view center deviation, so that the subsequent real-time ranging can adaptively compensate for the inconsistency of the optical axes of the two cameras. This significantly improves the accuracy of distance prediction and the system's tolerance to assembly and adjustment errors, and reduces the stringent requirements for the accuracy of the axis alignment in the early stage. It is simple to calculate and has good real-time performance. Distance calculation can be completed with only basic multiplication and division operations, which is very suitable for the online processing requirements of embedded real-time systems.

[0076] By introducing the camera's focal length and pixel size as intrinsic parameters for distance calculation, the pixel-level miss distance is accurately converted into physical angle information, realizing an accurate mapping from image pixel coordinates to actual spatial distance. This ranging method based on camera intrinsic parameters does not rely on the target's shape or prior knowledge and has good adaptability to different types and sizes of UAV targets. At the same time, the focal length and pixel size are inherent attributes of the camera and can be used for a long time once calibrated without dynamic updates during tracking, further simplifying the complexity of real-time calculation and ensuring stable output at high refresh rates of 100Hz and above.

[0077] By solidifying the physical relationship between real-time distance and various parameters into a functional relationship determined by the similar triangle theorem, this method directly reflects the intrinsic connection between the target distance and the difference in miss distance between the two sensors: when the target distance changes, the rate of change of the deviation angle of the field of view center of the two cameras is different. By capturing this difference, the target distance can be deduced. Compared with the purely data-driven fitting method, this physical model-based ranging method has stronger extrapolation ability and generalization performance, and can still maintain high ranging accuracy even in tracking scenarios outside the calibration distance range.

[0078] Since the above implementation relies on image recognition and tracking of targets, when the image recognizes other targets or tracking fails, the calculated distance will have a large error compared with the actual distance. Therefore, it is necessary to perform logical and predictive processing on its output.

[0079] Furthermore, in an optional implementation, the method for performing logical judgment and predictive filtering on the estimated results includes:

[0080] Calculate the difference between the current estimated distance and the historical distance value at the previous time.

[0081] When the difference is greater than the first threshold, the predicted value is used as the real-time distance;

[0082] When the difference is less than the second threshold, the estimated result is used as the real-time distance;

[0083] Wherein, the first threshold is greater than or equal to the second threshold.

[0084] Furthermore, the predicted value is obtained by a first-order Kalman filter based on the target's motion state.

[0085] Specifically, after performing predictive filtering, the real-time distance to the tracked target is obtained as shown in Equation 6:

[0086]

[0087] in, This is the final output value for the real-time distance. and These are the current value (estimated result) calculated by Equation 5 and the value at the previous moment (historical distance value), respectively. For predicted values, and This is a set threshold value, which can be flexibly adjusted through actual use. The larger the value, the more dependent it is on the output value of equation 5. The larger the value, the more dependent it is on the predicted value;

[0088] This implementation method can effectively ensure the utilization rate of real data while increasing the error tolerance of the predictor. The predicted value adopts a first-order Kalman filter, which can not only effectively handle the target motion prediction problem in spatial coordinates, but also has a small amount of computation and a high cost performance.

[0089] When the target undergoes normal maneuvers that cause a significant change in distance (the difference is greater than the first threshold), the system prioritizes the output of the predicted value, effectively avoiding distance jumps caused by temporary image tracking failures or target occlusion. When the target's movement is stable and the calculated value is reliable (the difference is less than the second threshold), the system directly uses the calculated value, ensuring the real-time responsiveness of the ranging. Its hybrid output strategy effectively ensures the utilization rate of real data while significantly increasing the error tolerance of the predictor, enabling the system to maintain the continuity and smoothness of distance output even in complex tracking environments with high maneuverability and strong interference.

[0090] In the prior art, Kalman filtering has been widely used for filtering and noise reduction and trajectory prediction in target tracking. Its input observations are the azimuth and pitch angle of the target motion. After filtering, the velocity and acceleration information of the target motion can be estimated in real time, and it can effectively suppress high-frequency noise generated by image processing and turntable motion. The dual threshold logic in this embodiment further enhances the system's anomaly detection and fault tolerance capabilities.

[0091] In a preferred embodiment that incorporates all the above-described implementation methods, a simulation experiment was conducted using a UAV flight path of 100m, an altitude of 10m, a UAV speed of 20m / s, a rangefinder frequency of 1Hz, and a TETS output frequency of 100Hz as the experimental conditions. The experimental results are as follows. Figure 3 As shown in the figure, the vertical axis represents the distance from the UAV to the measurement point, and the horizontal axis represents time.

[0092] Depend on Figure 3 As can be seen, this implementation method can more realistically restore the true distance information of the target, and the accuracy of its distance information value is significantly improved compared with the rangefinder, which fully verifies its feasibility.

[0093] For highly maneuverable UAV targets, this real-time method achieves a distance refresh rate of 100Hz and above, which is much higher than the 1~10Hz of conventional rangefinders.

[0094] For a typical UAV target with a speed of 20m / s, within the rangefinder's 1Hz update interval, the target has moved 20m, which will cause the fine TV to lag in focusing and the image to be blurry. However, with the present invention, at a refresh rate of 100Hz, the target only moves 0.2m in the same time, and the fine TV can follow the target distance changes in real time to adjust the focus, always keeping the image clear.

[0095] This high-frequency real-time ranging capability effectively eliminates the distance information gap period of traditional rangefinders, significantly improving the tracking accuracy and response speed of the ATP tracking platform for highly maneuverable targets. It provides reliable distance information for subsequent precise aiming and countermeasure operations. In existing photoelectric tracking systems, visible light detection devices acquire rich target image information and have advantages in target recognition. However, a single visible light camera usually does not have target ranging capabilities and requires multi-station cross-positioning or the addition of laser ranging devices to obtain target distance. This implementation method achieves single-station passive ranging through dual-sensor fusion, breaking through this limitation.

[0096] Furthermore, this implementation utilizes the existing radar in the system to complete dual-range calibration upon initial target acquisition, eliminating the need for additional dedicated calibration equipment or manual intervention. This achieves automation and zero cost in the calibration process. The one-second time interval between the two recordings ensures that the target has sufficient displacement to produce a distinguishable difference in miss distance while avoiding the risk of target loss due to excessively long intervals. A good balance is achieved between system real-time performance and calibration accuracy. This power-on calibration design allows the system to automatically adapt to the current environmental conditions and equipment status after each startup, eliminating the degradation of calibration parameters caused by factors such as temperature drift and mechanical aging, and ensuring ranging stability and reliability during long-term operation.

[0097] This application also provides embodiments of a dual-sensor predictive ranging system for an ATP tracking platform, such as... Figure 4 As shown, including;

[0098] The miss distance recording module is used to record the coarse television miss distance and coarse infrared miss distance at different first and second distances when the ATP tracking platform first acquires and tracks the target via radar. The module obtains a first miss distance recording value including the first coarse television miss distance recording value and the first coarse infrared miss distance recording value, and a second miss distance recording value including the second coarse television miss distance recording value and the second coarse infrared miss distance recording value.

[0099] A dual-sensor estimator is used to calculate the estimated angle based on the first and second miss distance records to calibrate the overlap position of the field of view centers between the coarse television camera and the coarse infrared camera.

[0100] Furthermore, based on the obtained predicted angle, when the ATP tracking platform is performing stable tracking of the target, it performs distance prediction based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera, and performs logical judgment and predictive filtering on the prediction results to obtain the real-time distance corresponding to the tracked target.

[0101] In one alternative implementation, the dual-sensor predictor includes a distance calculation unit and a prediction filtering unit;

[0102] The distance calculation unit is used to estimate the distance based on the real-time coarse television miss distance and the real-time coarse infrared miss distance.

[0103] The prediction filtering unit is used to perform the logical judgment and prediction filtering processing on the prediction results.

[0104] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A dual-sensor ranging prediction method for an ATP tracking platform, characterized in that, Includes the following steps: When the ATP tracking platform first acquires the tracked target via radar, it records the coarse television miss and coarse infrared miss at different first and second distances, respectively, to obtain a first miss record value including the first coarse television miss record value and the first coarse infrared miss record value, and a second miss record value including the second coarse television miss record value and the second coarse infrared miss record value. The dual-sensor estimator calculates the predicted angle based on the first and second miss distance records to calibrate the overlap position of the field of view center between the coarse television camera and the coarse infrared camera. Based on the obtained predicted angle, when the ATP tracking platform is tracking the target stably, the dual-sensor predictor performs distance prediction based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera. The predictor then performs logical judgment and predictive filtering on the prediction results to obtain the real-time distance corresponding to the tracked target.

2. The dual-sensor ranging method for an ATP tracking platform as described in claim 1, characterized in that: The dual-sensor predictor includes a distance calculation unit and a prediction filtering unit; The distance calculation unit is used to estimate the distance based on the real-time coarse television miss distance and the real-time coarse infrared miss distance. The prediction filtering unit is used to perform the logical judgment and prediction filtering processing on the prediction results.

3. The dual-sensor ranging method for an ATP tracking platform as described in claim 1, characterized in that, Methods for calculating the estimated angle include: Based on the similar triangle theorem, the current estimated angle of the ATP tracking platform is obtained according to the first distance and the corresponding first coarse television miss value and the first coarse infrared miss value, as well as the second distance and the corresponding second coarse television miss value and the second coarse infrared miss value, to determine the field of view center deviation between the coarse television camera and the coarse infrared camera.

4. The dual-sensor ranging method for an ATP tracking platform as described in claim 3, characterized in that, Methods for distance estimation include: Based on the principle of similar triangles, calculations are performed using the focal length and pixel size of the coarse television camera, the focal length and pixel size of the coarse infrared camera, and the real-time coarse television miss distance and the real-time coarse infrared miss distance.

5. The dual-sensor ranging method for an ATP tracking platform as described in claim 4, characterized in that: The real-time distance, the deviation from the field of view center, the real-time coarse television miss distance, the real-time coarse infrared miss distance, and the corresponding camera focal length and pixel size satisfy a functional relationship determined by the similar triangle theorem.

6. The dual-sensor ranging method for an ATP tracking platform as described in claim 1, characterized in that, Methods for performing logical judgments and predictive filtering on the estimated results include: Calculate the difference between the current estimated distance and the historical distance value at the previous time. When the difference is greater than the first threshold, the predicted value is used as the real-time distance; When the difference is less than the second threshold, the estimated result is used as the real-time distance; Wherein, the first threshold is greater than or equal to the second threshold.

7. The dual-sensor ranging method for an ATP tracking platform as described in claim 6, characterized in that: The predicted value is obtained by a first-order Kalman filter based on the target's motion state.

8. The dual-sensor ranging method for an ATP tracking platform as described in claim 1, characterized in that: The tracking target is a drone, and the real-time distance refresh frequency is greater than or equal to 100Hz.

9. The dual-sensor ranging method for an ATP tracking platform as described in claim 1, characterized in that: By using a preset acquisition time interval, the coarse television miss distance and the coarse infrared miss distance are recorded to obtain the first miss distance record value corresponding to the first distance and the second miss distance record value corresponding to the second distance.

10. A dual-sensor ranging system for an ATP tracking platform, characterized in that, include; The miss distance recording module is used to record the coarse television miss distance and coarse infrared miss distance at different first and second distances when the ATP tracking platform first acquires and tracks the target via radar. The module obtains a first miss distance recording value including the first coarse television miss distance recording value and the first coarse infrared miss distance recording value, and a second miss distance recording value including the second coarse television miss distance recording value and the second coarse infrared miss distance recording value. A dual-sensor estimator is used to calculate the estimated angle based on the first and second miss distance records to calibrate the overlap position of the field of view centers between the coarse television camera and the coarse infrared camera. Furthermore, based on the obtained predicted angle, when the ATP tracking platform is performing stable tracking of the target, it performs distance prediction based on the real-time coarse television miss distance obtained by the coarse television camera and the real-time coarse infrared miss distance obtained by the coarse infrared camera, and performs logical judgment and predictive filtering on the prediction results to obtain the real-time distance corresponding to the tracked target.

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