A method for detecting indirect aiming points

Abstract

The present invention discloses a method for detecting the impact point of indirect fire, comprising the following steps: step one, instrument arrangement; step two, impact point collection; step three, data call; step four, model generation; and step five, impact point detection. In the above step one, an acquisition terminal is arranged in a predetermined area of ​​an indirect fire target point, and according to the accuracy requirements of the indirect fire impact point detection, a number of sensing points are selected in the sensing area of ​​the acquisition terminal with corresponding spacing, and then a semiconductor pressure sensor, a locator and a wireless communicator are installed on each sensing point, and are numbered and recorded in sequence. The invention utilizes Fisher discriminant classification algorithm, Lasso regression algorithm, classification regression decision tree algorithm and big data technology, and constructs an indirect fire weapon artillery shell impact point detection model through evaluation index and cost complexity pruning method. After the instrument is arranged, no human operation is required, the instrument has a high degree of intelligence, fast detection efficiency, high accuracy and low labor cost.

Description

Technical Field

[0001] The present invention relates to the technical field of indirect aiming point detection, and in particular to an indirect aiming point detection method. Background Art

[0002] Indirect fire weapons are characterized by curved trajectories and long range. Using this method, one can fire without seeing the target, simply knowing its direction and position. They can traverse terrain obstacles such as hills, and their parabolic trajectory allows for a long range, making them suitable for striking distant targets. Currently, indirect fire weapons are commonly used in military combat simulations. By measuring the impact point error of indirect fire weapon shells, operators can improve their proficiency in indirect fire weapons.

[0003] However, conventional indirect impact point detection methods have a low level of intelligence. Personnel operate instruments to detect the impact point errors of indirect impact weapon shells, resulting in low detection efficiency, poor accuracy, and high labor costs. In addition, the detection instruments are difficult to deploy and require planning of location and size, which is time-consuming and labor-intensive, with high deployment costs. At the same time, only the GPS positioning system is used, and the transmission of satellite communication signals is greatly affected by the atmosphere, satellite ephemeris, and satellite species differences. The positioning accuracy is low and the error is large, which reduces the accuracy of indirect impact point detection. Summary of the Invention

[0004] The object of the present invention is to provide a method for detecting indirect aiming points to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for detecting indirect aiming points, comprising the following steps: step 1, instrument arrangement; step 2, point collection; step 3, data call; step 4, model generation; step 5, point detection;

[0006] In step 1 above, the acquisition terminal is placed in the predetermined area of ​​the indirect aiming target point, and according to the accuracy requirements of the indirect aiming point detection, a number of sensing points are selected at corresponding intervals in the sensing area of ​​the acquisition terminal. Then, the semiconductor pressure sensor, locator and wireless communicator are installed at each sensing point and recorded in sequence.

[0007] In step 2 above, the indirect weapon fires at the target point, and the shells bombard the sensing area of ​​the acquisition terminal, forming a spherical shock wave at several sensing points. The semiconductor pressure sensor then collects the impact potential energy of the indirect weapon fire at each sensing point, obtains the impact potential energy peak, and transmits the position, trigger time, and point number of each sensing point to the 3D detection gimbal via a locator and wireless communicator.

[0008] In step 3 above, a large amount of projectile characteristic data is imported from the indirect fire weapon projectile database and a large amount of impact point shock wave field characteristic data is imported from the crater shock wave field database through the data call module. The projectile characteristic data and impact point shock wave field characteristic data are then used as the original data of the model and divided into training set data and test set data.

[0009] In the above step 4, the training set data is constructed into an indirect fire point analysis model through the model construction module using the Fisher discriminant classification algorithm, the Lasso regression algorithm and the classification regression decision tree algorithm. The test set data is then input into the indirect fire point analysis model through the model optimization module. The evaluation index is used to evaluate the quality of the indirect fire point analysis model for the intermediate fire weapon shell characteristic data and the impact wave field characteristic data. The cost complexity pruning method is used to select the indirect fire weapon shell detection model.

[0010] In the above step five, the three-dimensional detection gimbal inputs the point position, trigger time and point number of each sensing point into the indirect fire weapon artillery shell landing point detection model through the landing point detection module, calculates the wave velocity of the spherical shock wave based on the point information and trigger time, and obtains the firepower level of the indirect fire weapon artillery shell, and then calculates the range and center of the spherical shock wave based on the size of the impact potential energy peak of each sensing point and the order of the point number, obtains the indirect fire weapon damage efficiency and the indirect fire crater position, and then calculates the distance between the indirect fire crater position and the indirect fire target point to obtain the indirect fire weapon artillery shell landing point error.

[0011] Preferably, in step 1, the semiconductor pressure sensor is a miniature high-precision semiconductor pressure sensor.

[0012] Preferably, in step 1, the locator is a GPS locator or RTK locator with centimeter-level accuracy.

[0013] Preferably, in step 1, the semiconductor pressure sensor, positioner and wireless communicator installed at the same sensing point all use the same number.

[0014] Preferably, in step 2, the indirect fire weapon is an indirect fire weapon used for military combat simulation training.

[0015] Preferably, in step three, the training set data and the test set data account for 70% and 30% of the original data respectively.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the indirect fire impact point detection method utilizes Fisher discriminant classification algorithm, Lasso regression algorithm, classification regression decision tree algorithm and big data technology, and through evaluation index and cost complexity pruning method, constructs an indirect fire weapon artillery shell impact point detection model; after the instrument is arranged, no human operation is required, the degree of intelligence is high, the detection efficiency is fast, the accuracy is high, and the labor cost is low; the detection instrument is convenient to arrange and can be arranged arbitrarily, without the need for size planning, saving time and effort, and low arrangement cost; RTK positioning is added on the basis of GPS positioning, and the carrier of the satellite communication signal is phase-differentially processed, the positioning accuracy is high, the error is small, and the accuracy of indirect fire impact point detection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system structure diagram of the present invention;

[0018] Figure 2 This is a structural diagram of the arrangement of sensing points in the present invention;

[0019] Figure 3 is a system flow chart of the present invention;

[0020] Figure 4 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1-4 An embodiment of the present invention provides a method for detecting an indirect aiming point, comprising the following steps: step 1, instrument arrangement; step 2, point collection; step 3, data call; step 4, model generation; step 5, point detection;

[0023] In step 1 above, the acquisition terminal is placed in the predetermined area of ​​the indirect aiming target point, and according to the accuracy requirements of the indirect aiming point detection, a number of sensing points are selected in the sensing area of ​​the acquisition terminal at corresponding intervals. Then, a miniature high-precision semiconductor pressure sensor, a centimeter-level precision GPS locator and RTK locator, and a wireless communicator are installed at each sensing point and numbered in sequence. The semiconductor pressure sensor, locator, and wireless communicator installed at the same sensing point all use the same number.

[0024] In the above step 2, the military combat simulation training uses indirect fire weapons to fire at the target point. The shells bombard the sensing area of ​​the acquisition terminal, forming a spherical shock wave at several sensing points. The semiconductor pressure sensor then collects the impact potential energy of the indirect fire weapon at each sensing point, obtains the impact potential energy peak, and transmits the position, trigger time and point number of each sensing point to the three-dimensional detection pan-tilt platform through the locator and wireless communicator.

[0025] In step 3 above, a large amount of projectile characteristic data is imported from the indirect fire weapon projectile database and a large amount of impact point shock wave field characteristic data is imported from the crater shock wave field database through the data call module. The projectile characteristic data and impact point shock wave field characteristic data are then used as the original data of the model and divided into training set data and test set data, with the training set data and test set data accounting for 70% and 30% of the original data respectively.

[0026] In the above step 4, the training set data is constructed into an indirect fire point analysis model through the model construction module using the Fisher discriminant classification algorithm, the Lasso regression algorithm and the classification regression decision tree algorithm. The test set data is then input into the indirect fire point analysis model through the model optimization module. The evaluation index is used to evaluate the quality of the indirect fire point analysis model for the intermediate fire weapon shell characteristic data and the impact wave field characteristic data. The cost complexity pruning method is used to select the indirect fire weapon shell detection model.

[0027] In the above step five, the three-dimensional detection gimbal inputs the point position, trigger time and point number of each sensing point into the indirect fire weapon artillery shell landing point detection model through the landing point detection module, calculates the wave velocity of the spherical shock wave based on the point information and trigger time, and obtains the firepower level of the indirect fire weapon artillery shell, and then calculates the range and center of the spherical shock wave based on the size of the impact potential energy peak of each sensing point and the order of the point number, obtains the indirect fire weapon damage efficiency and the indirect fire crater position, and then calculates the distance between the indirect fire crater position and the indirect fire target point to obtain the indirect fire weapon artillery shell landing point error.

[0028] Based on the above, the advantages of the present invention are that, by utilizing Fisher discriminant classification algorithm, Lasso regression algorithm, classification regression decision tree algorithm and big data technology, and through evaluation index and cost complexity pruning method, a model for detecting the impact point of indirect weapon shells is constructed. After the instrument is arranged, no human operation is required, the degree of intelligence is high, the detection efficiency is fast, the accuracy is high, the labor cost is low, and the detection instrument is convenient to arrange and can be arranged arbitrarily without planning the size, which saves time and effort and has low arrangement cost. At the same time, RTK positioning is added on the basis of GPS positioning, and the carrier of the satellite communication signal is phase-differentially processed, so the positioning accuracy is high and the error is small, thereby improving the accuracy of indirect impact point detection.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for detecting an indirect aiming point, comprising the following steps: Step 1: Instrument layout; Step 2: Landing point collection; Step 3: Data call; Step 4: Model generation; Step 5: Landing point detection; It is characterized by: In step 1 above, the acquisition terminal is placed in the predetermined area of ​​the indirect aiming target point, and according to the accuracy requirements of the indirect aiming point detection, a number of sensing points are selected at corresponding intervals in the sensing area of ​​the acquisition terminal. Then, the semiconductor pressure sensor, locator and wireless communicator are installed at each sensing point and recorded in sequence. In step 2 above, the indirect weapon fires at the target point, and the shells bombard the sensing area of ​​the acquisition terminal, forming a spherical shock wave at several sensing points. The semiconductor pressure sensor then collects the impact potential energy of the indirect weapon fire at each sensing point, obtains the impact potential energy peak, and transmits the position, trigger time, and point number of each sensing point to the 3D detection gimbal via a locator and wireless communicator. In step 3 above, a large amount of projectile characteristic data is imported from the indirect fire weapon projectile database and a large amount of impact point shock wave field characteristic data is imported from the crater shock wave field database through the data call module. The projectile characteristic data and impact point shock wave field characteristic data are then used as the original data of the model and divided into training set data and test set data. In the above step 4, the training set data is constructed into an indirect fire point analysis model through the model construction module using the Fisher discriminant classification algorithm, the Lasso regression algorithm and the classification regression decision tree algorithm. The test set data is then input into the indirect fire point analysis model through the model optimization module. The evaluation index is used to evaluate the quality of the indirect fire point analysis model for the intermediate fire weapon shell characteristic data and the impact wave field characteristic data. The cost complexity pruning method is used to select the indirect fire weapon shell detection model. In the above step five, the three-dimensional detection gimbal inputs the point position, trigger time and point number of each sensing point into the indirect fire weapon artillery shell landing point detection model through the landing point detection module, calculates the wave velocity of the spherical shock wave based on the point information and trigger time, and obtains the firepower level of the indirect fire weapon artillery shell, and then calculates the range and center of the spherical shock wave based on the size of the impact potential energy peak of each sensing point and the order of the point number, obtains the indirect fire weapon damage efficiency and the indirect fire crater position, and then calculates the distance between the indirect fire crater position and the indirect fire target point to obtain the indirect fire weapon artillery shell landing point error.

2. The indirect aiming point detection method according to claim 1, characterized in that: In the step 1, the semiconductor pressure sensor is a miniature high-precision semiconductor pressure sensor.

3. The indirect aiming point detection method according to claim 1, characterized in that: In the step 1, the locator is a GPS locator or RTK locator with centimeter-level accuracy.

4. The indirect aiming point detection method according to claim 1, characterized in that: In the step 1, the semiconductor pressure sensor, positioner and wireless communicator installed at the same sensing point all use the same number.

5. The indirect aiming point detection method according to claim 1, characterized in that: In the step 2, the indirect fire weapon is an indirect fire weapon used for military combat simulation training.

6. The indirect aiming point detection method according to claim 1, characterized in that: In step 3, the training set data and the test set data account for 70% and 30% of the original data respectively.

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