A comprehensive control method for frequency-frequency training in a complex weapon system

Abstract

The present invention discloses a comprehensive control method for medium-frequency training of a complex weapon system, namely, when a close-in defense weapon system is in a medium-frequency training working mode, a medium-frequency simulator generates target data and sends it to a fire control device, which receives and processes the target data and sends target indication data to a tracking sensor, and receives target tracking data from the tracking sensor, performs various element calculations and various element extrapolations, performs filtering estimation and predicts gun deviation, corrects the various element extrapolation data in real time, obtains a gun servo position prediction value, and the medium-frequency simulator uses the gun servo position prediction data to perform coordinate transformation on the target data, drives the tracking sensor to complete target tracking, and realizes the medium-frequency training function of the system; the present invention solves the problem that a close-in defense weapon system cannot perform medium-frequency training because the gun servo position information data rate does not meet the requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing equipment, and in particular relates to a comprehensive control method for frequency-frequency training of a complex weapon system. Background Art

[0002] Medium frequency training is a key training method for close-in defense weapon systems, enabling training of the weapon system's target designation, tracking, fire control calculations, and artillery operation processes. In this training method, the medium frequency simulator simulates and generates target data, transforming the target data's coordinates using artillery servo mount information and generating medium frequency simulation signals. The fire control equipment receives and processes the target data from the medium frequency simulator, issuing target designation data to the tracking sensor, directing it to track the medium frequency simulated target. The tracking data is then received and controlled to control the artillery servo operation, completing the medium frequency training of the weapon system.

[0003] Medium-frequency training requires high data rates for artillery servo-mounting, often several times higher than those of other system equipment. In a two-in-one integrated system, the tracking sensor is mounted on the gun carriage, and the medium-frequency simulator needs to acquire this data in real time to transform the carriage's coordinates. To ensure the required data rate, a position acquisition device is typically installed at the base of the tracking sensor. This device collects this information in real time during the medium-frequency simulator's operating cycle. This processing method offers the advantages of high real-time performance and low latency, but it requires disassembly of the tracking sensor and installation of a position reading device. It also requires revalidation of multiple indicators that could affect system performance. Therefore, this modification is labor-intensive and unsuitable for large-scale deployment. For artillery systems that lack the necessary modification options, the medium-frequency simulator can only acquire lower-data-rate information from the weapon system network and use it to transform the carriage's coordinates. However, the low data rate and inaccurate and untimely coordinate transformations can easily lead to unstable tracking, gun jitter, and inaccurate accuracy. Summary of the Invention

[0004] In view of this, an object of the present invention is to provide a comprehensive control method for medium-frequency training of a complex weapon system, which can save time and economic costs.

[0005] A comprehensive control method for frequency-frequency training of a complex weapon system, comprising:

[0006] Step 1: The weapon system is in the medium frequency training mode. The medium frequency simulator generates target data and sends it to the fire control device. The fire control device sends target indication data to the tracking sensor and receives its target tracking data to obtain the data γ. G (k),φ G (k), extrapolated data of various variables Among them, time k is any time after the filtering is stable, and the filtering start time is time 0;

[0007] Step 2: Use the data γ at time k G (k) and gun servo position data γ Z (k), calculate the gun deviation data d k (k) = γ Z (k)-γ G (k);

[0008] Step 3: For the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun n is the filter window length;

[0009] Step 4: Use the predicted value of the gun deviation Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position

[0010] Step 5: The fire control equipment sends the gun servo position prediction value to the medium frequency simulator The intermediate frequency simulator completes the coordinate transformation and drives the tracking sensor to track the target.

[0011] Preferably, in step 1, the weapon system is in the medium frequency training mode, the medium frequency simulator generates target data and sends it to the fire control device, the fire control device sends target indication data to the tracking sensor, and receives its target tracking data to obtain the artillery position data γ G (k) and high and low data and the extrapolated values of the artillery position parameters and extrapolated values of high and low parameters Extrapolated data The formula is as follows:

[0012]

[0013]

[0014] in, and γ G (k) and The corresponding feedforward control quantity. i=1,…,N, is the number of artillery servo position prediction cycles, T s is the weapon system network communication cycle, and △T is the intermediate frequency simulator operation cycle.

[0015] Preferably, in step 3, the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun The formula is as follows:

[0016]

[0017] Preferably, in step 4, the deviation prediction value with gun is used Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position The formula is as follows:

[0018]

[0019] Preferably, the fire control equipment calculates multiple sets of artillery servo position prediction data simultaneously.

[0020] The present invention has the following beneficial effects:

[0021] (1) A comprehensive control method for medium frequency training of a complex weapon system can partially and effectively overcome the problem that the weapon system cannot perform medium frequency training normally due to insufficient data rate of the artillery servo position; (2) The system error of the artillery operation is obtained by filtering, and the error is eliminated by using a compensation correction method. The accuracy of the artillery servo position prediction value is better, and the medium frequency training process of the weapon system is more coordinated. (3) Since there is no need to install an additional artillery servo position acquisition device, but relying on the existing components of the weapon system, the problem of medium frequency training is solved by data processing methods, saving time and economic costs; (4) It has a wide range of applications and can be extended to artillery systems with similar configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the system configuration of a comprehensive control method for medium-frequency training in a complex weapon system of the present invention.

[0023] Figure 2 This is a comparison curve of azimuth error under a 300 m / s route in an embodiment of the present invention.

[0024] Figure 3This is a comparison curve of azimuth angle errors under a 500 m / s route in an embodiment of the present invention.

[0025] Figure 4 This is a system flow chart of a comprehensive control method for medium-frequency training in a complex weapon system of the present invention. DETAILED DESCRIPTION

[0026] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0027] The present invention provides a comprehensive control method for medium-frequency training of a complex weapon system, that is, when a close-in defense weapon system is in a medium-frequency training working mode, a medium-frequency simulator generates target data and sends it to a fire control device. The fire control device receives and processes the target data and sends target indication data to a tracking sensor, and receives target tracking data from the tracking sensor, performs various element calculations and various element extrapolations, filters and estimates and predicts gun deviations, corrects the various element extrapolation data in real time, obtains a gun servo position prediction value, and the medium-frequency simulator uses the gun servo position prediction data to perform coordinate transformation on the target data, drives the tracking sensor to complete target tracking, and realizes the medium-frequency training function of the system.

[0028] Step 1: The weapon system is in the medium frequency training mode. The medium frequency simulator generates target data and sends it to the fire control device. The fire control device sends target indication data to the tracking sensor and receives its target tracking data to obtain the data γ. G (k),φ G (k), extrapolated data of various variables The k moment is any moment after the filtering is stable, and the filtering start time is the 0 moment.

[0029] Step 2: Use the data γ at time k G (k) and gun servo position data γ Z (k), calculate the gun deviation data d k (k) = γ Z (k)-γ G (k);

[0030] Step 3: For the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun n is the filter window length;

[0031] Step 4: Use the predicted value of the gun deviation Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position

[0032] Step 5: The fire control equipment sends the gun servo position prediction value to the medium frequency simulator The intermediate frequency simulator completes the coordinate transformation and drives the tracking sensor to track the target.

[0033] In step 1, the weapon system is in the medium frequency training mode. The medium frequency simulator generates target data and sends it to the fire control device. The fire control device sends target indication data to the tracking sensor and receives its target tracking data to obtain the artillery position data γ G (k) and high and low data and the extrapolated values of the artillery position parameters and extrapolated values of high and low parameters Extrapolated data The formula is as follows:

[0034]

[0035]

[0036] in, and γ G (k) and The corresponding feedforward control quantity. i=1,…,N, is the number of artillery servo position prediction cycles, T s is the weapon system network communication cycle, and △T is the intermediate frequency simulator operation cycle.

[0037] In step 3, the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun The formula is as follows:

[0038]

[0039] The deviation sequence contains the systematic error and random error of the gun operation. The systematic error is obtained through filtering processing, and its extrapolation can obtain the predicted value of the gun deviation, that is, the predicted systematic error. Through subsequent compensation and correction processing, the predicted systematic error is eliminated and the prediction accuracy of the gun servo position is improved.

[0040] In step 4, use the predicted value with gun deviation Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position The formula is as follows:

[0041]

[0042] The present invention will be described in further detail below in conjunction with examples:

[0043] like Figure 1 As shown, the two-in-one integrated weapon system is mainly equipped with tracking sensors, fire control equipment, artillery and platform attitude equipment. The tracking sensor is installed on the artillery bracket, and the medium frequency simulator is a supporting equipment for the tracking sensor, which mainly completes the medium frequency drive of the tracking sensor. The fire control equipment is the fire control center of the weapon system. It is responsible for operating the tracking sensor, receiving the tracking data and platform attitude information of the tracking sensor, solving the shooting parameters in real time, and controlling the artillery to aim and shoot. The integrated weapon system conducts medium frequency training to simulate the target route, and the target speeds are 300m / s and 500m / s respectively. The original method and the patented method are used for medium frequency training respectively, and their system errors are counted. The original method refers to the method in which the medium frequency simulator obtains the artillery servo position through the weapon system network to drive the tracking sensor to carry out medium frequency training. The comparison results are shown in Figure 2 、 Figure 3 , by the attached Figure 2 、 3 It can be seen that the method of the present invention can significantly improve the system accuracy.

[0044] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A comprehensive control method for medium-frequency training of a complex weapon system, characterized in that: include: Step 1: The weapon system is in the medium frequency training mode. The medium frequency simulator generates target data and sends it to the fire control device. The fire control device sends target indication data to the tracking sensor and receives its target tracking data to obtain the data γ. G (k),φ G (k), extrapolated data of various variables Among them, time k is any time after the filtering is stable, and the filtering start time is time 0; Among them, the weapon system is in the medium frequency training mode, the medium frequency simulator generates target data and sends it to the fire control equipment, the fire control equipment sends target indication data to the tracking sensor, and receives its target tracking data to obtain the artillery azimuth data γ G (k) and high and low data and the extrapolated values of the artillery position parameters and extrapolated values of high and low parameters Extrapolated data The formula is as follows: in, and γ G (k) and The corresponding feedforward control quantity; i = 1, ..., N, is the number of artillery servo position prediction cycles, T s is the weapon system network communication cycle, △T is the intermediate frequency simulator operation cycle; Step 2, use the data γ at time k G (k) and gun servo position data γ Z (k), calculate the gun deviation data d k (k) = γ Z (k)-γ G (k); Step 3: For the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun n is the filter window length; Among them, for the deviation sequence {d k-n (kn),d k-n+1 (k-n+1),d k-n+2 (k-n+2),…d k (k)}(k≥n) to filter and obtain the estimated deviation of the gun and the rate of change of deviation Extrapolate the deviation estimation data to obtain the deviation prediction value with gun The formula is as follows: Step 4: Use the predicted value of the gun deviation Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position Among them, the predicted value with gun deviation is used Extrapolate data for various variables Perform correction compensation to obtain the predicted value of the gun servo position The formula is as follows: Step 5: The fire control equipment sends the gun servo position prediction value to the medium frequency simulator The intermediate frequency simulator completes the coordinate transformation and drives the tracking sensor to track the target.

2. The integrated control method for medium-frequency training of a complex weapon system according to claim 1, characterized in that: The fire control equipment simultaneously calculates multiple sets of artillery servo position prediction data.

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