A method for detecting and handling abnormal values ​​of leveling current in an inertial platform system

Abstract

This application relates to the field of inertial detection, specifically disclosing a method for detecting and processing anomalies in the leveling current of an inertial platform system. The method includes: obtaining the recursive average value of the leveling current of the inertial platform system; determining the absolute value of the k-th difference based on the k-th fluctuation peak and the recursive average value; if the absolute value of the k-th difference is greater than or equal to a preset threshold, removing integer-cycle data between the peak points before and after the k-th fluctuation peak; if the absolute value of the k-th difference is less than the preset threshold, retaining the data between the k-th fluctuation peak and the previous peak; repeating the above steps until the leveling current terminates; and selectively retaining valid data as calculation data based on the value of k. The solution provided by this application can improve the sensitivity to external disturbances. By using the calculation data after anomaly detection processing, the average value calculation result of the leveling current is more accurate, thereby making the self-alignment and self-calibration of the inertial platform system more accurate.

Description

Technical Field

[0001] This application relates to the technical field of inertial detection, and in particular to a method for detecting and processing abnormal values ​​of leveling current in an inertial platform system. Background Technology

[0002] The platform system leveling loop is used to stabilize the platform body in the geographic coordinate system, that is, two of its axes are kept at geographic level (or one axis is kept at geographic level and the other axis is at a fixed angle to geographic level). It is part of the initial alignment system of the platform system before firing and is also a key link for the inertial platform system to achieve self-aiming and self-calibration under the current technology.

[0003] When performing auto-aiming and auto-calibration on an inertial platform system, the measured value of the leveling current is needed to calculate the auto-aiming and auto-calibration results. Therefore, the proper processing and utilization of the leveling current measurement data directly affects the accuracy of the inertial platform system. Due to the harsh working environment on the missile and the large amount of random interference, the comprehensive interference is difficult to express using mathematical models, making the processing of leveling current data very difficult.

[0004] Dynamic signal data is generally divided into two main categories: deterministic data and random data. Deterministic data can be further divided into periodic data and aperiodic data. Periodic data is divided into simple harmonic periodic data and complex periodic data. Aperiodic data is divided into quasi-periodic data and transient data. Random data can be divided into stationary random processes and non-stationary random processes. Stationary random processes can be divided into ergodic processes and non-ergodic processes. Non-stationary random processes can be divided into general non-stationary random processes and transient random processes. The data of the leveling current is affected by the platform's own circuit and the external environment, and is a superposition of multiple forms of data.

[0005] Currently, the processing of leveling current typically involves averaging the data from the stable segment. This method is simple and efficient, and generally yields the required accuracy. However, it is insensitive to disturbances and cannot eliminate the influence of external disturbances. Therefore, the leveling current results may be inaccurate, leading to inaccurate auto-alignment and auto-calibration of the inertial platform system. Summary of the Invention

[0006] This application provides a method for detecting and processing abnormal values ​​of the leveling current in an inertial platform system. The purpose is to improve the sensitivity to external disturbances, make the leveling current results more accurate, and thus make the auto-aiming and auto-calibration of the inertial platform system more accurate.

[0007] Firstly, a method for detecting and processing abnormal values ​​of leveling current in an inertial platform system is provided, including:

[0008] Step 1: Obtain the recursive average value and fluctuation peak value of the leveling current of the inertial platform system;

[0009] Step 2: Determine the absolute value of the k-th difference based on the k-th fluctuation peak and the recursive average value, where the absolute value of the k-th difference is the absolute value of the difference between the k-th fluctuation peak and the recursive average value;

[0010] Step 3a: If the absolute value of the kth difference is greater than or equal to a preset threshold, then remove the leveling current data between the peak points before and after the kth fluctuation peak.

[0011] Step 3b: If the absolute value of the kth difference is less than the preset threshold, then retain the leveling current between the kth fluctuation peak value and the (k-1)th fluctuation peak value;

[0012] Step 4: Let k = k + 1, and repeat step 1 to adjust the termination time;

[0013] Step 5: If k is even, take the valid data between the first peak and the (k-1)th peak as the calculation data; if k is odd, take the valid data between the first peak and the kth peak as the calculation data.

[0014] Compared with the prior art, the solution provided in this application has at least the following beneficial technical effects: The method for detecting and processing abnormal values ​​of the leveling current in an inertial platform system proposed in this invention can effectively detect abnormal leveling currents and greatly improve the quality of data processing.

[0015] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes step 6: performing self-aiming or self-calibration result calculation of the inertial platform system based on the processed leveling current data.

[0016] This allows for relatively precise auto-aiming and / or auto-calibration of the inertial platform system.

[0017] In conjunction with the first aspect, in certain implementations of the first aspect, the self-aiming of the inertial platform system includes:

[0018] Obtain the azimuth angle α for self-aiming, where the azimuth angle α satisfies:

[0019]

[0020] I xi Let θ be the average current for leveling at the i-th position, and let θ be the azimuth angle between the three positions.

[0021] In conjunction with the first aspect, in some implementations of the first aspect, the recursive average value is the average value of the effective leveling current between the (k-1)th fluctuation peak value and the first fluctuation peak value before the kth fluctuation peak value.

[0022] It can retain or remove data between fluctuation peaks in real time based on existing leveling current data.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, the effective balancing current is the balancing current retained after removing abnormal data between the first fluctuation peak and the (k-1)th fluctuation peak.

[0024] When determining whether to retain or remove peak fluctuations, interference from disturbance signals in existing leveling current data can be reduced.

[0025] In conjunction with the first aspect, in some implementations of the first aspect, the preset threshold is twice the average of the m absolute values ​​of the differences preceding the absolute value of the k-th difference, where m <k。

[0026] It can retain or remove data between fluctuation peaks in real time based on existing leveling current data.

[0027] In conjunction with the first aspect, in some implementations of the first aspect, m = k-1.

[0028] As much as possible, existing global leveling current data can be considered to retain or remove data between fluctuation peaks.

[0029] In conjunction with the first aspect, in some implementations of the first aspect, the s-th fluctuation peak among the k-1 fluctuation peaks before the k-th fluctuation peak is removed, and the absolute value of the s-th difference corresponding to the s-th fluctuation peak is equal to the average of the absolute values ​​of the s-1 differences before the s-th fluctuation peak.

[0030] When determining whether to retain or remove peak fluctuations, interference from disturbance signals in existing leveling current data can be reduced.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, when step 2 is executed for the first time, k = 3 to 8.

[0032] When performing the method provided in this application for the first time, a suitable number of fluctuation peaks can be selected, which helps to reduce the introduction of disturbance signals during the first execution step or reduce the impact of disturbance signals on the accuracy of result determination.

[0033] In a second aspect, an electronic device is provided, characterized in that the electronic device is used to perform the method described in any of the implementations of the first aspect above. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart illustrating a method for detecting and processing abnormal values ​​of leveling current in adjusting the accuracy of an inertial platform system, provided in an embodiment of this application.

[0035] Figure 2This is a schematic flowchart illustrating a method for detecting and processing abnormal values ​​of leveling current in an inertial platform system, provided in an embodiment of this application.

[0036] Figure 3 The curve for detecting and processing abnormal values ​​in the leveling current data is shown in Experiment 1 (the horizontal axis represents time in seconds, and the vertical axis represents the number of leveling current pulses in LSB).

[0037] Figure 4 The graph shows the abnormal value detection and processing curves for the leveling current data in Experiment 2 (the horizontal axis represents time in seconds, and the vertical axis represents the number of leveling current pulses in LSB). Detailed Implementation

[0038] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0039] Figure 1 This is a schematic flowchart illustrating a method for detecting and processing abnormal values ​​of leveling current in adjusting the accuracy of an inertial platform system, provided in an embodiment of this application.

[0040] 110, obtain the average peak value of the leveling current of the inertial platform system.

[0041] In some embodiments, the average peak fluctuation can be an empirical preset value.

[0042] In other embodiments, the average peak fluctuation can be obtained from the (k-1) peak fluctuations preceding the k-th peak fluctuation. For example, the average peak fluctuation of the leveling current can be obtained by averaging the (k-1) peak fluctuations. Alternatively, the average peak fluctuation of the leveling current can be obtained by averaging the peak fluctuations retained from the (k-1) peak fluctuations.

[0043] 120. Based on the kth fluctuation peak and the average fluctuation peak, determine the absolute value of the kth difference. The absolute value of the kth difference can be the absolute value of the difference between the kth fluctuation peak and the average fluctuation peak.

[0044] 130a, if the absolute value of the kth difference is greater than or equal to the preset threshold, then remove the leveling current between the peak values ​​before and after the kth fluctuation peak.

[0045] 130b, if the absolute value of the kth difference is less than the preset threshold, then the kth fluctuation peak value is retained.

[0046] In some embodiments, the preset threshold can change continuously with the leveling current test. In one possible scenario, for the absolute value of the k-th difference, the preset threshold can be twice the average of the m absolute values of the differences before the absolute value of the k-th difference, where m < k. For example, if m = k - 1, the preset threshold can be the average of the k - 1 absolute values of the differences before the absolute value of the k-th difference. In one embodiment, among the k - 1 fluctuation peaks before the k-th fluctuation peak, there are cases where some are removed. Suppose the removed fluctuation peak is the s-th fluctuation peak, then the absolute value of the s-th difference can be equal to the average of the s - 1 absolute values of the differences before the s-th fluctuation peak.

[0047] 140. Let k = k + 1, and re - execute 110 until the leveling termination time.

[0048] 150a. If k is even, take the valid data between the first peak and the (k - 1)-th peak as the calculation data.

[0049] 150b. If k is odd, take the valid data between the first peak and the k-th peak as the calculation data.

[0050] 160. According to the processed leveling current data, perform self - aiming and / or self - calibration of the inertial platform system.

[0051] When the inertial platform system performs self - aiming and self - calibration, the measured value of the leveling current needs to be used to calculate the self - aiming result and the self - calibration result. Taking the three - position self - aiming as an example, the azimuth angle calculation formula is as follows:

[0052]

[0053] where, I xi is the average value of the leveling current at the i-th position, θ is the rotation azimuth angle of the three - position, is a known quantity, here it is 120°, and α is the azimuth angle to be calculated. From formula (1), it can be seen that the azimuth angle is calculated based on the average values of the leveling currents at three positions, and the calculation accuracy of the azimuth angle is directly related to the accuracy of the leveling current. Reasonable processing and utilization of the leveling current measurement data directly relate to the usage accuracy of the inertial platform system.

[0054] The embodiment of the present application also provides an electronic device for executing the method as Figure 1 shown.

[0055] The present application will be further described in detail in combination with the following content.

[0056] Embodiment 1

[0057] Figure 2 It is a flowchart of a method for detecting and processing abnormal values of the leveling current of an inertial platform system.

[0058] Step 1: Calculate the recursive average value of the leveling current data from the first peak point to the fourth peak point, and denote it as E4.

[0059] Specifically, starting from the first fluctuation peak of the collected leveling current data, the recursive average value of the leveling current data between the first peak point (a1) and the fourth peak point (a4) is calculated and denoted as E4.

[0060] Step 2: Calculate the average of the absolute values ​​of the differences between the first four peak values ​​and E4, denoted as E4.

[0061] Specifically, the peak value (a) of the first four peak points is obtained. i The absolute value of the difference between (i = 1, 2, 3, 4) and E4 (|a i -E4|=A i The mean of (i = 1, 2, 3, 4) is denoted as

[0062] Step 3: Find the next peak point i.

[0063] Step 4: Calculate A i .

[0064] The peak value of the next detected leveling current fluctuation (the i-th peak value a) will be used as the reference. i ) and the previous peak (the (i-1)th peak a) i-1 The average value of the leveling current at point (E) i-1 The absolute value of the difference between (a and b) (denoted as |a) i -E i-1 |=A i (i>4) is used as a reference value for judging abnormal fluctuations in the leveling current.

[0065] Step 5: If Then discard the data from the (i-1)th peak point to the (i+1)th peak point; otherwise, calculate the recursive average value E of the effective leveling current before the ith peak point. i Calculate the recursive average value A of the effective peak points. i .

[0066] With A1, ..., A i-1 recursive average As a criterion for judging outliers, if If the peak value (the i-th peak value) is considered to be an abnormal fluctuation period, the data between the (i-1)-th peak value and the (i+1)-th peak value is discarded. Conversely, if the peak value is not abnormal, the data between the peak value and the previous peak value is considered to be valid. The recursive mean of the balancing current E is then calculated. i and peak point recursive mean

[0067] Step 6: If the data acquisition of the leveling current is completed, the effective leveling current recursive average value is obtained; otherwise, proceed to step 3 to continue the calculation.

[0068] The collected balancing current data are sequentially detected, and the recursive average of the valid data and the recursive average of the valid peak points are calculated. This process continues until the last fluctuation peak before the balancing current data collection ends. If the fluctuation peak is even, the recursive average of the second-to-last peak is taken as the calculated balancing current value; if the fluctuation peak is odd, the recursive average of the last peak is taken as the calculated balancing current value. The method provided in this application has a certain degree of real-time requirement, necessitating timely real-time processing of the balancing current data and the implementation of self-aiming or self-calibration of the inertial platform system.

[0069] For example:

[0070] Taking the abnormal leveling current data from two self-aiming tests of a certain inertial platform system as an example, the abnormal value detection and processing of the leveling current was carried out, and the leveling current curve was obtained as follows. Figure 3 , Figure 4 As shown:

[0071] Figure 3 , Figure 4 The solid line represents detected outliers, and the dashed line represents valid data after removing outliers. The horizontal axis represents time in seconds (S), and the vertical axis represents the number of leveling current pulses in liters per second (LSB). Figure 3 , Figure 4 As can be seen, the method for detecting and processing abnormal values ​​of leveling current in an inertial platform system provided by this invention can effectively detect abnormal leveling currents and greatly improve data processing quality. Therefore, the method for detecting and processing abnormal values ​​of leveling current in an inertial platform system provided in this application can perform auto-aiming and auto-calibration of the inertial platform system with relatively high accuracy.

[0072] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims of the present invention.

Claims

1. A method for detecting and processing abnormal values ​​of leveling current in an inertial platform system, characterized in that, include: Step 1: Obtain the recursive average value and fluctuation peak value of the leveling current of the inertial platform system; The recursive average value is the average value of the effective leveling current between the (k-1)th fluctuation peak value before the kth fluctuation peak value and the first fluctuation peak value; Step 2: Determine the absolute value of the k-th difference based on the k-th fluctuation peak and the recursive average value, where the absolute value of the k-th difference is the absolute value of the difference between the k-th fluctuation peak and the recursive average value; Step 3a: If the absolute value of the kth difference is greater than or equal to a preset threshold, the data between the peak points before and after the kth fluctuation peak is treated as abnormal data and removed. Step 3b: If the absolute value of the kth difference is less than the preset threshold, then the data between the kth fluctuation peak point and the (k-1)th fluctuation peak point is retained as valid data; Step 4: Let k = k + 1, and repeat step 1 until the balancing termination time; Step 5: If k is even, take the valid data between the first peak and the (k-1)th peak as the calculation data; if k is odd, take the valid data between the first peak and the kth peak as the calculation data.

2. The method according to claim 1, characterized in that, The calculated data after the anomaly detection and processing of the leveling current is used to perform self-aiming and / or self-calibration calculations for the inertial platform system.

3. The method according to claim 2, characterized in that, The self-aiming of the inertial platform system includes: Get the azimuth angle of auto-aiming. azimuth satisfy: ， The average current for leveling at the i-th position. The angle between the three positions is the rotational azimuth.

4. The method according to claim 1, characterized in that, The effective leveling current is the leveling current retained after removing abnormal data between the first fluctuation peak and the (k-1)th fluctuation peak.

5. The method according to claim 1 or 2, characterized in that, The preset threshold is twice the average of the m absolute values ​​of the differences preceding the k-th absolute value, where m <k。 6. The method according to claim 5, characterized in that, m=k-1.

7. The method according to claim 1 or 2, characterized in that, The s-th fluctuation peak is removed from the k-1 fluctuation peaks preceding the k-th fluctuation peak, and the absolute value of the s-th difference corresponding to the s-th fluctuation peak is equal to the average of the absolute values ​​of the s-1 differences preceding the s-th fluctuation peak.

8. The method according to claim 1 or 2, characterized in that, When step 2 is executed for the first time, k = 3-8.

9. An electronic device, characterized in that, The electronic device is used to perform the method as described in any one of claims 1 to 8.

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