A frequency detection system and method for an airport navigation lighting control system

Abstract

The present invention relates to a frequency detection system and method for an airport navigation light control system, which belongs to the technical field of frequency detection and includes the following modules: a bandpass filtering module, a sampling rate conversion module, a total energy estimation module, a frequency energy estimation module, an energy proportion estimation module, an energy proportion frequency distribution estimation module, an optimal detection threshold estimation module, and a detection module; by using the frequency detection system and method of the present invention to detect the frequency of the input signal to be tested, i The energy proportion of the frequencies to be tested is used to determine whether these frequencies to be tested exist, and the lamp status control signal is output to control the runway lamps. In addition, the present invention achieves the effects of strong data transmission reliability, fast data processing response speed, and low cost and design complexity. The method of parsing frequencies by energy proportion proposed in the present invention has good adaptability to signal attenuation, is suitable for scenarios where signal strength changes due to transmission distance or attenuation, and has strong anti-noise ability.

Description

Technical Field

[0001] The present invention belongs to the technical field of frequency detection, and particularly relates to a frequency detection system and method for an airport navigation lighting control system. Background Art

[0002] With the rapid development of the global air transportation industry, the importance of the airport navigation lighting control system has become increasingly prominent. As an important facility to ensure the safe taxiing, takeoff and landing of aircraft, the requirements for the intelligence and high reliability of the airport navigation lighting control system are constantly increasing. In recent years, with the development of communication technology and control technology, the airport navigation lighting control system has gradually evolved from traditional wired switch control to centralized and digital management using power line carrier technology. At the same time, as an important branch in the field of signal processing, signal frequency detection technology has been continuously expanding its application scope in recent years with the rapid development of communication technology and digital processing technology, covering multiple fields such as wireless communication, industrial automation, environmental monitoring and intelligent transportation. In the airport navigation lighting control system, signal frequency detection technology is particularly important, which is directly related to the accurate judgment and control of the system on the lamp state. The uniqueness of the airport navigation lighting control system lies in that its operating environment has the characteristics of high noise and high interference, and at the same time has extremely high requirements for the real-time and accuracy of signal detection. In this scenario, traditional amplitude detection or data frame parsing methods are difficult to meet the requirements, and frequency detection technology has gradually become a key technology in the airport navigation lighting control system due to its strong anti-interference ability and flexible implementation method.

[0003] In the prior art, a Chinese patent with the publication number CN109506896A relates to an airport runway lighting detection system, which mainly consists of a single-chip microcomputer control system, a camera, a Bluetooth sending module, a Bluetooth receiving module and a computer terminal, and remotely detects the runway lighting through the photos taken by the camera; this patent has the following disadvantages: long processing time and low efficiency; it requires manpower and material resources for detection. In addition, a Chinese patent with the publication number CN110749601A relates to an image-based airport runway lamp detection system and method, including an image acquisition device for acquiring images of all lamp-containing areas within the runway area and sending the images of the lamp-containing areas to a processor; the processor is used for real-time detecting whether there are image areas with damaged lamps in the image set of the lamp-containing areas; and a result output device for outputting the detection result. This patent has the following disadvantages: high implementation cost, requiring high-resolution cameras and image processing hardware, etc.; image detection may be misjudged due to dirt, light, bad weather or interference; the time required for image acquisition, transmission and processing is long, and the computational burden is large. Summary of the Invention

[0004] The present invention aims at the above problems, makes up for the deficiencies of the prior art, and provides a frequency detection system and method for an airport navigation lighting control system.

[0005] To achieve the above object, the present invention adopts the following technical solutions.

[0006] A frequency detection system for an airport navigation lighting control system provided by the present invention includes the following modules;

[0007] Band-pass filtering module: used to filter the input signal to be measured, including a high-pass filter and a low-pass filter connected in series. The cut-off frequency of the high-pass filter is set to filter out signal components below the frequency range to be measured, and the cut-off frequency of the low-pass filter is set to filter out signal components above the frequency range to be measured;

[0008] Sampling rate conversion module: used to perform D-fold downsampling on the filtered signal to generate a downsampled signal; Total energy estimation module: used to calculate the total energy of the signal according to the downsampled signal;

[0009] Multiple frequency energy estimation modules: Each frequency energy estimation module uses the Goertzel algorithm to calculate the fundamental wave and second harmonic energy of the frequency to be measured, and takes the sum of the calculated fundamental wave and second harmonic energy as the total energy of the frequency to be measured;

[0010] Energy ratio estimation module: used to calculate the energy ratio of each frequency to be measured according to the total energy of the signal obtained by the total energy estimation module and the total energy of each frequency to be measured obtained by each frequency energy estimation module;

[0011] Energy ratio frequency distribution estimation module: generates a frequency distribution histogram of the energy ratio based on historical data;

[0012] Optimal detection threshold estimation module: applies the Otsu algorithm to analyze the frequency distribution histogram to determine the optimal decision threshold;

[0013] Detection module: used to compare the energy ratio of each frequency to be measured with the optimal decision threshold to determine whether these frequencies to be measured exist.

[0014] As a preferred solution of the present invention, the frequency range to be measured is 4 kHz to 9 kHz, the cut-off frequency of the high-pass filter is set to 3 kHz, and the cut-off frequency of the low-pass filter is set to 10 kHz.

[0015] As another preferred solution of the present invention, the difference equation of the high-pass filter is:

[0016] (1),

[0017] The difference equation of the low-pass filter is:

[0018] (2);

[0019] Wherein, a is the filtering coefficient of the high-pass filter, b is the filtering coefficient of the low-pass filter, x 0( n ) is the input of the high-pass filter, that is, the signal to be measured input to the band-pass filtering module, y 1( n ) is the output of the high-pass filter, x 1( n ) is the output of the low-pass filter, n = 0, 1, …, N -1, N represents x 0( n )'s original number of sampling points, that is, x 0( n )'s signal length is N sampling points, and the sampling rate is f s .

[0020] As another preferred embodiment of the present invention, the total energy estimation module uses the formula:

[0021] (3),

[0022] to calculate the total energy of the signal; in the formula: D represents the downsampling multiple in the sampling rate conversion module, N / D is D times the length of the signal after downsampling, x 2( n ) is the output signal of the sampling rate conversion module, and the output signal x 2( n ) = x 1( nD ), n = 0, 1, …, N / D - 1.

[0023] As another preferred embodiment of the present invention, in the frequency energy estimation module, the Goertzel algorithm is used to calculate the fundamental wave energy e i，1 The calculation steps are as follows:

[0024] , (4),

[0025] (5),

[0026] (6);

[0027] Wherein, q 1 ( n ) is an intermediate variable of the Goertzel algorithm;

[0028] Calculate the second harmonic energy using the Goertzel algorithm e i，2 The calculation steps are as follows:

[0029] (7),

[0030] (8),

[0031] (9),

[0032] (10);

[0033] Wherein, fi,[[]]END]] 1 represents the fundamental frequency of the i th frequency to be measured; fi,[[]]END]] 2 represents the second harmonic frequency of the i th frequency to be measured, that is, twice the fundamental frequency; q 2 ( n ) is an intermediate variable of the Goertzel algorithm; the output of the frequency energy estimation module e i is:

[0034] e i = e i，1＋ e i，2 (11).

[0035] As another preferred solution of the present invention, in the optimal detection threshold estimation module, the optimal decision threshold is determined by calculating the maximum between-class variance using the Otsu algorithm, specifically including: normalizing the histogram probability; calculating the cumulative probability and cumulative mean; traversing all thresholds and selecting the value that maximizes the between-class variance as the optimal decision threshold.

[0036] As another preferred solution of the present invention, in the detection module, if the energy ratio of each frequency to be measured exceeds the optimal decision threshold, it is determined that these frequencies to be measured exist, and a lamp state control signal is output.

[0037] A frequency detection method for an airport navigation lighting control system provided by the present invention is implemented using the frequency detection system of the airport navigation lighting control system, and includes the following steps:

[0038] First, the input signal to be measured x 0 ( n ) passes through a band-pass filtering module. After filtering out some high-frequency and low-frequency components, the signal x 1 ( n ) is obtained. Subsequently, the signal x 2 ( n ) is obtained through a sampling rate conversion module;

[0039] Then, the signal x 2 ( n ) passes through i frequency energy estimation modules and a total energy estimation module respectively, i where e i represents the number of frequencies to be measured; the output e total of each frequency energy estimation module and the output i of the total energy estimation module are used as inputs to enter d i energy proportion estimation modules to calculate the energy proportion

[0040] corresponding to each frequency to be measured; Th i Finally, for the energy proportion corresponding to each frequency to be measured, the optimal decision threshold d i is obtained through an energy proportion frequency distribution estimation module and an optimal detection threshold estimation module. The energy proportion Th i corresponding to each frequency to be measured is compared with the optimal decision threshold

[0041] through a detection module to obtain a detection result, so as to determine whether these frequencies to be measured exist. d i Furthermore, in the detection result, if the energy proportion Th i corresponding to each frequency to be measured exceeds the optimal decision threshold

[0042] The beneficial effects of the present invention are as follows:

[0043] The frequency detection system and method of an airport navigation lighting control system provided by the present invention, by using the frequency detection system and method of the present invention to detect the iThe energy proportion of the frequencies to be measured is used to determine whether these frequencies to be measured exist among the frequencies to be measured, so as to control the runway lights; and through the above technical solutions, the present invention achieves the effects of strong data transmission reliability, fast data processing response speed, and low cost and design complexity. In addition, the frequency detection system and method proposed by the present invention have low cost and are easy to implement; they have good stability and high accuracy under harsh noise environment conditions such as airports; they have small computational complexity in the system, high detection efficiency, and are convenient for subsequent operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 FIG. is a signal flow processing flowchart of a frequency detection system of an airport navigation lighting control system according to the present invention.

[0045] Figure 2 FIG. is a signal flow processing flowchart of a band-pass filtering module of a frequency detection system of an airport navigation lighting control system according to the present invention.

[0046] Figure 3 FIG. is a time-domain waveform diagram of a signal to be measured returned by a lamp.

[0047] Figure 4 FIG. is a frequency spectrum diagram of a signal to be measured returned by a lamp.

[0048] Figure 5 FIG. is a frequency distribution histogram of the energy proportion of candidate frequencies. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Combined with Figure 1 and Figure 2 shown in FIG., a frequency detection system of an airport navigation lighting control system provided by an embodiment of the present invention includes the following modules;

[0051] Band-pass filtering module: used to filter the input signal to be measured, including a high-pass filter and a low-pass filter connected in series. The cut-off frequency of the high-pass filter is set to filter out signal components below the frequency range to be measured, and the cut-off frequency of the low-pass filter is set to filter out signal components above the frequency range to be measured;

[0052] Sampling rate conversion module: used to perform D-fold downsampling on the filtered signal to generate a downsampled signal; Total energy estimation module: used to calculate the total energy of the signal according to the downsampled signal;

[0053] Multiple frequency energy estimation modules: Each frequency energy estimation module uses the Goertzel algorithm to calculate the fundamental wave and second harmonic energies of the frequency to be measured, and takes the sum of the calculated fundamental wave and second harmonic energies as the total energy of the frequency to be measured;

[0054] Energy proportion estimation module: Used to calculate the energy proportion of each frequency to be measured according to the total energy of the signal obtained by the total energy estimation module and the total energies of the frequencies to be measured obtained by each frequency energy estimation module;

[0055] Energy proportion frequency distribution estimation module: Generates a frequency distribution histogram of the energy proportion based on historical data;

[0056] Optimal detection threshold estimation module: Applies the Otsu algorithm to analyze the frequency distribution histogram and determine the optimal decision threshold;

[0057] Detection module: Used to compare the energy proportion of each frequency to be measured with the optimal decision threshold to determine whether these frequencies to be measured exist.

[0058] Specifically, in the application scenario of the present invention, the frequency range to be measured is concentrated between 4 kHz and 9 kHz. The basic alternating current of the power supply line is a periodic sine wave with a frequency of 50 Hz, and the signal used to transmit the lamp state information is superimposed on the low-frequency 50 Hz sine wave; in addition, due to the complex electromagnetic environment at the airport, there will be irrelevant frequency components interfering with subsequent frequency detection; through the preliminary filtering of the band-pass filtering module, the signal processed by the band-pass filtering module filters out the irrelevant high-frequency and low-frequency frequency components, which can improve the reliability of subsequent frequency detection. Therefore, the cut-off frequency of the high-pass filter is set to 3 kHz, and the cut-off frequency of the low-pass filter is set to 10 kHz; the difference equation of the high-pass filter is:

[0059] (1),

[0060] The difference equation of the low-pass filter is:

[0061] (2);

[0062] Wherein, a is the filtering coefficient of the high-pass filter, b is the filtering coefficient of the low-pass filter, x 0 ( n ) is the input of the high-pass filter, that is, the signal to be measured input to the band-pass filtering module, y 1 ( n ) is the output of the high-pass filter, x 1 ( n ) is the output of the low-pass filter, n= 0, 1, …, N -1, N denotes x 0 ( n ) the original number of sampling points, i.e., x 0 ( n ) the signal length is N number of sampling points, and the sampling rate is f s .

[0063] Specifically, the total energy estimation module calculates the total energy of the signal through the formula:

[0064] (3),

[0065] where in the formula: D denotes the downsampling multiple in the sampling rate conversion module, N / D is D times the length of the signal after downsampling, x 2 ( n ) is the output signal of the sampling rate conversion module, x 1 ( n ) after being processed by the D times downsampling of the sampling rate conversion module, the output signal x 2 ( n ) = x 1 ( nD ), n = 0, 1, …, N / D - 1.

[0066] Specifically, in the frequency energy estimation module, the Goertzel algorithm is used to calculate the signal energy on a specific frequency component to be measured in the signal to be measured. The Goertzel algorithm is an efficient digital signal processing algorithm for detecting specific frequency components in a signal. By adopting the Goertzel algorithm in the present invention, the computational complexity and resource consumption are significantly reduced. To improve the detection accuracy, in the present invention, the energy of the fundamental wave and the second harmonic (i.e., the second harmonic of the fundamental wave) on a specific frequency component to be measured in the signal to be measured is calculated and jointly used to analyze the existence of the specific frequency to be measured; x 2 ( n ) after passing through the frequency f i the output of the energy estimation module is the energy e i , i= 1, …, N－ 1. The calculation steps of the fundamental wave energy e i，1 in the present invention are as follows:

[0067] , (4),

[0068] (5),

[0069] (6);

[0070] Among them, q 1 ( n )is an intermediate variable of the Goertzel algorithm;

[0071] The steps of calculating the second harmonic energy using the Goertzel algorithm e i，2 are the same as those of calculating the fundamental wave energy e i，1 , but the frequency used for calculation needs to be replaced with the second harmonic of this frequency, that is:

[0072] (7),

[0073] (8),

[0074] (9),

[0075] (10);

[0076] Among them, fi,[[]]END]] 1 represents the fundamental wave frequency of the i th frequency to be measured; fi,[[]]END]] 2 represents the second harmonic frequency of the i th frequency to be measured, that is, the second harmonic of the fundamental wave frequency; q 2 ( n )is an intermediate variable of the Goertzel algorithm; the output of the frequency energy estimation module e i is:

[0077] e i = e i，1＋ e i，2 (11).

[0078] Specifically, the e total and e i obtained from equations (3) and (11) respectively can be used to calculate the energy ratio d i corresponding to each frequency to be measured. The calculation formula is: (12).

[0079] When determining whether there is a specific frequency component to be measured as expected in the carrier signal returned by the lamp, an optimal decision threshold is set. If the energy proportion of the candidate frequency exceeds this optimal decision threshold, it is considered that the specific frequency component to be measured exists in the carrier signal, which can be used to judge the state of the lamp. In order to calculate this optimal decision threshold, it is necessary to first use the existing historical R data for detection and statistically obtain the frequency distribution histogram of the energy proportion of the candidate frequency between 0 and 1. In the energy proportion frequency distribution estimation module, calculate the latest R data for the frequency f i energy proportion d i frequency distribution histogram, which is expressed as follows:

[0080] hist i m , m = 0, 1,..., 99 (13);

[0081] This frequency distribution histogram is statistically obtained according to the percentage of the energy proportion of the candidate frequency. This frequency distribution histogram consists of 100 bars from 0 to 99, where m represents the value obtained by rounding down the percentage of the energy calculated by expression (12), with values ranging from 0 to 99, hist i m represents the frequency of the energy proportion m of the candidate frequency d i appearing in the test data. The method for calculating the frequency distribution histogram of the energy proportion

[0082] is as follows:

[0083] First, initialize, histi[m] = 0 (14); R Then, accumulate the frequency of the k th data in the d i corresponding appearance in the d i th data, that is, whenever the same energy proportion d i appears once, the corresponding value in the frequency distribution histogram is incremented by 1, and the frequency distribution histogram of the energy proportion

[0084] ​​In the optimal detection threshold estimation module, the optimal decision threshold is determined by applying the Otsu algorithm to calculate the maximum between-class variance, which specifically includes: normalizing the histogram probability; calculating the cumulative probability and cumulative mean; traversing all thresholds and selecting the value that maximizes the between-class variance as the optimal decision threshold.

[0085] In the optimal detection threshold estimation module, for the energy proportion obtained from the detection of the above R data, the Otsu algorithm is used to find a threshold as the optimal decision threshold in the Otsu algorithm. Among them, the Otsu algorithm is a method for automatically determining the binarization threshold of an image. By calculating the gray-level histogram of the image, a gray-level value is selected as the threshold to divide the image pixels into two parts: foreground and background. In the present invention, the Otsu algorithm is used to find a suitable energy proportion threshold from the frequency distribution histogram of the energy proportion to divide the existence or non-existence of a specific frequency to be measured, so as to serve as the optimal decision threshold for detecting the specific frequency to be measured; the detailed calculation steps of the Otsu algorithm are as follows:

[0086] Calculate the normalized histogram probability as follows:

[0087] (15);

[0088] Where p ( i ) represents the occurrence probability of the i th energy proportion.

[0089] Calculate the cumulative probability and cumulative mean as follows:

[0090] (weight of the class where the frequency does not exist) (16),

[0091] (weight of the class where the frequency exists) (17),

[0092] (mean of the class where the frequency does not exist) (18),

[0093] (mean of the class where the frequency exists) (19);

[0094] Calculate the between-class variance as follows:

[0095] (20);

[0096] Traverse all possible thresholds t , and find the that maximizes t as the optimal decision threshold.

[0097] In addition, a frequency detection method for an airport navigation lighting control system provided by an embodiment of the present invention includes the following steps:

[0098] First, the input signal to be measured x 0 ( n ) passes through a band-pass filtering module, and after filtering out some high-frequency and low-frequency components, a signal x 1 ( n ) is obtained. Subsequently, a signal x 2 ( n ) is obtained through a sampling rate conversion module;

[0099] Then, the signal x 2 ( n ) passes through i frequency energy estimation modules and a total energy estimation module respectively, i representing the number of frequencies to be measured; the output e i of each frequency energy estimation module and the output e total of the total energy estimation module are used as inputs and enter i energy ratio estimation modules to calculate the energy ratio d i corresponding to each frequency to be measured;

[0100] Finally, for the energy ratio corresponding to each frequency to be measured, the optimal decision threshold Th i is obtained through an energy ratio frequency distribution estimation module and an optimal detection threshold estimation module. The energy ratio d i corresponding to each frequency to be measured and the optimal decision threshold Th i are compared through a detection module to obtain a detection result, so as to determine whether these frequencies to be measured exist; in the detection result, if the energy ratio d i corresponding to each frequency to be measured exceeds the optimal decision threshold Th i , it is determined that these frequencies to be measured exist, and a lamp state control signal is output.

[0101] Airport navigation light detection technology is an important part of the airport navigation light control system, which determines whether the airport tower can accurately know the status of the lights on the airport runway and perform further control. The frequency detection system and method of the airport navigation light control system proposed in the present invention can mainly solve the following problems in the airport navigation light control system: (1) The problem of low data transmission reliability in the existing technology: The existing airport navigation light detection technology is easily affected by the external environment such as bad weather, dust and light changes on image acquisition in the airport environment, resulting in misjudgment of the status of the lights on the airport runway; (2) The problem of poor real-time performance and slow response speed in the existing technology: The existing airport navigation light detection technology needs to judge the status of the lights through the pictures sent back from the site, and cannot perform real-time processing, and the response speed is slow; (3) The problem of high cost and design complexity of the existing technology: The existing airport navigation light detection technology requires high hardware cost, and the image processing system requires a large amount of computing resources and complex image processing algorithms, which has high cost and design complexity.

[0102] In summary, the present invention accurately determines the state of the lamp by analyzing certain specific frequency components in the carrier signal sent back by the lamp. The carrier signal sent back by the lamp is an electrical signal. The present invention allows the user to provide i The frequency to be tested is detected by using the frequency detection system and method of the present invention. i The energy proportion of the frequencies to be tested is used to determine whether these frequencies to be tested exist, and then the runway lights are controlled; and through the above-mentioned technical solution, the present invention achieves the effects of strong data transmission reliability, fast data processing response speed, and low cost and design complexity. The method of parsing frequency by energy proportion proposed in the present invention has good adaptability to signal attenuation, is suitable for scenarios where signal strength changes due to transmission distance or attenuation, and has strong anti-noise ability; in the frequency energy estimation module of the present invention, the Goertzel algorithm is used to calculate the energy of specific frequency components to be tested in the signal to be tested, which has high efficiency and reliability; in the optimal detection threshold estimation module of the present invention, the Otsu algorithm is applied to the field of signal processing, and the accuracy of the optimal decision threshold calculated by the Otsu algorithm is higher when making decisions. In the present invention, the Goertzel algorithm is combined with the Otsu algorithm to further improve the reliability and real-time performance of the airport navigation lighting control system in complex electromagnetic environments.

[0103] To verify the effectiveness of the frequency detection system and method of the present invention, the frequency detection system and method are implemented and tested on a development board based on the STM32F446RE microcontroller; STM32F446RE is a microcontroller based on the ARM Cortex-M4 core, with rich peripheral interfaces and powerful processing capabilities, and is widely used in industrial control, consumer electronics, communication and other fields.

[0104] Further, data collected from the airport site is selected for testing. There are about 130 signals transmitted back from the lamps collected from the site. The waveform diagram of the typical signal is as Figure 3 shown. The window length of the valid signal is about 3 - 4 ms. Given that the transmission frequency of this signal is 4.3 kHz, due to the complex electromagnetic environment at the airport site and the interference of components, the main peak of the signal spectrum may be partially shifted. The spectrum diagram of the signal is viewed as Figure 4 shown. Testing is carried out in the Keil uversion5 environment. The number of i frequencies to be measured is f selected as 3, which are respectively f 1 = 4.3 kHz, f 2 = 5.2 kHz,

[0105] 3 = 6.6 kHz. The test results are shown in Table 1 below.

[0106]

[0107] As can be seen from Table 1, for the detection of the 3 candidate frequencies respectively, the energy ratio results show that the energy ratio corresponding to the 4.3 kHz frequency is the highest, about 0.21948. It can be preliminarily determined that this frequency component exists in the signal to be measured. At the same time, for a signal to be measured with a length of about 3 - 4 ms, the detection time of the frequency detection system and method of the present invention is only about 1 ms, which can achieve efficient real-time detection and control.

[0108] Subsequently, to obtain the optimal decision threshold, 100 pieces of data collected from the site are used for batch detection, and the frequency distribution histogram of the energy ratio of the candidate frequencies is statistically obtained, as Figure 5 shown; the Otsu algorithm described in the present invention is applied to the frequency distribution histogram of the energy ratio of the candidate frequencies to calculate the classification threshold, and the optimal threshold is obtained as 0.16 as the optimal decision threshold. The detection results of another 30 pieces of data collected at the same site are shown in Table 2 below.

[0109] Table 2 Detection results of another 30 pieces of data at the same site

[0110]

[0111] As can be seen from the data in Table 2, taking 0.16 as the optimal decision threshold, all the frequency information carried in 30 pieces of data can be parsed, and the parsing accuracy can reach 100% under the condition of limited data in this environment.

[0112] It can be understood that the above specific description of the present invention is only for explaining the present invention and is not limited to the technical solutions described in the embodiments of the present invention. Those of ordinary skill in the art should understand that the present invention can still be modified or equivalently replaced to achieve the same technical effect; as long as the use requirements are met, they are all within the protection scope of the present invention.

Claims

1. A frequency detection system for an airport navigation lighting control system, characterized in that, It includes the following modules; Band - pass filtering module: It is used to filter the input signal to be measured. It includes a high - pass filter and a low - pass filter connected in series. The cut - off frequency of the high - pass filter is set to filter out signal components below the frequency range to be measured, and the cut - off frequency of the low - pass filter is set to filter out signal components above the frequency range to be measured; Sampling rate conversion module: It is used to perform D - fold down - sampling on the filtered signal to generate a down - sampled signal; Total energy estimation module: It is used to calculate the total energy of the signal according to the down - sampled signal; Multiple frequency energy estimation modules: Each frequency energy estimation module uses the Goertzel algorithm to calculate the fundamental wave and second - harmonic energy of the frequency to be measured, and takes the sum of the calculated fundamental wave and second - harmonic energy as the total energy of the frequency to be measured; Energy ratio estimation module: It is used to calculate the energy ratio of each frequency to be measured according to the total energy of the signal obtained by the total energy estimation module and the total energy of each frequency to be measured obtained by each frequency energy estimation module; Energy ratio frequency distribution estimation module: It generates a frequency distribution histogram of the energy ratio based on historical data; Optimal detection threshold estimation module: It analyzes the frequency distribution histogram by applying the Otsu algorithm to determine the optimal decision threshold; Detection module: It is used to compare the energy ratio of each frequency to be measured with the optimal decision threshold to determine whether these frequencies to be measured exist.

2. The frequency detection system of an airport navigation lighting control system according to claim 1, characterized in that The frequency range to be measured is from 4 kHz to 9 kHz. The cut - off frequency of the high - pass filter is set to 3 kHz, and the cut - off frequency of the low - pass filter is set to 10 kHz.

3. The frequency detection system of an airport navigation lighting control system according to claim 1, characterized in that The difference equation of the low - pass filter is: （1）； Among them, b is the filtering coefficient of the low-pass filter, y 1( n ) is the output of the high-pass filter, x 1( n ) is the output of the low-pass filter, n = 0, 1, …, N -1; the input of the high-pass filter is x 0( n ), that is, the signal to be measured input to the band-pass filtering module, N denotes x 0( n )'s original number of sampling points, that is, x 0( n )'s signal length is N sampling points, and the sampling rate is f s .

4. The frequency detection system of an airport navigation lighting control system according to claim 3, wherein the total energy estimation module passes through the formula: （2）， Calculate the total energy of the signal; in the formula: D Indicates the downsampling multiple in the sampling rate conversion module. N / D for D times the length of the downsampled signal, x 2 ( n ) is the output signal of the sampling rate conversion module, the output signal x 2 ( n )= x 1( nD ), n =0, 1, …, N / D- 1.

5. The frequency detection system of an airport navigation lighting control system according to claim 4, characterized in that In the frequency energy estimation module, the Goertzel algorithm is used to calculate the fundamental wave energy e i，1 The calculation steps are as follows: ， （3）， （4）， （5）； Among them, q 1 ( n ) is an intermediate variable of the Goertzel algorithm; Calculate the second harmonic energy using the Goertzel algorithm e i，2 The calculation steps are as follows: （6）， （7）， （8）， （9）； Among them, fi, 1 represents the fundamental frequency of the i th frequency to be measured; fi, 2 represents the second harmonic frequency of the i th frequency to be measured, that is, the second harmonic of the fundamental frequency; q 2 ( n ) is an intermediate variable of the Goertzel algorithm; the output of the frequency energy estimation module e i is: e i = e i，1＋ e i，2 （10）。 6. The frequency detection system of an airport navigation lighting control system according to claim 1, characterized in that In the optimal detection threshold estimation module, the optimal decision threshold is determined by calculating the maximum value of the between - class variance by applying the Otsu algorithm, specifically including: normalizing the histogram probability; calculating the cumulative probability and cumulative mean; traversing all thresholds and selecting the value that makes the between - class variance the largest as the optimal decision threshold.

7. The frequency detection system of an airport navigation lighting control system according to claim 1, characterized in that In the detection module, if the energy ratio of each frequency to be measured exceeds the optimal decision threshold, it is determined that these frequencies to be measured exist, and a lamp state control signal is output.

8. A frequency detection method for an airport navigation lighting control system, characterized in that, It is implemented using the frequency detection system of the airport navigation lighting control system according to any one of claims 1 to 7, including the following steps: First, the input signal to be measured x 0 ( n ) passes through the band - pass filtering module. After filtering out some high - frequency and low - frequency components, the signal x 1 ( n ) is obtained. Subsequently, the signal x 2 ( n ) is obtained through the sampling rate conversion module; Then, the signal x 2 ( n ) are respectively passed through i frequency energy estimation modules and a total energy estimation module, i representing the number of frequencies to be measured; the output e i of each frequency energy estimation module e total and the output i of the total energy estimation module d i are used as inputs to enter i energy proportion estimation modules to calculate the energy proportion d i corresponding to each frequency to be measured; Finally, for the energy ratio corresponding to each frequency to be measured, the optimal decision threshold is obtained through the energy ratio frequency distribution estimation module and the optimal detection threshold estimation module. Th i , and the energy ratio corresponding to each frequency to be measured is d i compared with the optimal decision threshold Th i by the detection module to obtain the detection result, so as to determine whether there is a specific target frequency.

9. A frequency detection method for an airport navigation lighting control system according to claim 8, characterized in that, In the detection result, if the energy ratio corresponding to each frequency to be measured d i exceeds the optimal decision threshold Th i , it is determined that these frequencies to be measured exist, and a lamp state control signal is output.

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