Channel extraction method based on tetra cluster signal characteristics

Abstract

The invention discloses a channel extraction method based on tetra cluster signal features. The method comprises the following steps: acquiring zero intermediate frequency baseband IQ data; performing discrete Fourier transform on the IQ data to obtain spectrum data; extracting a center frequency corresponding to the maximum spectral line based on the spectrum data; performing frequency coarse correction on the baseband IQ data, and moving a target channel signal to a baseband; performing low-pass filtering on the signal data after coarse correction; performing frequency fine correction on the signal data after the low-pass filtering; and performing matched filtering on the finely corrected signal data, and extracting target channel IQ data.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a channel extraction method based on TETRA cluster signal characteristics. Background Art

[0002] In the TETRA trunking signal frequency band, the channel bandwidth is divided into 25kHz intervals. Several channels may be occupied simultaneously, and the receiver's receiving bandwidth often exceeds 25kHz, resulting in the receiving bandwidth containing more than one channel of TETRA signals. In this case, if demodulation is performed directly, the non-target channel signals will significantly interfere with the target channel signal, causing the TETRA demodulation bit error rate to increase significantly, and in severe cases, TETRA demodulation failure. Summary of the Invention

[0003] The present invention aims to provide a channel extraction method based on the characteristics of Tetra cluster signals. This method addresses the shortcomings of current mainstream UAV radio monitoring and direction-finding equipment. This device utilizes a Watson-Watt direction-finding system, a zero-IF software-defined radio chip as the core RF frequency converter, and a system-on-chip (SoC) integrating FPGA and ARM as the data processing system. This solution is lightweight and compact, making it suitable for installation on micro-UAVs. It also offers excellent direction-finding accuracy and robust signal monitoring and identification capabilities.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A channel extraction method based on TETRA trunking signals comprises the following steps:

[0006] Obtain zero-IF baseband IQ data;

[0007] Performing a discrete Fourier transform on the IQ data to obtain spectrum data;

[0008] Extract the center frequency corresponding to the maximum spectrum line based on the spectrum data;

[0009] Perform coarse frequency correction on baseband IQ data and move the target channel signal to baseband;

[0010] Performing low-pass filtering on the roughly corrected signal data;

[0011] Perform fine frequency correction on the signal data after low-pass filtering;

[0012] Perform matched filtering on the finely corrected signal data to extract the target channel IQ data.

[0013] In some embodiments, obtaining zero-IF baseband IQ data includes setting a center frequency and bandwidth of a receiver so that a data acquisition range of the receiver is within a tetra frequency band, and setting the bandwidth to be greater than 50 kHz.

[0014] In some embodiments, performing a discrete Fourier transform on the IQ data to obtain spectrum data includes: performing a discrete Fourier transform on the received baseband IQ data within the tetra frequency band, assuming a sampling rate of fs, taking a length of N to obtain spectrum data;

[0015] The length of N is determined by the following formula:

[0016] N>=ceil(fs*T), where T is the signal frame length; the tetra signal frame length is 14.167*4 milliseconds;

[0017] Ceil means round up.

[0018] In some embodiments, the method of extracting the center frequency corresponding to the maximum spectral line based on the spectrum data includes: finding the maximum value of the spectrum amplitude based on the spectrum data, and calculating its corresponding center frequency fc, which is the carrier frequency at the channel with the strongest signal.

[0019] In some embodiments, assuming that the data index corresponding to the maximum spectrum amplitude is indexMax, the center frequency fc is calculated as follows:

[0020] fc=-fs / 2+fs / N*indexMax; (1)

[0021] Where fs is the sampling rate and N is the number of discrete Fourier points.

[0022] In some embodiments, performing coarse frequency correction on the baseband IQ data to move the target channel signal to the baseband includes: performing coarse frequency correction on the acquired baseband IQ data, using the acquired carrier frequency as the correction frequency, and performing the correction method as follows:

[0023] dataFix=data.*exp(-1i*(2*π*fc*n / fs)); (2)

[0024] Where data is the acquired baseband IQ data; π is a constant; n is 0, 1, 2, ...; fs is the sampling rate; and 1i is an imaginary number.

[0025] In some embodiments, the low-pass filtering of the roughly corrected signal data includes: setting the passband width of the low-pass filter to twice the TETRA symbol rate; after completing the filter setting, filtering the obtained corrected signal data through the low-pass filter to obtain the target channel data.

[0026] In some embodiments, the frequency of the signal data after low-pass filtering is finely corrected; including: setting the Fourier transform window length to 30 symbol lengths, performing Fourier transform on the data within the sliding window range in the form of a sliding window, and judging whether there is only a single-tone signal in the sliding window. If there is only a single-tone signal, calculating the frequency of the single tone as fc1, and correcting the obtained low-pass filtered signal data. The correction method is the same as formula (2), that is, completing the moving of the target channel signal to zero intermediate frequency.

[0027] In some embodiments, the method for determining a single tone signal and calculating fc1 is as follows:

[0028] Step 1: Perform FFT on the data in the window to obtain the spectrum data X(k), k = 1, ..., K, and calculate the total frequency domain energy Ex of X(k);

[0029] Step 2: Take the energy sum Emax of the frequency point with the largest amplitude in X(k) and its M left and right frequency points;

[0030] Step 3: Calculate the correlation coefficient C as follows

[0031] C=√(Emax / Ex) (3)

[0032] Step 4: Set the threshold Th. When C>Th, the window is considered to be a single-tone signal. Based on the spectrum data in Step 1, calculate the frequency of the single-tone signal.

[0033] In some embodiments, the matched filtering of the finely corrected signal data to extract the target channel IQ data includes: using a matched filter with a shaping filter coefficient of 0.5 to match filter the target channel signal already at zero intermediate frequency, completely filtering out out-of-band noise, and achieving the extraction of the target channel IQ data.

[0034] The beneficial effects that may be brought about by the method disclosed in this application include but are not limited to:

[0035] By applying the method provided in this application, TETRA signals can be correctly demodulated. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a channel extraction method based on tetra cluster signal features. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0038] On the contrary, this application covers any alternatives, modifications, equivalents, and solutions made within the spirit and scope of this application as defined by the claims. Furthermore, to facilitate a better understanding of this application, certain specific details are described in detail below in the detailed description of this application. Those skilled in the art will be able to fully understand this application without these details.

[0039] The following is a detailed description of a channel extraction method based on tetra cluster signal features involved in an embodiment of the present application.

[0040] like Figure 1 As shown, a channel extraction method based on the characteristics of Tetra cluster signals is provided. Within the frequency band of Tetra cluster signals, when the received baseband IQ data bandwidth is greater than 50kHz, there are at least one or more complete Tetra channels within the receiving bandwidth (the protocol stipulates that the channel bandwidth of Tetra cluster signals is 25kHz). Determining and extracting complete channel IQ data is a prerequisite for correct demodulation of Tetra signals. This patent provides an IQ data extraction method for the channel with the strongest signal within the receiving bandwidth. The specific implementation steps are as follows:

[0041] Step 1:

[0042] Acquire zero-IF baseband IQ data. Set the receiver center frequency and bandwidth so that the receiver collects data within the tetra frequency band and the bandwidth is greater than 50 kHz.

[0043] Step 2:

[0044] The signal is discrete Fourier transformed. Based on the received baseband IQ data within the tetra frequency band, assuming the sampling rate is fs, a discrete Fourier transform is performed with a length of N to obtain the spectrum data.

[0045] The length of N is determined by the following formula:

[0046] N>=ceil(fs*T), where T is the signal frame length. The tetra signal frame length is 14.167*4 milliseconds.

[0047] Ceil means round up.

[0048] The length of N is chosen for the following reasons:

[0049] The Tetra protocol specifies that each Tetra trunked signal frame consists of four time slots. Time slot 1 is the primary carrier control channel, hereinafter referred to as the master channel. The master channel is typically used to transmit broadcast information, which uses synchronous bursts. Each synchronization burst contains a single tone signal with a prominent amplitude in the spectrum. This characteristic allows for a preliminary estimation of the center frequency of the strongest signal. Therefore, by controlling the number of Fourier transform points to be greater than the frame length, the frequency component of the single tone signal is included in the spectrum.

[0050] Step 3:

[0051] Extract the center frequency corresponding to the maximum spectral line. Based on the spectrum data from step 2, find the maximum spectrum amplitude and calculate its corresponding center frequency fc. This center frequency fc is the carrier frequency of the channel with the strongest signal.

[0052] Assuming that the data index corresponding to the maximum spectrum amplitude is indexMax, the center frequency fc is calculated as follows:

[0053] fc=-fs / 2+fs / N*indexMax; (1)

[0054] (fs: sampling rate, N: number of discrete Fourier points)

[0055] Step 4:

[0056] Frequency coarse correction is used to move the target channel signal to the baseband. The baseband IQ data obtained in step 1 is subjected to frequency coarse correction. The correction frequency used is the carrier frequency obtained in step 3. The correction method is as follows:

[0057] dataFix=data.*exp(-1i*(2*π*fc*n / fs)); (2)

[0058] Where data is the baseband IQ data received in step 1; π is a constant; n is 0, 1, 2, ...; fs is the sampling rate; 1i: imaginary number

[0059] Step 5:

[0060] Low-pass filtering. Since the TETRA symbol rate Rb = 18kHz, and considering the carrier frequency extracted in step 2 has errors, to prevent the useful frequency from being filtered out, the low-pass filter's passband is set to twice the symbol rate. After the filter is set, the corrected data obtained in step 4 is filtered through the low-pass filter. This allows the target channel data to be obtained.

[0061] Step 6:

[0062] Fine frequency correction. After step five, most of the noise outside the target channel has been filtered out, but some noise still remains unfiltered. Here, the noise outside the target channel needs to be further filtered out. According to the characteristics of the tetra synchronization channel, there is a single-tone signal with a length of 30 symbols. Therefore, the Fourier transform window length is set to 30 symbols. The data within the sliding window range is Fourier transformed in the form of a sliding window, and it is determined whether there is only a single-tone signal in the sliding window. If there is only a single-tone signal, the single-tone frequency is calculated as fc1. According to the tetra protocol, the single-tone frequency is 2.25kHz above the carrier frequency. Based on this feature, it can be concluded that the fine carrier frequency deviation is fc2=fc1-2.25kHz. The signal obtained in step five is corrected. The correction method is the same as formula (2). At this point, the target channel signal has been moved to zero intermediate frequency.

[0063] The single tone signal judgment and fc1 calculation method are:

[0064] Step 1: Perform FFT (Fast Fourier Transform) on the data in the window to obtain the spectrum data X(k), k = 1, ..., K, and calculate the total frequency domain energy Ex of X(k);

[0065] Step 2: Take the energy sum Emax of the frequency point with the largest amplitude in X(k) and its M left and right frequency points;

[0066] Step 3: Calculate the correlation coefficient C as follows

[0067]

[0068] Step 4: Set the threshold Th. When C>Th, the window is considered to be a single-tone signal. Based on the spectrum data in Step 1, calculate the frequency of the single-tone signal. The calculation method is the same as step 3.

[0069] Step 7:

[0070] Matched filtering. The Tetra system uses a matched filter with a shaping filter coefficient of 0.5. Since the target channel signal is already at zero intermediate frequency after step 6, further matched filtering is performed on this signal to completely filter out out-of-band noise and extract the target channel IQ data.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements 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 channel extraction method based on TETRA trunking signal, characterized in that: The following steps are involved: Obtain zero-IF baseband IQ data; Performing a discrete Fourier transform on the IQ data to obtain spectrum data; Extract the center frequency corresponding to the maximum spectrum line based on the spectrum data; Perform coarse frequency correction on baseband IQ data and move the target channel signal to baseband; Performing low-pass filtering on the roughly corrected signal data; Perform fine frequency correction on the signal data after low-pass filtering; Perform matched filtering on the finely corrected signal data to extract the target channel IQ data.

2. The method according to claim 1, characterized in that The obtaining of zero-IF baseband IQ data is characterized by including: setting a center frequency and bandwidth of a receiver so that the data acquisition range of the receiver is within the tetra frequency band, and setting the bandwidth to be greater than 50 kHz.

3. The method according to claim 1, characterized in that The performing of a discrete Fourier transform on the IQ data to obtain spectrum data includes: performing a discrete Fourier transform on the received baseband IQ data within the tetra frequency band, assuming a sampling rate of fs, taking a length of N to obtain spectrum data; The length of N is determined by the following formula: N>=ceil(fs*T), where T is the signal frame length; the tetra signal frame length is 14.167*4 milliseconds; Ceil means round up.

4. The method according to claim 1, wherein The method of extracting the center frequency corresponding to the maximum spectrum line based on the spectrum data includes: finding the maximum spectrum amplitude based on the spectrum data, and calculating its corresponding center frequency fc, which is the carrier frequency at the channel with the strongest signal.

5. The method according to claim 3, characterized in that Assuming that the data index corresponding to the maximum spectrum amplitude is indexMax, the center frequency fc is calculated as follows: fc=-fs / 2+fs / N*indexMax; (1) Where fs is the sampling rate and N is the number of discrete Fourier points.

6. The method according to claim 3, characterized in that The baseband IQ data is subjected to coarse frequency correction to move the target channel signal to the baseband; including: performing coarse frequency correction on the acquired baseband IQ data, using the correction frequency as the acquired carrier frequency, and the correction method is as follows: dataFix=data.*exp(-1i*(2*π*fc*n / fs)); (2) Where data is the acquired baseband IQ data; π is a constant; n is 0, 1, 2, ...; fs is the sampling rate; and 1i is an imaginary number.

7. The method according to claim 1, characterized in that The low-pass filtering of the roughly corrected signal data includes: setting the passband width of the low-pass filter to 2 times the TETRA symbol rate. After the filter setting is completed, the obtained corrected signal data is filtered through the low-pass filter to obtain the target channel data.

8. The method according to claim 1, characterized in that The method comprises the following steps: setting the Fourier transform window length to 30 symbol lengths, performing Fourier transform on the data within the sliding window range in the form of a sliding window, and determining whether there is only a single-tone signal within the sliding window. If there is only a single-tone signal, calculating the frequency of the single tone as fc1, and correcting the obtained low-pass filtered signal data. The correction method is the same as that in formula (2), that is, completing the process of moving the target channel signal to zero intermediate frequency.

9. The method according to claim 7, characterized in that The single tone signal judgment and fc1 calculation method are: Step 1: Perform FFT on the data in the window to obtain the spectrum data X(k), k = 1, ..., K, and calculate the total frequency domain energy Ex of X(k); Step 2: Take the energy sum Emax of the frequency point with the largest amplitude in X(k) and its M left and right frequency points; Step 3: Calculate the correlation coefficient C as follows Step 4: Set the threshold Th. When C>Th, the window is considered to be a single-tone signal. Based on the spectrum data in Step 1, calculate the frequency of the single-tone signal.

10. The method according to claim 1, characterized in that The matched filtering of the finely corrected signal data to extract the target channel IQ data includes: using a matched filter with a shaping filter coefficient of 0.5 to perform matched filtering on the target channel signal already at zero intermediate frequency, completely filtering out out-of-band noise, and achieving extraction of the target channel IQ data.

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