System identification and interpretation method and system for DMR signal

Abstract

The invention relates to a DMR signal system identification and interpretation method, which comprises the following steps of: 1, receiving a DMR system cluster signal through a receiver, identifying a modulation mode of the received signal by utilizing spectrum correlation characteristics, and determining a system of the received signal according to synchronization information; and 2, demodulating the DMR signal subjected to system identification, searching synchronization information, extracting data of a voice channel or a data channel according to a synchronization code, de-interleaving the extracted data of the voice channel or the data channel, and interpreting the de-interleaved data by an AMBE vocoder to obtain voice or data information. According to the method, the DMR signal can be accurately identified in a complex communication environment, and the DMR signal can be efficiently interpreted, so that the urgent demand on DMR signal processing in the current wireless communication field can be met.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a system identification and interpretation method and system for DMR signals. Background Art

[0002] With the rapid development of wireless communication technology, DMR (Digital Mobile Radio), as an efficient and reliable digital trunked mobile communication standard, has been widely adopted in public safety, transportation, commercial communications, and other fields. However, accurately identifying and interpreting DMR signals in complex communication environments has become a pressing issue. Existing DMR signal recognition and interpretation methods often suffer from low recognition efficiency and insufficient interpretation accuracy, failing to meet the needs of practical applications. Summary of the Invention

[0003] The purpose of the present invention is to provide a system identification and interpretation method and system for DMR signals, which are used to accurately identify DMR signals in complex communication environments and efficiently interpret them, so as to meet the urgent demand for DMR signal processing in the current wireless communication field.

[0004] The present invention provides a method for identifying and interpreting a DMR signal system, comprising the following steps:

[0005] Step 1: Receive a DMR trunking signal through a receiver, identify the modulation mode of the received signal using spectrum correlation characteristics, and determine the system of the received signal based on synchronization information;

[0006] Step 2: Demodulate the DMR signal after system identification, search for synchronization information, extract the data of the voice channel or data channel according to the synchronization code, deinterleave the extracted data of the voice channel or data channel, and decode the deinterleaved data using the AMBE vocoder to decode the voice or data information.

[0007] Furthermore, the step 1 includes:

[0008] 1) According to the relationship between the autocorrelation function and the spectral density function, the following expression is obtained:

[0009]

[0010] in, is the spectral correlation function of x(t), that is, the periodic spectrum, α is the periodic frequency, f is the spectrum frequency; T is the time interval; Δt is the sampling interval; t is the continuous time variable; u is the integral interval The variable that changes within the time domain; x(t) is a continuous time signal;

[0011] The modulation mode of the signal is distinguished by the number n of delta pulses appearing on the f-axis of the periodic spectrum. Different periodic spectra have different modulation characteristics represented by delta pulses appearing on the f-axis. The number n of delta pulses present on the f-axis characterizes the frequency characteristics of the signal. For an ASK signal, there will be one delta pulse, while for a 2FSK signal, there will be two delta pulses, and for a 4FSK signal, there will be four delta pulses.

[0012] The characteristic parameter n is the spectrum The number of spectral peaks on the axis f∈[0,+∞) is obtained by extracting the peaks formed by the sinusoidal components through autoregressive modeling and spectral estimation;

[0013] Assume that the autoregressive model of the signal is:

[0014]

[0015] Among them, u(n) has a mean of 0 and a variance of σ 2 The white noise sequence, x(n) represents the p-order AR process, a(k) is the coefficient of the autoregressive model, x is the autoregressive process of the signal, k is the time delay, and its spectrum estimation value is:

[0016]

[0017] The spectrum peak is detected by the sign change of the discrete first-order difference sequence, assuming:

[0018] d p (n) = P n (n+1)-P x (n),n=0,1,…,N p -2

[0019] If d p (n)>0 and d p (n+1)<0, determine the frequency range [nf s / N p ,(n+1)f s / N p ] There is a spectral peak; the position of the spectral peak f p Calculated by linear interpolation:

[0020]

[0021] Where j is the imaginary unit; d p (n) is the discrete first-order difference sequence; P n (n) and P x (n) is the spectrum estimation value of the signal under different conditions; N p is the total number of samples; f s is the sampling frequency;

[0022] The number of detected spectral peaks is the number n of delta pulses that the spectrum exhibits on the f-axis;

[0023] 2) If four peaks are detected, the signal is considered to be modulated using 4FSK. The carrier frequency of the received signal is calculated based on the number of peaks detected and their locations to reduce the frequency deviation of the DMR signal. The center frequency is obtained by adding the values ​​of each frequency component and taking the average.

[0024] 3) If the received signal is modulated using 4FSK based on the spectrum correlation characteristics, a trial demodulation is performed. The received signal is demodulated using 4FSK according to the center frequency obtained from the spectrum peak. The synchronization information is then searched for in the demodulated data. If the synchronization information is found, the received signal is considered to be a DMR system. Otherwise, it is considered to be a conventional 4FSK modulation system rather than a DMR system.

[0025] Furthermore, the step 2 includes:

[0026] 1) Demodulate the DMR signal with correct system identification according to the 4FSK modulation method to obtain the demodulation result;

[0027] 2) Search for synchronization information according to the synchronization code to obtain synchronization information;

[0028] 3) Arrange the demodulated speech coded data in the form of bits according to the interleaving code table, input it into the matrix in horizontal rows, from the upper left to the lower right, to complete the interleaving; output the data in the form of vertical columns, from the upper left to the lower right, to complete the deinterleaving;

[0029] 4) The deinterleaved data of the speech coding is sent to the AMBE vocoder. After the deinterleaved data is decoded by the AMBE vocoder, the voice data can be restored.

[0030] The present invention also provides a system for identifying and interpreting DMR signals, comprising an identification and interpretation module, wherein the identification and interpretation module executes the system identification and interpretation method for DMR signals.

[0031] The present invention also provides a non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for system identification and interpretation of DMR signals is implemented.

[0032] The present invention further provides an electronic device, characterized in that it includes:

[0033] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for system identification and interpretation of DMR signals.

[0034] The above solution, through the method and system for system identification and interpretation of DMR signals, has the following technical effects:

[0035] 1) The present invention can accurately determine whether the received signal is a DMR system by performing modulation identification and synchronization information detection on the DMR signal, thereby enhancing the accuracy of DMR signal interpretation.

[0036] 2) The decoding part of the present invention only decodes voice channels and data signals, which greatly reduces the complexity of other channel processing and makes the decoding of DMR signals into voice or data more targeted, making the decoding faster.

[0037] 3) The present invention uses the spectrum correlation algorithm for modulation identification, which can not only successfully identify the modulation mode of the target signal, but also accurately estimate the carrier of the signal, reduce the frequency deviation, and further reduce the complexity of the calculation.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Identify a flow chart for the present invention system;

[0040] Figure 2 This is a schematic diagram related to the spectrum of the present invention;

[0041] Figure 3 Interpret the flow chart for the present invention;

[0042] Figure 4 The figure is a schematic diagram of an electronic device according to the present invention. DETAILED DESCRIPTION

[0043] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0044] This embodiment provides a method for identifying and interpreting a DMR signal system, including the following steps:

[0045] Step 1, system identification: First, the DMR system cluster signal is received through the receiver; second, the modulation mode of the received signal is identified by spectrum correlation; finally, the system of the received signal is determined based on the synchronization information. The system identification process is as follows Figure 1 As shown:

[0046] 1.1 Spectral correlation characteristics utilize the correlation characteristics between different frequency bands to reveal the periodic characteristics of the signal and the internal mechanism of signal changes due to physical processes such as modulation. The following is a detailed explanation of spectral correlation characteristics:

[0047] According to the relationship between the autocorrelation function and the spectral density function, the periodic autocorrelation function Perform Fourier transform to obtain the periodic density spectrum function:

[0048]

[0049] in, represents the periodic autocorrelation function, which can be expressed as follows:

[0050]

[0051] Bringing the periodic autocorrelation function expression into the periodic density spectrum, it can be expressed as follows:

[0052]

[0053] The periodic density spectrum function can be further simplified as:

[0054]

[0055] in,

[0056]

[0057] After transformation, we can get:

[0058]

[0059] According to the general definition of density spectrum, Indicates that x(t) is The limiting instantaneous correlation of the spectral components at , so the periodic spectral density can be expressed as:

[0060]

[0061] in, is the limit period diagram with a periodic frequency of α; X T (t,f) is the short-time complex spectrum of x(t). is the spectral correlation function of x(t), sometimes also called the periodic spectrum, where α is the periodic frequency and f is the spectral frequency.

[0062] The modulation mode of the signal is distinguished by the number n of delta pulses present on the f-axis of the periodic spectrum. Different periodic spectra have different modulation characteristics represented by delta pulses on the f-axis, such as The number n of delta pulses present on the f-axis characterizes the frequency characteristics of the signal. For an ASK signal, there will be one delta pulse, for a 2FSK signal, there will be two delta pulses, and for a 4FSK signal, there will be four delta pulses.

[0063] The characteristic parameter n is the spectrum The number of spectral peaks on the axis f∈[0,+∞) is generally obtained by extracting the peaks formed by the sinusoidal components through autoregression (AR) modeling and spectrum estimation.

[0064] Assume that the autoregressive model of the signal is:

[0065]

[0066] Among them, u(n) has a mean of 0 and a variance of σ 2 The white noise sequence x(n) represents the p-order AR process, and its spectrum estimation value is:

[0067]

[0068] The sign change of the discrete first-order difference sequence can be used to detect spectral peaks. Assume:

[0069] d p (n) = P n (n+1)-P x (n),n=0,1,…,N p -2

[0070] Among them, N p is the total number of samples, if d p (n)>0 and d p (n+1)<0, it can be determined that in the frequency range [nf s / N p ,(n+1)f s / N p ]There is a spectral peak. The position of the spectral peak is f p It can be calculated by linear interpolation:

[0071]

[0072] The number of spectrum peaks detected by the above process is the number n of delta pulses present in the spectrum on the f-axis.

[0073] 1.2 If the number of spectrum peaks detected is 4, the modulation mode of the signal is determined to be 4FSK. In addition, based on the number of spectrum peaks detected, the carrier frequency of the received signal can be calculated according to the location of the spectrum peaks, thereby reducing the frequency deviation of the DMR signal. The value of each frequency component is added and then the average is taken to obtain the intermediate frequency. Figure 2 As shown:

[0074] 1.3 If the received signal is modulated using 4FSK based on the spectrum correlation characteristics, a trial demodulation is performed. The received signal is demodulated using 4FSK according to the center frequency obtained from the spectrum peak. The synchronization information is then searched for in the demodulated data. If synchronization information is found, the received signal is considered to be a DMR system. Otherwise, it is considered to be a conventional 4FSK system rather than a DMR system.

[0075] Step 2, decoding: First, demodulate the DMR signal after system identification, then find the synchronization information, extract the data of the voice channel or data channel according to the synchronization code, then deinterleave the extracted data of the voice channel or data channel, and finally decode the deinterleaved data using the AMBE vocoder to extract the voice or data information. The decoding process is as follows: Figure 3 shown.

[0076] 2.1 Demodulation: The DMR signal with correct system identification is demodulated according to the 4FSK modulation method. The demodulation results are as follows:

[0077]

[0078] 2.2 Synchronization information search is mainly based on the synchronization code. Since the DMR system signal has synchronization information on both the voice channel and the data channel, the voice-encoded data and the data-encoded data can be found based on the synchronization information. The synchronization information is as follows:

[0079]

[0080] 2.3 Deinterleaving: As we know from the DMR signal trunking protocol, the interleaving of this system is performed according to the interleaving code table to reduce the occurrence of continuous errors during data transmission. The interleaving code table is as follows:

[0081]

[0082] Deinterleaving: Deinterleaving is done by arranging the demodulated speech coded data in bits according to the interleaving code table. The data is input into the matrix in horizontal rows, from top left to bottom right. Deinterleaving outputs the data in vertical columns, from top left to bottom right. For example, the interleaved bit order can be [0, 1, 2, ..., 71], while the deinterleaved output bit order is [0, 4, 8, ..., 71].

[0083] 2.4AMBE vocoder decoding: The deinterleaved data of the speech coding is sent to the AMBE vocoder. After the deinterleaved data is decoded by the AMBE vocoder, the voice data can be restored.

[0084] This embodiment provides an efficient and accurate method for identifying and interpreting DMR signal systems, which has the following technical effects:

[0085] 1) Improved the accuracy and efficiency of DMR signal recognition.

[0086] By introducing advanced signal processing technologies and algorithms, the present invention can achieve rapid and accurate recognition of DMR signals, improve the efficiency and accuracy of signal processing, and solve the problems of low recognition accuracy and slow processing speed often faced by existing DMR signal processing methods in complex communication environments.

[0087] 2) Automatic interpretation of DMR signals is realized.

[0088] This technology can automatically interpret DMR signals, enabling automatic decoding and information extraction, reducing the cost and risk of manual intervention. It addresses the problem that DMR signals contain rich information, but traditional interpretation methods often require manual intervention, which is time-consuming, labor-intensive, and prone to errors.

[0089] 3) Improved technological innovation in the field of wireless communications.

[0090] By applying the DMR signal system identification and interpretation method of the present invention to a wireless communication system, adaptive signal processing and efficient information transmission can be achieved, thereby improving the intelligence level and overall performance of the system.

[0091] This embodiment further provides a system for identifying and interpreting a DMR signal system, including an optimization module. The optimization module executes the method for identifying and interpreting a DMR signal system.

[0092] This embodiment further provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method for identifying and interpreting the DMR signal system is implemented.

[0093] Ginseng Figure 4 As shown, this embodiment further provides an electronic device, including:

[0094] The memory 201 and the processor 202 are communicatively connected to each other. The memory 201 stores computer instructions. The processor 202 executes the computer instructions to perform the method for identifying and interpreting a DMR signal system.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying and interpreting DMR signal systems, characterized in that: The following steps are involved: Step 1: Receive a DMR trunking signal through a receiver, identify the modulation mode of the received signal using spectrum correlation characteristics, and determine the system of the received signal based on synchronization information; Step 2: Demodulate the DMR signal after system identification, search for synchronization information, extract the data of the voice channel or data channel according to the synchronization code, deinterleave the extracted data of the voice channel or data channel, and decode the deinterleaved data using the AMBE vocoder to decode the voice or data information.

2. The method for DMR signal system identification and interpretation according to claim 1, characterized in that: The step 1 comprises: 1) According to the relationship between the autocorrelation function and the spectral density function, the following expression is obtained: in, is the spectral correlation function of x(t), that is, the periodic spectrum, α is the periodic frequency, f is the spectrum frequency; T is the time interval; Δt is the sampling interval; t is the continuous time variable; u is the integral interval The variable that changes within the time domain; x(t) is a continuous time signal; The modulation mode of the signal is distinguished by the number n of delta pulses appearing on the f-axis of the periodic spectrum. Different periodic spectra have different modulation characteristics represented by delta pulses appearing on the f-axis. The number n of delta pulses present on the f-axis characterizes the frequency characteristics of the signal. For an ASK signal, there will be one delta pulse, while for a 2FSK signal, there will be two delta pulses, and for a 4FSK signal, there will be four delta pulses. The characteristic parameter n is the spectrum The number of spectral peaks on the axis f∈[0,+∞) is obtained by extracting the peaks formed by the sinusoidal components through autoregressive modeling and spectral estimation; Assume that the autoregressive model of the signal is: Among them, u(n) has a mean of 0 and a variance of σ 2 The white noise sequence, x(n) represents the p-order AR process, a(k) is the coefficient of the autoregressive model, x is the autoregressive process of the signal, k is the time delay, and its spectrum estimation value is: The spectrum peak is detected by the sign change of the discrete first-order difference sequence, assuming: d p (n)=P n (n+1)-P x (n,n=0,1,…,N p -2 If d p (n)>0 and d p (n+1)<0, determine the frequency range [nf s / N p ,(n+1)f s / N p ] There is a spectral peak; the position of the spectral peak f p Calculated by linear interpolation: Where j is the imaginary unit; d p (n) is the discrete first-order difference sequence; P n (n) and P x (n) is the spectrum estimation value of the signal under different conditions; N p is the total number of samples; f s is the sampling frequency; The number of detected spectral peaks is the number n of delta pulses that the spectrum exhibits on the f-axis; 2) If four peaks are detected, the signal is considered to be modulated using 4FSK. The carrier frequency of the received signal is calculated based on the number of peaks detected and their locations to reduce the frequency deviation of the DMR signal. The center frequency is obtained by adding the values ​​of each frequency component and taking the average. 3) If the received signal is modulated using 4FSK based on the spectrum correlation characteristics, a trial demodulation is performed. The received signal is demodulated using 4FSK according to the center frequency obtained from the spectrum peak. The synchronization information is then searched for in the demodulated data. If the synchronization information is found, the received signal is considered to be a DMR system. Otherwise, it is considered to be a conventional 4FSK modulation system rather than a DMR system.

3. The method for DMR signal system identification and interpretation according to claim 2, characterized in that: The step 2 includes: 1) Demodulate the DMR signal with correct system identification according to the 4FSK modulation method to obtain the demodulation result; 2) Search for synchronization information according to the synchronization code to obtain synchronization information; 3) Arrange the demodulated speech coded data in the form of bits according to the interleaving code table, input it into the matrix in horizontal rows, from the upper left to the lower right, to complete the interleaving; output the data in the form of vertical columns, from the upper left to the lower right, to complete the deinterleaving; 4) The deinterleaved data of the speech coding is sent to the AMBE vocoder. After the deinterleaved data is decoded by the AMBE vocoder, the voice data can be restored.

4. A system for identifying and interpreting DMR signals, characterized in that: It comprises an identification and interpretation module, which executes the system identification and interpretation method for DMR signals as claimed in claim 1.

5. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for system identification and interpretation of DMR signals according to any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for system identification and interpretation of DMR signals according to any one of claims 1 to 3 by executing the computer instructions.

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