A high-precision power measurement method for digitally modulated communication signals

Abstract

The present invention discloses a high-precision power measurement method for digitally modulated communication signals, belonging to the field of communication reconnaissance technology. The method comprises: collecting an intermediate frequency (IF) signal, guiding down-conversion decimation filtering of a single signal according to the IF signal frequency bandwidth, and obtaining baseband IQ data of the single signal; identifying the IF signal modulation pattern according to the baseband IQ data, and determining the category to which the IF signal belongs; completing multi-dimensional spectrum calculation of the IF signal according to the category to which the IF signal belongs; performing signal energy statistics on the multi-dimensional spectrum maximum points of the IF signal to obtain relative signal power; and accurately calculating signal power based on the multi-dimensional spectrum maximum points and the relative signal power. The present invention can achieve high-precision measurement of communication signal power within a large signal-to-noise ratio range.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication reconnaissance, and in particular relates to a high-precision power measurement method for digitally modulated communication signals. Background Art

[0002] The signal power of a communication radiation source is an important one-dimensional descriptive parameter of the target object. In the field of communication reconnaissance, high-precision measurement of the power of communication radiation source signals can assist the system in deriving various reconnaissance applications and has strong military application value.

[0003] Existing power measurement methods for communication signals are generally based on the time domain or frequency domain energy statistics of the intercepted signal.

[0004] There are several shortcomings in time-domain based signal power measurement:

[0005] (1) Time domain statistics can only be applied to a single signal after filtering;

[0006] (2) The measurement accuracy is dependent on the in-band fluctuation and out-of-band suppression characteristics of the filter;

[0007] (3) When the signal-to-noise ratio is reduced, the measurement result is the sum of the signal and noise power, which cannot accurately reflect the actual signal power.

[0008] The problems with measuring signal power based on the frequency domain are:

[0009] (1) There is measurement error in the 3dB bandwidth parameter measurement of digital signals, which leads to fluctuations in the in-band power statistics;

[0010] (2) The grid effect of FFT transformation has a great influence on the power measurement of narrowband signals;

[0011] (3) After the signal-to-noise ratio is reduced, the signal power measurement value in the frequency domain is still the sum of the signal and noise power, which cannot accurately reflect the actual signal power.

[0012] Based on the actual engineering applications of existing technologies, the accuracy of communication signal power measurement methods based on time domain or frequency domain is 1 to 1.5 dB. At the same time, due to the defects of the measurement principle, the power measurement results have certain nonlinear phenomena in low signal-to-noise ratio conditions. Summary of the Invention

[0013] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a high-precision power measurement method for digitally modulated communication signals. The method aims to achieve high-precision measurement of the power of communication signals within a large signal-to-noise ratio range by adopting a low-complexity approach for digitally modulated (BPSK, QPSK, 8PSK, 16QAM, etc.) signals in blind reconnaissance conditions.

[0014] The object of the present invention is achieved through the following technical solutions:

[0015] A high-precision power measurement method for a digitally modulated communication signal, the method comprising:

[0016] Acquire an intermediate frequency signal, guide down-conversion decimation filtering of a single signal according to the frequency bandwidth of the intermediate frequency signal, and obtain baseband IQ data of the single signal;

[0017] Identifying the modulation pattern of the intermediate frequency signal according to the baseband IQ data, and determining the category to which the intermediate frequency signal belongs;

[0018] According to the category to which the intermediate frequency signal belongs, completing multi-dimensional spectrum calculation of the intermediate frequency signal;

[0019] Performing signal energy statistics on the multi-spectral maximum points of the intermediate frequency signal to obtain relative signal power;

[0020] The accurate signal power is calculated based on the multi-spectral maximum value and the relative signal power.

[0021] Furthermore, determining the category to which the intermediate frequency signal belongs includes:

[0022] Calculating the instantaneous amplitude fluctuation rate of the signal, and determining the coarse category of the intermediate frequency signal according to a first preset threshold, wherein the coarse category includes BPSK, 16QAM, and QPSK, 8PSK;

[0023] Calculate the instantaneous phase of the signal, obtain the instantaneous phase jump of the signal, and determine the sub-category of the intermediate frequency signal according to the instantaneous phase jump of the signal and a second preset threshold. The sub-category includes BPSK, 16QAM, QPSK and 8PSK.

[0024] Furthermore, the determining of the sub-category of the intermediate frequency signal according to the instantaneous phase jump of the signal and the second preset threshold specifically includes:

[0025] The signal phase jump range distribution is obtained based on a histogram statistical method, and the sub-category of the intermediate frequency signal is determined according to the phase jump range distribution in the histogram.

[0026] Furthermore, the step of calculating the multi-spectrum of the intermediate frequency signal according to the category to which the intermediate frequency signal belongs specifically includes:

[0027] The BPSK signal completes a second spectrum calculation, the QPSK or 16QAM signal completes a fourth spectrum calculation, and the 8PSK signal completes an eighth spectrum calculation.

[0028] Furthermore, the method further includes judging the validity of the multi-spectrum maximum point:

[0029] The ratio of the multi-power spectrum maximum to the spectrum mean within a preset range is calculated, and when the ratio is greater than a preset first ratio threshold, it is determined to be valid.

[0030] Furthermore, performing signal energy statistics on the multi-spectrum maximum points of the intermediate frequency signal specifically includes:

[0031] The energy of the maximum point and the points adjacent to the maximum point on both sides is statistically calculated as the relative statistical result of the signal energy.

[0032] The beneficial effects of the present invention are:

[0033] (1) The present invention uses the cyclostationary characteristics of digital modulated signals and the modulation pattern recognition results to find multi-dimensional spectrum features in the signal transformation domain for high-precision power measurement. The estimation accuracy is improved by more than one order of magnitude compared with traditional time-domain and frequency-domain algorithms.

[0034] (2) The present invention can adapt to the accurate estimation of signal power under low signal-to-noise ratio conditions, and completely avoid the nonlinear defects in traditional time-domain and frequency-domain power estimation algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flowchart of a high-precision power measurement method for digitally modulated communication signals according to an embodiment of the present application;

[0036] Figure 2 This is a distribution diagram of instantaneous amplitude fluctuation rates of signals of different modulation types according to an embodiment of the present application;

[0037] Figure 3 This is a schematic diagram of the fourth spectrum of the QPSK signal in an embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of the secondary spectrum of the BPSK signal in an embodiment of the present application;

[0039] Figure 5 This is a schematic diagram of the secondary spectrum of a 0.5 MHz frequency offset BPSK signal in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0041] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0042] Based on the actual engineering applications of existing technologies, the accuracy of communication signal power measurement methods based on time domain or frequency domain is 1 to 1.5 dB. At the same time, due to the defects of the measurement principle, the power measurement results have certain nonlinear phenomena in low signal-to-noise ratio conditions.

[0043] In order to solve the above technical problems, the present invention proposes the following embodiments of a high-precision power measurement method for digitally modulated communication signals.

[0044] Reference Figure 1 ,like Figure 1 FIG. 1 is a flow chart of a method for high-precision power measurement of a digitally modulated communication signal according to an embodiment of the present application. The method specifically includes the following steps:

[0045] Step 1: Target signal acquisition and preprocessing to remove the effects of irrelevant signals and noise on the algorithm. The intermediate frequency signal is acquired through AD, the power spectrum is calculated, and a general distribution of signals within the reconnaissance range is obtained through conventional frequency domain multi-signal search. Down-conversion and decimation filtering of a single signal is guided based on the signal frequency bandwidth to obtain baseband IQ data for the single signal.

[0046] Step 2: Complete the communication signal modulation type identification to provide guidance information for the multi-square spectrum calculation. Based on the baseband IQ data of the signal, calculate the instantaneous amplitude fluctuation rate R of the signal. R is the normalized variance of the signal envelope. The calculation formula is as follows, where r(t) = abs(s(t)), which is the instantaneous envelope of the signal:

[0047] R=var(r(t)) / mean(r(t))^2

[0048] The difference in amplitude fluctuation rate of each digital modulation signal is shown below Figure 1 By reasonably setting the classification threshold, the signal can be classified into two categories: BPSK\16QAM and QPSK\8PSK; the instantaneous phase of the signal is calculated, the instantaneous phase jump of the signal is obtained, and the phase jump range distribution of the signal is obtained based on the histogram statistics method (the phase jump between BPSK signal symbols is 180°, and the phase jump distribution between 16QAM signal symbols is relatively dispersed; the phase jump between QPSK signal symbols is 180° and 90°, while 8PSK is relatively dispersed). By reasonably setting the classification threshold, the two types of BPSK\16QAM and QPSK\8PSK signals can be further identified.

[0049] Common digital modulation baseband models can generally be expressed as

[0050]

[0051] Among them (a n +jb n ) represents the information symbol sequence, T is the symbol period, g(t) represents the shaped pulse, the most common of which is the raised cosine pulse.

[0052] Taking the commonly used QPSK modulation signal as an example, its information sequence (a n +jb n ) value set is The statistical expectation of the quartic form of the QPSK signal is

[0053] The expected value is a function with a period of T. Calculating its fourth power spectrum yields

[0054]

[0055] Where A(f) is the result of the fourth convolution of the Fourier transform of g(t).

[0056] It can be seen from V(f) that the maximum value can be obtained at the frequency {0, ±1 / T, ±2 / T}, and this value is consistent with the signal power. At the same time, the maximum value is linearly related to the fourth power energy of g(t), which greatly improves the signal energy at this point and suppresses the noise energy.

[0057] Reference Figure 2-Figure 4 ,like Figure 2 The figure shows the distribution of instantaneous amplitude fluctuation rate of different modulation types of signals in the embodiment of the present application. Figure 3 The diagram is a schematic diagram of the fourth spectrum of the QPSK signal according to the embodiment of the present application. Figure 4 The figure shows a schematic diagram of the secondary spectrum of the BPSK signal in the embodiment of the present application. As can be seen from the figure,

[0058] The frequency value of {0, ±1 / T, ±2 / T} is at least 10dB higher than the other frequency components of the original signal, making it easy to detect the frequency value. Secondly, this frequency value is a single-frequency signal, and the statistical signal energy does not need to consider the 3dB bandwidth problem, so the energy statistics are accurate. Considering that the general non-cooperative reconnaissance baseband signal has a residual frequency offset Δf, the frequency maximum value appears at {2Δf, 2Δf±1 / T, 2Δf±2 / T}. Figure 5 ,like Figure 5 The figure shows a schematic diagram of the secondary spectrum of a 0.5 MHz frequency offset BPSK signal according to an embodiment of the present application, with a residual frequency of 0.5 MHz and a sampling rate of 5 MHz.

[0059] Step 3: Based on the signal modulation type, calculate the different types of multi-dimensional spectra of the baseband IQ data. For BPSK signals, the quadratic spectrum is calculated, for QPSK / 16QAM signals, the quartic spectrum is calculated, and for 8PSK signals, the octal spectrum is calculated.

[0060] Step 4: Utilize the accumulation effect of the signal at positions {0, ±1 / T, ±2 / T} in the frequency domain after multiple power transformations to complete the energy statistics of the spectrum maximum points and obtain high-precision relative statistical results of the signal power.

[0061] Due to significant errors in the signal frequency measurement in step 1, the maximum spectral peak of the multi-order spectrum data may not occur at zero frequency. For the Nth-order spectrum, the peaks occur at {NΔf, NΔf±1 / T, NΔf±2 / T}. Therefore, a search for the maximum spectral value is necessary for the multi-order spectrum data. To improve the reliability of peak determination, the ratio of the peak value to the mean of the surrounding spectrum can be calculated. A ratio greater than 8dB is considered a valid peak.

[0062] Calculating the signal's power is equivalent to converting it into a single-tone signal. To avoid energy dispersion caused by the FFT grid effect in calculating the single-tone signal spectrum, this method calculates the energy sum of the peak point and its adjacent points to the left and right as the relative statistical result of the signal energy.

[0063] Step 5: Based on the maximum energy of the signal multi-spectrum and the signal power The relationship is linear, the fixed power deviation is removed, and the accurate power of the signal is calculated.

[0064] Since the statistical value of signal energy in the digital processing result is related to many factors such as the shaping filter g(t) at the transmitter, the number of AD quantization bits at the receiver, and the down-conversion DDC processing gain, it is difficult to obtain it through theoretical deduction. In engineering implementation, it is recommended to obtain it through multiple measurement statistics. As a fixed deviation, the signal energy measurement value is corrected in the final result to calculate the absolute signal power.

[0065] This embodiment leverages the cyclostationary properties of digitally modulated signals and modulation pattern recognition results to identify multi-dimensional spectrum features in the signal transform domain for high-precision power measurement. This method improves estimation accuracy by more than an order of magnitude compared to traditional time- and frequency-domain algorithms. This method can accurately estimate signal power in low signal-to-noise ratio (SNR) conditions, completely avoiding the nonlinear flaws inherent in traditional time- and frequency-domain power estimation algorithms.

[0066] 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 high-precision power measurement method for digitally modulated communication signals, characterized in that: The method comprises: Acquire an intermediate frequency signal, guide down-conversion decimation filtering of a single signal according to the frequency bandwidth of the intermediate frequency signal, and obtain baseband IQ data of the single signal; Identifying the modulation pattern of the intermediate frequency signal according to the baseband IQ data, and determining the category to which the intermediate frequency signal belongs; According to the category to which the intermediate frequency signal belongs, completing multi-dimensional spectrum calculation of the intermediate frequency signal; Performing signal energy statistics on the multi-spectral maximum points of the intermediate frequency signal to obtain relative signal power; Calculating accurate signal power based on the multi-spectral maximum value and the relative signal power; The determining of the category to which the intermediate frequency signal belongs includes: calculating the instantaneous amplitude fluctuation rate of the signal, and determining the coarse category of the intermediate frequency signal according to a first preset threshold, wherein the coarse categories include BPSK, 16QAM, and QPSK, and 8PSK; calculating the instantaneous phase of the signal, obtaining the instantaneous phase jump of the signal, and determining the fine category of the intermediate frequency signal according to the instantaneous phase jump of the signal and a second preset threshold, wherein the fine categories include BPSK, 16QAM, QPSK, and 8PSK; Determining the sub-category of the intermediate frequency signal according to the instantaneous phase jump of the signal and the second preset threshold specifically includes: obtaining a signal phase jump range distribution based on a histogram statistics method, and determining the sub-category of the intermediate frequency signal according to the phase jump range distribution in the histogram; The performing of the multi-order spectrum calculation of the intermediate frequency signal according to the category to which the intermediate frequency signal belongs specifically includes: performing a quadratic spectrum calculation for a BPSK signal, performing a quartic spectrum calculation for a QPSK or 16QAM signal, and performing an octal spectrum calculation for an 8PSK signal; The performing signal energy statistics on the multi-spectrum maximum point of the intermediate frequency signal specifically includes: calculating the energy of the maximum point and adjacent points on both sides of the maximum point as a relative statistical result of the signal energy.

2. The high-precision power measurement method for digitally modulated communication signals according to claim 1, wherein: The method further includes judging the validity of the multi-dimensional spectrum maximum point: calculating the ratio of the multi-dimensional spectrum maximum to the spectrum mean within a preset range, and judging it as valid when the ratio is greater than a preset first ratio threshold.

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