Method, device and equipment for measuring phase nonlinearity of digital phased array receiving channel

Abstract

The invention discloses a method, a device and equipment for measuring the phase nonlinearity of a digital phased array receiving channel, and the method comprises the steps: generating a plurality of baseband linear frequency modulation signals with different center frequencies, and obtaining a broadband radio frequency linear frequency modulation signal set; inputting the signals into a digital phased array receiving channel at the same time to obtain a multi-path baseband data set; according to the real-time total delay of the receiving channel, performing integral multiple clock period delay on each signal data in the baseband data set to obtain a delay linear frequency modulation signal set; according to the multi-path baseband data set and the delay linear frequency modulation signal set, determining a peak position of each datum in the correlation peak data set through a peak search algorithm model, and further calculating a phase difference set; and removing a first-order linear component and a direct-current component in the phase difference set by adopting a dechirp algorithm to obtain a phase nonlinear set of the digital phased array receiving channel. According to the invention, nonlinear, efficient, accurate and low-cost measurement of the phase of the digital phased array receiving channel can be realized.

Description

Technical Field

[0001] The present invention relates to the field of antenna technology, and in particular to a method, device and equipment for measuring phase nonlinearity of a digital phased array receiving channel. Background Art

[0002] In the field of phase nonlinear measurement of digital phased array receiving channels, existing technical solutions mainly include the following common practices:

[0003] Traditional measurement methods are usually based on vector network analyzers. This method evaluates phase nonlinearity by sending a specific test signal to the system under test and measuring the amplitude and phase information of the reflected or transmitted signal. However, it has obvious limitations when applied to digital phased array receiving channels. Vector network analyzers are mainly suitable for measuring linear and passive networks. For digital phased array receiving channels that include analog-to-digital hybrid systems and frequency conversion systems, it cannot directly measure phase nonlinearity. In addition, this method has low test efficiency, requires measurement at each frequency point, and is costly because the vector network analyzer itself is a high-precision, high-cost instrument.

[0004] Another existing technology is a phase nonlinearity measurement method based on the digital domain. This method usually requires a high sampling rate analog-to-digital converter (ADC) and high-performance digital signal processing (DSP) hardware. It first samples the input signal at high speed, and then calculates the phase nonlinearity through a complex digital signal processing algorithm. However, this method has high hardware requirements. It not only requires the ADC to have an extremely short sampling interval, but also requires a powerful DSP to process a large amount of data. This not only increases the complexity of the system, but also increases the cost. In addition, due to the short sampling interval, the system's clock accuracy and stability requirements are also very high, otherwise additional measurement errors will be introduced.

[0005] Therefore, when measuring the phase nonlinearity of the digital phased array receiving channel, the existing technology has problems such as being unable to directly measure the phase nonlinearity of the analog-to-digital mixing and frequency conversion system, low test efficiency, high cost, high hardware requirements, and short sampling interval leading to system complexity. It is difficult to meet the needs of efficient, accurate and low-cost measurement of the phase nonlinearity of the digital phased array receiving channel in practical applications. Summary of the invention

[0006] The embodiments of the present invention provide a method, device and equipment for measuring phase nonlinearity of a digital phased array receiving channel, which can achieve efficient, accurate and low-cost measurement of phase nonlinearity of a digital phased array receiving channel.

[0007] An embodiment of the present invention provides a method for measuring phase nonlinearity of a digital phased array receiving channel, comprising:

[0008] Generate multiple baseband linear frequency modulation signals with different center frequencies, and perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a set of broadband radio frequency linear frequency modulation signals;

[0009] Input each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel at the same time, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0010] According to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband I / Q data set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set;

[0011] According to the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set, and through the peak search algorithm model, the peak position of each data in the correlation peak data set is measured;

[0012] According to the peak position of each data in the correlation peak data set, a phase difference set is calculated;

[0013] A de-skewing algorithm is used to remove the first-order linear component and the direct current component of the phase difference set, so as to obtain a phase nonlinear set of the digital phased array receiving channel.

[0014] As an improvement of the above scheme, the peak position of each data in the correlation peak data set is predicted based on the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model, including:

[0015] Perform multi-dimensional cross-correlation operation on each signal data in the multi-channel baseband I / Q data set and the signal in the corresponding delayed linear frequency modulation signal set to obtain a correlation peak data set;

[0016] The peak search algorithm model is used to perform peak search on each data in the relevant peak data set, and the peak position of each data is predicted.

[0017] As an improvement of the above solution, the phase difference set is calculated according to the peak position of each data in the correlation peak data set, including:

[0018] Perform interpolation operation at the peak position of each data in the correlation peak data set to obtain a delay set at the peak;

[0019] Generate a synchronous linear frequency modulation signal set based on the delay set and the dynamic integer multiple delay;

[0020] Performing phase calculations on corresponding signal data in both the synchronous linear frequency modulation signal set and the multi-channel baseband I / Q data set to obtain phase calculation result sets of the two;

[0021] According to the phase calculation result sets of the two, a phase difference set of the two is calculated.

[0022] As an improvement of the above solution, the de-skewing algorithm is used to remove the first-order linear component and the DC component of the phase difference set to obtain the phase nonlinear set of the digital phased array receiving channel, including:

[0023] For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n (n)} is subjected to fast Fourier transform to obtain the frequency domain representation ΔΦ(f); where Δφ is i The discrete Fourier transform of (n) is ΔΦ i (f) then N is the data length, that is, the number of data points in the phase difference set; f is the frequency variable, used for frequency domain analysis; n represents the discrete time point index in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at the discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at the corresponding frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at the corresponding frequency f in the frequency domain after discrete Fourier transform, which is the value of all ΔΦ i (f) the sum of;

[0024] Analyze the frequency domain data to determine the characteristics of the first-order linear component and the DC component; the DC component characteristics: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristics: observe the changing trend of the frequency domain data in the low-frequency part; assume that the first-order linear component is expressed in the form of a1f+b1 in the frequency domain, and determine the coefficients a1 and b1 by fitting the data of the low-frequency part; a1 represents the slope of the first-order linear component in the frequency domain, and b1 is a constant term;

[0025] Design a high-pass filter to remove the DC component: where the high-pass filter transfer function is where f cutoff is the cutoff frequency of the high-pass filter; the frequency domain data after filtering by the high-pass filter is ΔΦ filtered (f)=ΔΦ(f)H hp (f); H hp (f) is the transfer function of the high-pass filter; f is the input frequency; when the input frequency f>f cutoffWhen f≤f cutoff When , the transfer function value is 0, indicating that the input signal of this frequency is completely attenuated;

[0026] Design a linear phase filter to remove the first-order linear component: The transfer function of the linear phase filter is designed to be H lp (f) = exp(-j2π(a2f+b2)), H lp (f) is the transfer function of the linear phase filter; where a2 is a frequency-dependent coefficient, b2 is the slope estimate of the first-order linear component to be removed, obtained by linear fitting of the low-frequency frequency points; the design purpose of this transfer function is to adjust according to the slope of the first-order linear component to remove the first-order linear component; where the coefficient a2 = -a1, b2 is adjusted as needed to ensure the linear phase characteristics of the filter; the filtered frequency domain data is again passed through the filter to obtain ΔΦ final (f) = ΔΦ filtered (f)H lp (f);

[0027] For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the de-skewed time domain data ΔΦ deskewed (n), thus obtaining the phase nonlinear data of the digital phased array receiving channel after removing the first-order linear component and DC component. The calculation formula is:

[0028] As an improvement of the above scheme, the method generates a plurality of baseband linear frequency modulation signals with different center frequencies, and performs separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a broadband radio frequency linear frequency modulation signal set, including:

[0029] A signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies; for each baseband linear frequency modulation signal a i (t), which is expressed as A i represents the amplitude of the ith signal, which determines the strength of the signal. The amplitude is selected based on actual needs and the dynamic range of the system; f 0i is the starting frequency of the ith signal. Different starting frequencies make each signal cover different frequency ranges; μ iis the FM slope of the ith signal, which determines the frequency change rate of the signal over time; the FM slope is determined by combining the bandwidth and time duration of the signal to meet the requirements of the time-bandwidth product; t is a time variable, which represents the change of the signal over time; the bandwidth of each signal is assumed to be the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel; where the time-bandwidth product meets a specific optimization threshold, which is assumed to be (360 / δ) 2 / 8, where δ is the phase nonlinear measurement error; the time bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the duration and frequency modulation slope of the signal, the time-bandwidth product meets the threshold requirement, thereby improving the measurement accuracy;

[0030] Each baseband linear frequency modulation signal is individually subjected to digital-to-analog conversion, up-conversion, filtering and signal amplification processing to obtain a broadband radio frequency linear frequency modulation signal set.

[0031] As an improvement of the above scheme, the respective signals in the broadband RF linear frequency modulation signal set are simultaneously input into the digital phased array receiving channel, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set, including:

[0032] The broadband RF linear frequency modulation signal set B = {b1(t), b2(t), ..., b n (t)} are simultaneously input into the digital phased array receiving channel; this is achieved by a multiplexer or parallel connection to ensure that each signal enters the receiving channel at the same time;

[0033] Each input signal is processed independently, including down-conversion, filtering, signal amplification and digital preprocessing, to obtain a multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)}; n represents a discrete time point, each c i (n) corresponds to the input signal b i (t) processed baseband data; wherein the down-conversion operation converts the RF signal into a baseband signal; for signal b i (t), down-conversion is achieved by mixing with the signal generated by the local oscillator; let the frequency of the local oscillator be f LOi , then the down-converted signal can be expressed as in, Represents the signal after down-conversion, which is the input RF signal b i (t) The baseband signal obtained after mixing with the local oscillator signal; b i (t) is the input RF signal, exp(-j2πfLOi t) is the complex exponential signal generated by the local oscillator; f LOi is the frequency of the local oscillator, which is used to down-convert the RF signal to baseband; t is a time variable, which represents the change of the signal over time; the filtering operation is to use a digital filter or an analog filter to remove unnecessary frequency components and improve the purity of the signal; the signal amplification is to use an amplifier to linearly amplify the signal to enhance the strength of the signal; the digital preprocessing is achieved through an analog-to-digital converter to convert the analog signal into a digital signal.

[0034] As an improvement of the above scheme, according to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband I / Q data set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set, including:

[0035] Each signal in the baseband linear frequency modulation signal set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set; the delay time is dynamically adjusted according to the real-time total delay of the correction source generation module and the digital phased array receiving channel; wherein, the clock cycle is assumed to be T c , for each signal a in the baseband linear frequency modulation signal set i (t), the delayed signal d i (t) is expressed as d i (t) is the delayed signal, which is the signal obtained by delaying the baseband linear frequency modulation signal by a dynamic integer multiple clock cycle; A i is the amplitude of the ith signal, which is consistent with the amplitude parameter in the baseband linear frequency modulation signal and determines the strength of the signal; μ i is the frequency modulation slope of the i-th signal, which is consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal and determines the frequency change rate of the signal over time; t is the time variable, indicating the change of the signal over time; k i is the dynamic integer delay multiple of the i-th signal, which is used to adjust the delay time so that the delayed signal is aligned with the signal processed by the receiving channel in time; the dynamic integer delay multiple k i The method of determining is as follows:

[0036] First, measure the real-time total delay T between the correction source generation module and the digital phased array receiving channel. delay ; By inserting a time measurement module into the system, or by estimating using known signal propagation time and processing time;

[0037] Then, calculate the dynamic integer delay multiple in Represents a floor function that ensures that the delayed signal is aligned as closely as possible in time with the signal processed by the receiving channel.

[0038] Another embodiment of the present invention provides a device for measuring phase nonlinearity of a digital phased array receiving channel, including:

[0039] A signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies, and perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a set of broadband RF linear frequency modulation signals;

[0040] A signal processing module is used to simultaneously input each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0041] A signal delay module is used to dynamically delay each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulation signal set;

[0042] A peak measurement module is used to measure the peak position of each data in the correlation peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model;

[0043] A calculation module, used for calculating a phase difference set according to the peak position of each data in the correlation peak data set;

[0044] The removal module is used to remove the first-order linear component and the DC component of the phase difference set by using a de-skewing algorithm to obtain a phase nonlinear set of the digital phased array receiving channel.

[0045] Another embodiment of the present invention provides a device for measuring phase nonlinearity of a digital phased array receiving channel, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for measuring phase nonlinearity of a digital phased array receiving channel described in the above-mentioned embodiment of the invention is implemented.

[0046] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0047] First, multiple baseband linear frequency modulation signals with different center frequencies are generated, and digital-to-analog conversion, up-conversion, filtering and signal amplification are performed on them to obtain a set of broadband RF linear frequency modulation signals. The advantage of this is that it can cover a wider frequency range and more comprehensively detect the phase nonlinear characteristics of the digital phased array receiving channel through multiple signals of different frequencies. Compared with the traditional method, this scheme can better adapt to the complex system of the digital phased array because it can generate signals suitable for the system. Then, these broadband RF linear frequency modulation signals are simultaneously input into the digital phased array receiving channel, and the receiving channel performs down-conversion, filtering, signal amplification and digital preprocessing on each input signal to obtain a multi-channel baseband I / Q data set. Inputting multiple signals at the same time and performing parallel processing greatly improves the measurement efficiency and avoids the low efficiency of measuring signals one by one in the traditional method. Moreover, through a series of preprocessing operations, more accurate baseband data can be obtained. Then, according to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband data set is delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set. This dynamic delay adjustment can ensure that the signal is accurately aligned in time, thereby providing an accurate basis for subsequent phase calculation. This solves the measurement error problem caused by signal asynchrony in the prior art. Afterwards, the peak position of each data in the correlation peak data set is determined by using a peak search algorithm model through a multi-channel baseband I / Q data set and a delayed linear frequency modulation signal set. This precise peak search algorithm can accurately find the correlation peak between signals, thereby improving the measurement accuracy. Finally, the phase difference set is calculated according to the peak position, and the de-skewing algorithm is used to remove the first-order linear component and the DC component to obtain the phase nonlinear set of the digital phased array receiving channel. The application of the de-skewing algorithm effectively removes interference factors, so that the measurement results more accurately reflect the phase nonlinear characteristics of the digital phased array receiving channel. From the above analysis, it can be seen that the embodiment of the present invention overcomes the limitations of the prior art through the generation of multi-frequency signals, parallel processing, dynamic delay adjustment, precise peak search and effective de-skewing algorithm, and provides a reliable and efficient solution for the performance evaluation and calibration of the digital phased array receiving channel. Therefore, the embodiment of the present invention can achieve efficient, accurate and low-cost measurement of the phase nonlinearity of the digital phased array receiving channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for measuring phase nonlinearity of a digital phased array receiving channel provided by one embodiment of the present invention;

[0049] Figure 2 It is a structural schematic diagram of a device for measuring phase nonlinearity of a digital phased array receiving channel provided by one embodiment of the present invention;

[0050] Figure 3It is a structural schematic diagram of a digital phased array receiving channel phase nonlinearity measurement device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 , is a flow chart of a method for measuring phase nonlinearity of a digital phased array receiving channel provided by an embodiment of the present invention. The method for measuring phase nonlinearity of a digital phased array receiving channel comprises steps S10 to S15:

[0053] S10, generating a plurality of baseband linear frequency modulation signals with different center frequencies, and performing separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a broadband radio frequency linear frequency modulation signal set;

[0054] S11, inputting each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel at the same time, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0055] S12, dynamically delaying each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel, to obtain a delayed linear frequency modulation signal set;

[0056] S13, according to the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set, and through the peak search algorithm model, measuring the peak position of each data in the correlation peak data set;

[0057] S14, calculating a phase difference set according to the peak position of each data in the correlation peak data set;

[0058] S15, using a de-skewing algorithm to remove the first-order linear component and the DC component of the phase difference set to obtain a phase nonlinear set of the digital phased array receiving channel.

[0059] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0060] First, multiple baseband linear frequency modulation signals with different center frequencies are generated, and digital-to-analog conversion, up-conversion, filtering and signal amplification are performed on them to obtain a set of broadband RF linear frequency modulation signals. The advantage of this is that it can cover a wider frequency range and more comprehensively detect the phase nonlinear characteristics of the digital phased array receiving channel through multiple signals of different frequencies. Compared with the traditional method, this scheme can better adapt to the complex system of the digital phased array because it can generate signals suitable for the system. Then, these broadband RF linear frequency modulation signals are simultaneously input into the digital phased array receiving channel, and the receiving channel performs down-conversion, filtering, signal amplification and digital preprocessing on each input signal to obtain a multi-channel baseband I / Q data set. Inputting multiple signals at the same time and performing parallel processing greatly improves the measurement efficiency and avoids the low efficiency of measuring signals one by one in the traditional method. Moreover, through a series of preprocessing operations, more accurate baseband data can be obtained. Then, according to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband data set is delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set. This dynamic delay adjustment can ensure that the signal is accurately aligned in time, thereby providing an accurate basis for subsequent phase calculation. This solves the measurement error problem caused by signal asynchrony in the prior art. Afterwards, the peak position of each data in the correlation peak data set is determined by using a peak search algorithm model through a multi-channel baseband I / Q data set and a delayed linear frequency modulation signal set. This precise peak search algorithm can accurately find the correlation peak between signals, thereby improving the measurement accuracy. Finally, the phase difference set is calculated according to the peak position, and the de-skewing algorithm is used to remove the first-order linear component and the DC component to obtain the phase nonlinear set of the digital phased array receiving channel. The application of the de-skewing algorithm effectively removes interference factors, so that the measurement results more accurately reflect the phase nonlinear characteristics of the digital phased array receiving channel. From the above analysis, it can be seen that the embodiment of the present invention overcomes the limitations of the prior art through the generation of multi-frequency signals, parallel processing, dynamic delay adjustment, precise peak search and effective de-skewing algorithm, and provides a reliable and efficient solution for the performance evaluation and calibration of the digital phased array receiving channel. Therefore, the embodiment of the present invention can achieve efficient, accurate and low-cost measurement of the phase nonlinearity of the digital phased array receiving channel.

[0061] As an improvement of the above embodiment, the method of generating a plurality of baseband linear frequency modulation signals with different center frequencies, and performing separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a broadband radio frequency linear frequency modulation signal set includes:

[0062] A signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies; for each baseband linear frequency modulation signal a i (t), which is expressed as A represents the amplitude of the ith signal, which determines the strength of the signal. The amplitude is selected based on actual needs and the dynamic range of the system; f 0i is the starting frequency of the ith signal. Different starting frequencies make each signal cover different frequency ranges; μ i is the FM slope of the ith signal, which determines the frequency change rate of the signal over time; the FM slope is determined by combining the bandwidth and time duration of the signal to meet the requirements of the time-bandwidth product; t is a time variable, which represents the change of the signal over time; the bandwidth of each signal is assumed to be the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel; where the time-bandwidth product meets a specific optimization threshold, which is assumed to be (360 / δ) 2 / 8, where δ is the phase nonlinear measurement error; the time bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the duration and frequency modulation slope of the signal, the time-bandwidth product meets the threshold requirement, thereby improving the measurement accuracy;

[0063] Each baseband linear frequency modulation signal is individually subjected to digital-to-analog conversion, up-conversion, filtering and signal amplification processing to obtain a broadband radio frequency linear frequency modulation signal set.

[0064] In this embodiment, a signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies, and then a set of broadband RF linear frequency modulation signals is obtained through a series of processing steps to provide a basic input signal for the subsequent digital phased array receiving channel phase nonlinear measurement. The measurement accuracy is improved by reasonably setting the signal parameters and meeting the specific time-width bandwidth product threshold requirements. At the same time, each baseband linear frequency modulation signal is processed separately to ensure the accuracy and stability of the signal. Among them, a separate digital-to-analog conversion is performed on each baseband linear frequency modulation signal to convert the digital signal into an analog signal. Then an up-conversion operation is performed to increase the signal frequency to the RF band. Then, the unnecessary frequency components are removed through filtering to ensure the signal purity. Finally, the signal is amplified to obtain a set of broadband RF linear frequency modulation signals.

[0065] For ease of understanding, an example is given here: For example, in the field of wireless communications, the performance of a digital phased array receiving channel is critical to the quality of communication. In this embodiment, a plurality of baseband linear frequency modulation signals with different center frequencies are first generated. Assuming that a specific frequency range needs to be covered, different starting frequencies f are set according to actual conditions. 0i and frequency modulation slope μ i. At the same time, ensure that the time-bandwidth product meets the threshold requirements to improve measurement accuracy. For example, in a communication system, the phase nonlinear measurement error is required to be as small as possible. By adjusting the signal duration and frequency modulation slope, the time-bandwidth product meets a specific optimization threshold. These baseband signals are then subjected to digital-to-analog conversion, up-conversion, filtering, and signal amplification processing to obtain a set of broadband RF linear frequency modulation signals. These signals enter the digital phased array receiving channel as input signals for subsequent phase nonlinear measurement and analysis, so that the receiving channel can be optimized and calibrated, the performance of the communication system can be improved, signal distortion and interference can be reduced, and communication quality and reliability can be improved.

[0066] As an improvement of the above embodiment, the respective signals in the broadband RF linear frequency modulation signal set are simultaneously input into the digital phased array receiving channel, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set, including:

[0067] The broadband RF linear frequency modulation signal set B = {b1(t), b2(t), ..., b n (t)} are simultaneously input into the digital phased array receiving channel; this is achieved by a multiplexer or parallel connection to ensure that each signal enters the receiving channel at the same time;

[0068] Each input signal is processed independently, including down-conversion, filtering, signal amplification and digital preprocessing, to obtain a multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)}; n represents a discrete time point, each c i (n) corresponds to the input signal b i (t) processed baseband data; wherein the down-conversion operation converts the RF signal into a baseband signal; for signal b i (t), down-conversion is achieved by mixing with the signal generated by the local oscillator; let the frequency of the local oscillator be f LOi , then the down-converted signal can be expressed as in, Represents the signal after down-conversion, which is the input RF signal b i (t) The baseband signal obtained after mixing with the local oscillator signal; b i (t) is the input RF signal, exp(-j2πf LOi t) is the complex exponential signal generated by the local oscillator; f LOiis the frequency of the local oscillator, which is used to down-convert the RF signal to baseband; t is a time variable, which represents the change of the signal over time; the filtering operation is to use a digital filter or an analog filter to remove unnecessary frequency components and improve the purity of the signal; the signal amplification is to use an amplifier to linearly amplify the signal to enhance the strength of the signal; the digital preprocessing is achieved through an analog-to-digital converter to convert the analog signal into a digital signal.

[0069] In this embodiment, each signal in the broadband RF linear frequency modulation signal set is simultaneously input into the digital phased array receiving channel, and a series of independent processing steps are performed on each input signal through a parallel processing architecture to obtain a multi-channel baseband data set, providing an accurate data basis for subsequent phase nonlinear measurement. Among them, a multiplexer or parallel connection method is used to ensure that the signals are input simultaneously, and each signal is processed independently, thereby improving processing efficiency and data accuracy.

[0070] For ease of understanding, an example is given here: for example, in a radar system, a digital phased array receiving channel needs to process multiple different input signals. Through this embodiment, a broadband RF linear frequency modulation signal is simultaneously input into the receiving channel. Assume that there are multiple targets that need to be detected, and different RF signals correspond to different target echo signals. Through a parallel processing architecture, each signal is independently down-converted, filtered, amplified, and digitally preprocessed. For example, for a specific RF signal, it is mixed with a local oscillator and down-converted to baseband, and then passed through a suitable filter to remove clutter, and then amplified and converted into a digital signal through an analog-to-digital converter. The resulting multi-channel baseband data set can be used for subsequent phase nonlinear analysis, thereby accurately measuring the performance of the receiving channel, optimizing the radar system's ability to detect and track targets, and improving the reliability and accuracy of the radar system.

[0071] As an improvement of the above embodiment, the method of dynamically delaying each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulation signal set includes:

[0072] Each signal in the baseband linear frequency modulation signal set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set; the delay time is dynamically adjusted according to the real-time total delay of the correction source generation module and the digital phased array receiving channel; wherein, the clock cycle is assumed to be T c , for each signal a in the baseband linear frequency modulation signal set i (t), the delayed signal d i (t) is expressed as d i (t) is the delayed signal, which is the signal obtained by delaying the baseband linear frequency modulation signal by a dynamic integer multiple clock cycle; Ai is the amplitude of the ith signal, which is consistent with the amplitude parameter in the baseband linear frequency modulation signal and determines the strength of the signal; μ i is the frequency modulation slope of the i-th signal, which is consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal and determines the frequency change rate of the signal over time; t is the time variable, indicating the change of the signal over time; k i is the dynamic integer delay multiple of the i′th signal, which is used to adjust the delay time so that the delayed signal is aligned with the signal processed by the receiving channel in time; the dynamic integer delay multiple k i The method of determining is as follows:

[0073] First, measure the real-time total delay T between the correction source generation module and the digital phased array receiving channel. delay ; By inserting a time measurement module into the system, or by estimating using known signal propagation time and processing time;

[0074] Then, calculate the dynamic integer delay multiple in Represents a floor function that ensures that the delayed signal is aligned as closely as possible in time with the signal processed by the receiving channel.

[0075] In this embodiment, according to the real-time total delay of the digital phased array receiving channel, the signal in the baseband data set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set, so that the delayed signal is aligned in time with the signal processed by the receiving channel, and is prepared for the subsequent phase nonlinear calculation. Among them, by adopting a dynamic delay adjustment method, it is possible to adapt to different system delay changes and improve the accuracy and stability of the measurement. At the same time, by accurately calculating the dynamic integer delay multiple, it is ensured that the signal is aligned as close to time as possible.

[0076] For ease of understanding, an example is given here: for example, in a communication system, the digital phased array receiving channel may cause unstable signal transmission delay due to various factors. Through this embodiment, the delay of the signal is dynamically adjusted according to the real-time total delay. Assume that in a complex communication environment, the real-time total delay of the correction source generation module and the receiving channel is constantly changing. By measuring this total delay, the dynamic integer delay multiple is calculated, and the baseband signal is accurately delayed. In this way, even in a dynamically changing environment, it can be ensured that the delayed signal is aligned in time with the signal processed by the receiving channel, so that the phase nonlinearity calculation can be accurately performed. This helps to improve the performance of the communication system, reduce signal distortion and interference, and ensure the stability and reliability of communication.

[0077] As an improvement of the above embodiment, the peak position of each data in the correlation peak data set is predicted based on the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model, including:

[0078] Each signal data in the multi-channel baseband I / Q data set is subjected to a multi-dimensional cross-correlation operation with the signal in the corresponding delayed linear frequency modulation signal set to obtain a correlation peak data set; in this step, specifically, the time delay relationship between the multi-channel baseband II / Q data and the delayed linear frequency modulation signal is found through the multi-dimensional cross-correlation operation, which provides a basis for the subsequent phase nonlinear calculation; for the multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)} and the delayed linear frequency modulation signal set D = {d1(t), d2(t), ..., d n (t)}, perform the following operations: for each d i (t) is flipped in time domain, and we get The meaning here is to reverse the signal on the time axis so as to perform cross-correlation operation later; i (n) is conjugated to obtain The purpose of taking conjugate is to achieve signal matching in the cross-correlation operation; then a multi-dimensional convolution operation is performed, that is, Here i (n) represents the correlation peak data set E = {e1(n), e2(n), ..., e n (n)}, which is obtained by taking the conjugate baseband data and the delayed linear frequency modulation signal after inversion The result obtained by performing multi-dimensional convolution operation; this result reflects the correlation between the multi-channel baseband data and the delayed linear frequency modulation signal, and its peak position will be used for subsequent time delay estimation and phase nonlinearity calculation; n represents a discrete time point, which is used to traverse the correlation peak data in the time domain and determine the correlation degree at different time points; m is a variable used to traverse the conjugated baseband data. In the convolution operation, it and the time point n together determine the product and accumulation of the two signals at different time points; is a multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)} i (n) The result obtained after taking the conjugate. The conjugate operation is to achieve signal matching in the cross-correlation operation so that the two signals can better reflect the similarity between them in the convolution operation; is a delayed linear frequency modulation signal set D = {d1(t), d2(t), ..., d n (t)}i (t) is the result obtained after time domain flipping. Time domain flipping is to reverse the signal on the time axis so that it can be matched with the conjugated baseband data in the convolution operation to find the time delay relationship between the two signals; this formula means that the convolution operation is performed on the conjugated baseband data and the flipped delayed linear frequency modulation signal to obtain the correlation peak data. The convolution operation can be regarded as the process of matching and integrating two signals in the time domain. In this way, the similarity and time delay between the two signals can be found.

[0079] The peak search algorithm model is used to perform peak search on each data in the related peak data set, and the peak position of each data is predicted; in this step, specifically, an intelligent peak search algorithm is used to perform peak search on each data in the related peak data set; the algorithm combines the use of support vector machine (SVM) or random forest algorithms to classify and predict the peaks of the related peak data; specifically, the features of the related peak data, such as peak height, width, symmetry, etc., are extracted as input features, and then the peak position is predicted through the trained machine learning model.

[0080] The following are the specific implementation steps of the intelligent peak search algorithm of this embodiment:

[0081] 1. Data preprocessing: Normalize the correlation peak data set and map the data values ​​to the [0,1] interval for subsequent analysis and comparison. i (n), the maximum value is e max , the minimum value is e min , then the normalized data

[0082] 2. Feature extraction: Extract the features of the relevant peak data, which may include but are not limited to the following aspects:

[0083] Peak height H: Calculate the maximum value in the data as the peak height H: H = max{e i (n)}, where e i (n) is an element in the correlation peak data set, n represents a discrete time point, and H represents the maximum value in the data, that is, the peak height.

[0084] Peak width W: The peak width W is determined by finding the position where both sides of the peak drop to a certain ratio (e.g. half the peak height): First find the peak position n p Make e i (n p )=H. Let half of the peak height be H / 2, and search from the peak position to both sides to find a value that satisfies e i (n1) = H / 2 and e i(n2) = H / 2 positions n1 and n2 (n1 < n p <n2). Peak width W=n2-n1.

[0085] Symmetry S: Calculates the degree of symmetry of the data on both sides of the peak, which can be measured by comparing the difference in data values ​​at corresponding positions on both sides of the peak. Calculate the sum of the differences on the right side symmetry When the left and right sides are completely symmetrical, S=1. The greater the difference, the closer S is to 0.

[0086] Local change rate R: Calculate the change rate of the data near the peak value, reflecting the sharpness of the peak. Among them, a small range [n p -Δn,n p +Δn], calculate the sum of the absolute values ​​of the first-order differences of the data within this range

[0087] 3. Machine Learning Model Training

[0088] Prepare training data: Collect a large number of relevant peak data samples with known peak positions, extract the above features as input features, and use the corresponding peak positions as output labels.

[0089] Select machine learning algorithm: You can choose support vector machine (SVM), random forest, neural network and other algorithms. Here we take random forest as an example.

[0090] Training model: Use training data to train the random forest model and adjust the model parameters to improve prediction accuracy. Training phase: Assume there are N training samples, each sample consists of a feature vector x k =(H k , W k , S k , R k ) and the corresponding peak position y k Labels. Random forests are composed of multiple decision trees. For the training of each tree, n samples (usually n < N) are randomly selected from N samples with replacement as the training set of the tree. When each node is split, a part of the features is randomly selected from the four features of the feature vector (peak height, peak width, symmetry, local change rate), and the best split features and split points are selected based on indicators such as information gain. Repeat the above process to construct multiple decision trees to form a random forest.

[0091] Prediction stage: For new correlation peak data, extract its feature vector x new =(H new , W new , S new , Rnew ). new Input each decision tree in the random forest to get the predicted peak position of each decision tree (t represents the tth tree). The final predicted peak position is Where T is the number of decision trees in the random forest.

[0092] 4. Peak search: For new related peak data, extract its features. Input the features into the trained machine learning model to obtain the predicted peak position.

[0093] In this embodiment, a multi-channel baseband I / Q data set and a delayed linear frequency modulation signal set are subjected to a multi-dimensional cross-correlation operation to obtain a correlation peak data set, and then an intelligent peak search algorithm is used to predict the peak position of each correlation peak data. Among them, a multi-dimensional cross-correlation operation is used to perform time domain flipping, conjugation and multi-dimensional convolution operations on the multi-channel baseband data and the delayed linear frequency modulation signal to accurately find the time delay relationship between the signals, laying the foundation for subsequent phase nonlinear calculations. The intelligent peak search algorithm combines machine learning technology, extracts the characteristics of the correlation peak data (such as peak height, width, symmetry, local change rate, etc.), and uses algorithms such as random forests for training and prediction, thereby improving the accuracy and efficiency of peak position prediction.

[0094] For ease of understanding, an example is given here: For example, in a digital phased array radar system, the phase nonlinearity of the receiving channel needs to be measured. First, multi-channel baseband I / Q data and delayed linear frequency modulation signals are generated, and then the peak position of the correlation peak data is determined through the above-mentioned multi-dimensional cross-correlation operation and intelligent peak search algorithm. Assume that there is a set of correlation peak data. After data preprocessing and feature extraction, the peak height H = 5, peak width W = 10, symmetry S = 0.8, local change rate R = 20 and other features are obtained. These features are input into the trained random forest model, and the model predicts the peak position of the correlation peak data based on the relationship between the previously learned features and the peak position. In this way, the peak position of each correlation peak data can be accurately found, and then the phase difference can be calculated, which provides key parameters for the subsequent removal of the first-order linear component and the DC component to obtain the phase nonlinear set of the digital phased array receiving channel. In practical applications, this precise measurement method can help optimize the performance of the radar system and improve the accuracy of target detection and tracking.

[0095] As an improvement of the above embodiment, the phase difference set is calculated according to the peak position of each data in the correlation peak data set, including:

[0096] An interpolation operation is performed at the peak position of each data in the correlation peak data set to obtain a delay set at the peak. In this step, specifically: a high-precision interpolation operation is performed, such as using a cubic spline interpolation method, assuming that the function of the correlation peak data in a certain interval [x1, x2] is y(x), and a cubic polynomial function S(x) can be constructed by the cubic spline interpolation method, so that S(x i )=y(x i ), S′(x i )=y′(x i ), S″(x i )=y″(x i ), where i = 1, 2; this allows for more precise interpolation near the peak, improving the accuracy of delay estimation; the delay set F at the peak is obtained = {f1, f2, ..., f n}, where f i represents the delay estimation value of the corresponding signal;

[0097] Based on the delay set and the dynamic integer multiple delay, a synchronous linear frequency modulation signal set is generated; in this step, specifically: based on the delay set and the dynamic integer multiple delay, a linear frequency modulation signal set synchronized with the multi-channel baseband data is generated to provide an accurate reference signal for subsequent phase nonlinear calculation; wherein, based on the delay set F = {f1, f2, ..., f n} and dynamic integer delay to generate a synchronous linear frequency modulation signal set G = {g1(t), g2(t), ..., g n (t)}; for the i-th synchronous linear frequency modulation signal g i (t), which is expressed as A i is the amplitude of the ith signal, which determines the strength of the signal and is usually set during signal generation according to system requirements and signal propagation characteristics; f 0i is the starting frequency of the ith signal. Different starting frequencies make each signal have a specific position in the frequency domain, which is used to cover different frequency ranges to improve the accuracy of measurement; μ i is the frequency modulation slope of the i-th signal, which determines the frequency change rate of the signal over time and is related to parameters such as the bandwidth and duration of the signal; t is the time variable, indicating the change of the signal over time; f i is the i-th delay value in the delay set F, representing the time delay of the corresponding signal determined after the previous processing; k i is the dynamic integer delay multiple of the ith signal, used to further adjust the signal delay to achieve more accurate synchronization; T c is the clock cycle, which is the basic unit of time measurement in the system and is used to determine the accuracy of delay;

[0098] For a synchronous linear frequency modulation signal set G and a multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)} respectively perform phase calculation on the corresponding signal data in the two to obtain a set of phase calculation results of the two; in this step, specifically: perform phase calculation on each signal in the synchronous linear frequency modulation signal set and the corresponding data in the multi-channel baseband I / Q data set to obtain a set of phase calculation results; wherein, for the i-th synchronous linear frequency modulation signal g i The phase of (t) is calculated as Here, ∠ represents the phase angle of the complex number, that is, the phase of the signal. By analyzing the expression of the synchronous linear frequency modulation signal, the relationship between its phase and time is obtained. For the i-th multi-channel baseband I / Q data c i The phase of (n) is calculated as Multi-channel baseband I / Q data is usually expressed in complex form, and the corresponding data phase is obtained by calculating its amplitude angle;

[0099] According to the phase calculation result set of the two, the phase difference set of the two is calculated; in this step, specifically: the phase difference set of the two is calculated ΔΦ = {Δφ1(n), Δφ2(n), ..., Δφ n (n)}, where That is, for each corresponding data point, the difference between the phase of the synchronous linear frequency modulation signal and the phase of the multi-channel baseband data is calculated.

[0100] In this embodiment, an interpolation operation is first performed at the peak position of the correlation peak data set to obtain a delay set, and then a synchronous linear frequency modulation signal set is generated based on the delay set. Then, the phases of the synchronous linear frequency modulation signal set and the corresponding signals in the multi-channel baseband I / Q data set are calculated respectively, and finally, the phase difference set between the two is calculated. The technical innovation lies in the use of cubic spline interpolation to improve the delay estimation accuracy, and in accurately measuring the phase nonlinearity by generating synchronous linear frequency modulation signals and calculating the phase difference. This method can more accurately reflect the phase characteristics of the digital phased array receiving channel, providing strong support for subsequent calibration and optimization.

[0101] For ease of understanding, an example is given here: for example, in a wireless communication system, the phase nonlinearity of the digital phased array receiving channel will affect the signal reception quality. Through this embodiment, the phase nonlinearity of the receiving channel can be accurately measured. Assume that at a certain moment, a correlation peak data set is obtained, and after a cubic spline interpolation operation, a delay set F at the peak is obtained. Then, a synchronous linear frequency modulation signal set G is generated based on these delay values. Then, the phase of the synchronous linear frequency modulation signal and the multi-channel baseband data is calculated. For example, at a certain moment, the phase of the synchronous linear frequency modulation signal The phase of the corresponding multi-channel baseband I / Q data Then their phase difference Δφ i (n) = 0.2. By performing such calculations on multiple data points, a phase difference set ΔΦ is obtained. This phase difference set can reflect the phase nonlinear characteristics of the receiving channel at different times and frequencies. Based on these characteristics, the receiving channel can be calibrated and optimized to improve the performance of the wireless communication system, reduce signal distortion and bit error rate, and thus enhance the user's communication experience.

[0102] As an improvement of the above embodiment, the de-skewing algorithm is used to remove the first-order linear component and the DC component of the phase difference set to obtain the phase nonlinear set of the digital phased array receiving channel, including:

[0103] For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n (n)} is subjected to fast Fourier transform to obtain the frequency domain representation ΔΦ(f); where Δφ is i The discrete Fourier transform of (n) is ΔΦ i (f) then N is the data length, that is, the number of data points in the phase difference set; f is the frequency variable, used for frequency domain analysis; n represents the discrete time point index in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at the discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at the corresponding frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at the corresponding frequency f in the frequency domain after discrete Fourier transform, which is the value of all ΔΦ i (f) the sum of;

[0104] Analyze the frequency domain data to determine the characteristics of the first-order linear component and the DC component; the DC component characteristics: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristics: observe the changing trend of the frequency domain data in the low-frequency part; assume that the first-order linear component is expressed in the form of a1f+b1 in the frequency domain, and determine the coefficients a1 and b1 by fitting the data of the low-frequency part; a1 represents the slope of the first-order linear component in the frequency domain, and b1 is a constant term;

[0105] Design a high-pass filter to remove the DC component: where the high-pass filter transfer function is where f cutoff is the cutoff frequency of the high-pass filter; the frequency domain data after filtering by the high-pass filter is ΔΦ filtered (f)=ΔΦ(f)H hp (f); H hp(f) is the transfer function of the high-pass filter; f is the input frequency; when the input frequency f>f cutoff When f≤f cutoff When , the transfer function value is 0, indicating that the input signal of this frequency is completely attenuated;

[0106] Design a linear phase filter to remove the first-order linear component: The transfer function of the linear phase filter is designed to be H lp (f) = exp(-j2π(a2f+b2)), H lp (f) is the transfer function of the linear phase filter; where a2 is a frequency-dependent coefficient, b2 is the slope estimate of the first-order linear component to be removed, obtained by linear fitting of the low-frequency frequency points; the design purpose of this transfer function is to adjust according to the slope of the first-order linear component to remove the first-order linear component; where the coefficient a2 = -a1, b2 is adjusted as needed to ensure the linear phase characteristics of the filter; the filtered frequency domain data is again passed through the filter to obtain ΔΦ final (f) = ΔΦ filtered (f)H lp (f);

[0107] For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the de-skewed time domain data Δφ deskewed (n), thus obtaining the phase nonlinear data of the digital phased array receiving channel after removing the first-order linear component and DC component. The calculation formula is:

[0108] In this embodiment, the characteristics of the first-order linear component and the DC component of the phase difference set are determined by performing spectrum analysis on the phase difference set, and then a high-pass filter and a linear phase filter are designed to remove the DC component and the first-order linear component respectively, and finally the phase nonlinear set of the digital phased array receiving channel is obtained. The technical innovation lies in the use of a de-skewing algorithm based on spectrum analysis and filter design, which can more accurately remove the interference components in the phase difference set and improve the accuracy of phase nonlinear measurement. This method combines fast Fourier transform (FFT), filter design and inverse fast Fourier transform (IFFT) to achieve accurate processing of complex signals.

[0109] For ease of understanding, an example is given here: for example, in satellite communications, the phase nonlinearity of the digital phased array receiving channel will affect the transmission quality of the signal. Through this embodiment, the phase nonlinearity of the receiving channel can be accurately measured and corrected. Assuming that a phase difference set is obtained, after fast Fourier transform, obvious DC components and first-order linear components are found in the frequency domain. These interference components are removed by designing a suitable high-pass filter and a linear phase filter. For example, the cutoff frequency of the high-pass filter is set to a suitable value so that the DC component is effectively removed; the linear phase filter is designed according to the slope estimate of the first-order linear component to accurately remove the first-order linear component. After inverse fast Fourier transform, the phase nonlinear data after interference removal is obtained. Based on these data, the digital phased array receiving channel in the satellite communication system can be optimized and calibrated to improve the reception quality of the signal, reduce the bit error rate, and ensure the stability and reliability of communication. In this way, whether it is high-definition video transmission or important data communication, it can be better guaranteed to enhance the user experience in satellite communications.

[0110] See also Figure 2 , is a schematic diagram of the structure of a digital phased array receiving channel phase nonlinearity measurement device provided by an embodiment of the present invention. The digital phased array receiving channel phase nonlinearity measurement device comprises:

[0111] The signal generating module 10 is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies, and perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a broadband radio frequency linear frequency modulation signal set;

[0112] The signal processing module 11 is used to simultaneously input each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0113] The signal delay module 12 is used to dynamically delay each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulation signal set;

[0114] The peak measurement module 13 is used to measure the peak position of each data in the correlation peak data set according to the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model;

[0115] A calculation module 14, configured to calculate a phase difference set according to the peak position of each data in the correlation peak data set;

[0116] The removal module 15 is used to remove the first-order linear component and the DC component of the phase difference set by using a de-skewing algorithm to obtain a phase nonlinearity set of the digital phased array receiving channel.

[0117] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0118] First, multiple baseband linear frequency modulation signals with different center frequencies are generated, and digital-to-analog conversion, up-conversion, filtering and signal amplification are performed on them to obtain a set of broadband RF linear frequency modulation signals. The advantage of this is that it can cover a wider frequency range and more comprehensively detect the phase nonlinear characteristics of the digital phased array receiving channel through multiple signals of different frequencies. Compared with the traditional method, this scheme can better adapt to the complex system of the digital phased array because it can generate signals suitable for the system. Then, these broadband RF linear frequency modulation signals are simultaneously input into the digital phased array receiving channel, and the receiving channel performs down-conversion, filtering, signal amplification and digital preprocessing on each input signal to obtain a multi-channel baseband I / Q data set. Inputting multiple signals at the same time and performing parallel processing greatly improves the measurement efficiency and avoids the low efficiency of measuring signals one by one in the traditional method. Moreover, through a series of preprocessing operations, more accurate baseband data can be obtained. Then, according to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband data set is delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set. This dynamic delay adjustment can ensure that the signal is accurately aligned in time, thereby providing an accurate basis for subsequent phase calculation. This solves the measurement error problem caused by signal asynchrony in the prior art. Afterwards, the peak position of each data in the correlation peak data set is determined by using a peak search algorithm model through a multi-channel baseband I / Q data set and a delayed linear frequency modulation signal set. This precise peak search algorithm can accurately find the correlation peak between signals, thereby improving the measurement accuracy. Finally, the phase difference set is calculated according to the peak position, and the de-skewing algorithm is used to remove the first-order linear component and the DC component to obtain the phase nonlinear set of the digital phased array receiving channel. The application of the de-skewing algorithm effectively removes interference factors, so that the measurement results more accurately reflect the phase nonlinear characteristics of the digital phased array receiving channel. From the above analysis, it can be seen that the embodiment of the present invention overcomes the limitations of the prior art through the generation of multi-frequency signals, parallel processing, dynamic delay adjustment, precise peak search and effective de-skewing algorithm, and provides a reliable and efficient solution for the performance evaluation and calibration of the digital phased array receiving channel. Therefore, the embodiment of the present invention can achieve efficient, accurate and low-cost measurement of the phase nonlinearity of the digital phased array receiving channel.

[0119] It can be understood that the specific embodiments of the above-mentioned digital phased array receiving channel phase nonlinearity measurement device can correspond to the relevant embodiments of the above-mentioned digital phased array receiving channel phase nonlinearity measurement method, which will not be repeated here.

[0120] See also Figure 3 , is a schematic diagram of a digital phased array receiving channel phase nonlinearity measurement device provided in an embodiment of the present invention. The digital phased array receiving channel phase nonlinearity measurement device of this embodiment includes: a processor 100, a memory 101, and a computer program stored in the memory 101 and executable on the processor 100, such as a digital phased array receiving channel phase nonlinearity measurement program. When the processor 100 executes the computer program, the steps in the above-mentioned various digital phased array receiving channel phase nonlinearity measurement method embodiments are implemented. Alternatively, when the processor 100 executes the computer program, the functions of the modules / units in the above-mentioned various device embodiments are implemented.

[0121] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the digital phased array receiving channel phase nonlinearity measurement device.

[0122] The measuring device for the phase nonlinearity of the receiving channel of the digital phased array may be a computing device such as a control device of the digital phased array, a desktop computer, a notebook, a PDA, and a cloud server. The measuring device for the phase nonlinearity of the receiving channel of the digital phased array may include, but is not limited to, a processor and a memory. Those skilled in the art may understand that the schematic diagram is only an example of the measuring device for the phase nonlinearity of the receiving channel of the digital phased array, and does not constitute a limitation on the measuring device for the phase nonlinearity of the receiving channel of the digital phased array, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the measuring device for the phase nonlinearity of the receiving channel of the digital phased array may also include input and output devices, network access devices, buses, etc.

[0123] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the digital phased array receiving channel phase nonlinearity measurement device, and uses various interfaces and lines to connect various parts of the entire digital phased array receiving channel phase nonlinearity measurement device.

[0124] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the digital phased array receiving channel phase nonlinearity measurement device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0125] Wherein, if the module / unit integrated in the measurement device for phase nonlinearity of the digital phased array receiving channel is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0126] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0127] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for measuring phase nonlinearity of a digital phased array receiving channel, characterized in that: include: Generate multiple baseband linear frequency modulation signals with different center frequencies, and perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a set of broadband radio frequency linear frequency modulation signals; Input each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel at the same time, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set; According to the real-time total delay of the digital phased array receiving channel, each signal data in the baseband I / Q data set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set; According to the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set, and through the peak search algorithm model, the peak position of each data in the correlation peak data set is measured; According to the peak position of each data in the correlation peak data set, a phase difference set is calculated; A de-skewing algorithm is used to remove the first-order linear component and the direct current component of the phase difference set, so as to obtain a phase nonlinear set of the digital phased array receiving channel.

2. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The method predicts the peak position of each data in the correlation peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model, including: Perform multi-dimensional cross-correlation operation on each signal data in the multi-channel baseband I / Q data set and the signal in the corresponding delayed linear frequency modulation signal set to obtain a correlation peak data set; The peak search algorithm model is used to search for the peak of each data in the relevant peak data set, and the peak position of each data is predicted.

3. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The step of calculating a phase difference set according to the peak position of each data in the correlation peak data set includes: Perform interpolation operation at the peak position of each data in the correlation peak data set to obtain a delay set at the peak; Generate a synchronous linear frequency modulation signal set based on the delay set and the dynamic integer multiple delay; Performing phase calculations on corresponding signal data in both the synchronous linear frequency modulation signal set and the multi-channel baseband I / Q data set to obtain phase calculation result sets of the two; According to the phase calculation result sets of the two, a phase difference set of the two is calculated.

4. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The method of using a de-skewing algorithm to remove the first-order linear component and the DC component of the phase difference set to obtain a phase nonlinear set of a digital phased array receiving channel includes: For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n (n)} is subjected to fast Fourier transform to obtain the frequency domain representation ΔΦ(f); where Δφ is i The discrete Fourier transform of (n) is ΔΦ i (f) N is the data length, that is, the number of data points in the phase difference set; g is the frequency variable, used for frequency domain analysis; n represents the discrete time point index in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at the discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at the corresponding frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at the corresponding frequency f in the frequency domain after discrete Fourier transform, which is the value of all ΔΦ i (f) the sum of; Analyze the frequency domain data to determine the characteristics of the first-order linear component and the DC component; the DC component characteristics: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristics: observe the changing trend of the frequency domain data in the low-frequency part; assume that the first-order linear component is expressed in the form of a1f+b1 in the frequency domain, and determine the coefficients a1 and b1 by fitting the data of the low-frequency part; a1 represents the slope of the first-order linear component in the frequency domain, and b1 is a constant term; Design a high-pass filter to remove the DC component: where the high-pass filter transfer function is where f cutoff is the cutoff frequency of the high-pass filter; the frequency domain data after filtering by the high-pass filter is ΔΦ filtered (f)=ΔΦ(f)H hp (f); H hp (f) is the transfer function of the high-pass filter; f is the input frequency; when the input frequency f>f cutoff When f≤f cutoff When , the transfer function value is 0, indicating that the input signal of this frequency is completely attenuated; Design a linear phase filter to remove the first-order linear component: The transfer function of the linear phase filter is designed to be H lp (f) = exp(-j2π(a2f+b2)), H lp (f) is the transfer function of the linear phase filter; where a2 is a frequency-dependent coefficient, b2 is the slope estimate of the first-order linear component to be removed, obtained by linear fitting of the low-frequency frequency points; the design purpose of this transfer function is to adjust according to the slope of the first-order linear component to remove the first-order linear component; where the coefficient a2 = -a1, b2 is adjusted as needed to ensure the linear phase characteristics of the filter; the filtered frequency domain data is again passed through the filter to obtain ΔΦ final (f) = ΔΦ filtered (f)H lp (f); For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the de-skewed time domain data Δφ deskewed (n), thus obtaining the phase nonlinear data of the digital phased array receiving channel after removing the first-order linear component and DC component. The calculation formula is:

5. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The method generates a plurality of baseband linear frequency modulation signals with different center frequencies, and performs separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a broadband radio frequency linear frequency modulation signal set, including: A signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies; for each baseband linear frequency modulation signal a i (t), which is expressed as A i represents the amplitude of the ith signal, which determines the strength of the signal. The amplitude is selected based on actual needs and the dynamic range of the system; f 0i is the starting frequency of the ith signal. Different starting frequencies make each signal cover different frequency ranges; μ i is the FM slope of the ith signal, which determines the frequency change rate of the signal over time; the FM slope is determined by combining the bandwidth and time duration of the signal to meet the requirements of the time-bandwidth product; t is a time variable, which represents the change of the signal over time; the bandwidth of each signal is assumed to be the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel; where the time-bandwidth product meets a specific optimization threshold, which is assumed to be (360 / δ) 2 / 8, where δ is the phase nonlinearity measurement error; the time bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the duration and frequency modulation slope of the signal, the time-bandwidth product meets the threshold requirement, thereby improving the measurement accuracy; Each baseband linear frequency modulation signal is individually subjected to digital-to-analog conversion, up-conversion, filtering and signal amplification processing to obtain a broadband radio frequency linear frequency modulation signal set.

6. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The method comprises: inputting each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel at the same time, and performing independent down-conversion, filtering, signal amplification and digital preprocessing on each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set, including: The broadband RF linear frequency modulation signal set B = {b1(t), b2(t), ..., b n (t)} are simultaneously input into the digital phased array receiving channel; this is achieved by a multiplexer or parallel connection to ensure that each signal enters the receiving channel at the same time; Each input signal is processed independently, including down-conversion, filtering, signal amplification and digital preprocessing, to obtain a multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c n (n)}; n represents a discrete time point, each c i (n) corresponds to the input signal b i (t) processed baseband data; wherein the down-conversion operation converts the RF signal into a baseband signal; for signal b i (t), down-conversion is achieved by mixing with the signal generated by the local oscillator; let the frequency of the local oscillator be f LOi , then the down-converted signal can be expressed as in, Represents the signal after down-conversion, which is the input RF signal b i (t) The baseband signal obtained after mixing with the local oscillator signal; b i (t) is the input RF signal, exp(-j2πf LOi t) is the complex exponential signal generated by the local oscillator; f LOi is the frequency of the local oscillator, which is used to down-convert the RF signal to baseband; t is a time variable, which represents the change of the signal over time; the filtering operation is to use a digital filter or an analog filter to remove unnecessary frequency components and improve the purity of the signal; the signal amplification is to use an amplifier to linearly amplify the signal to enhance the strength of the signal; the digital preprocessing is achieved through an analog-to-digital converter to convert the analog signal into a digital signal.

7. The method for measuring phase nonlinearity of a digital phased array receiving channel according to claim 1, characterized in that: The method dynamically delays each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulation signal set, including: Each signal in the baseband linear frequency modulation signal set is dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulation signal set; the delay time is dynamically adjusted according to the real-time total delay of the correction source generation module and the digital phased array receiving channel; wherein, the clock cycle is assumed to be T c , for each signal a in the baseband linear frequency modulation signal set i (t), the delayed signal d i (t) is expressed as d i (t) is the delayed signal, which is the signal obtained by delaying the baseband linear frequency modulation signal by a dynamic integer multiple clock cycle; A i is the amplitude of the ith signal, which is consistent with the amplitude parameter in the baseband linear frequency modulation signal and determines the strength of the signal; μ i is the frequency modulation slope of the i-th signal, which is consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal and determines the frequency change rate of the signal over time; t is the time variable, indicating the change of the signal over time; k i is the dynamic integer delay multiple of the i-th signal, which is used to adjust the delay time so that the delayed signal is aligned with the signal processed by the receiving channel in time; the dynamic integer delay multiple k i The method of determining is as follows: First, measure the real-time total delay T between the correction source generation module and the digital phased array receiving channel. delay ; By inserting a time measurement module into the system, or by estimating using known signal propagation time and processing time; Then, calculate the dynamic integer delay multiple in Represents a floor function that ensures that the delayed signal is aligned as closely as possible in time with the signal processed by the receiving channel.

8. A device for measuring phase nonlinearity of a digital phased array receiving channel, characterized in that: include: A signal generation module is used to generate a plurality of baseband linear frequency modulation signals with different center frequencies, and perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification processing on each baseband linear frequency modulation signal to obtain a set of broadband RF linear frequency modulation signals; A signal processing module is used to simultaneously input each signal in the broadband RF linear frequency modulation signal set into the digital phased array receiving channel, so as to independently down-convert, filter, amplify and digitally pre-process each input signal through the receiving channel to obtain a multi-channel baseband I / Q data set; A signal delay module is used to dynamically delay each signal data in the baseband I / Q data set by an integer multiple of the clock cycle according to the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulation signal set; A peak measurement module is used to measure the peak position of each data in the correlation peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulation signal set and through the peak search algorithm model; A calculation module, used for calculating a phase difference set according to the peak position of each data in the correlation peak data set; The removal module is used to remove the first-order linear component and the DC component of the phase difference set by using a de-skewing algorithm to obtain a phase nonlinear set of the digital phased array receiving channel.

9. A digital phased array receiving channel phase nonlinearity measurement device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for measuring phase nonlinearity of a digital phased array receiving channel according to any one of claims 1 to 7 is implemented.

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