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

Abstract

The application discloses a kind of digital phased array receiving channel phase nonlinearities measurement method, device and equipment, method includes: generating multiple different center frequency baseband linear frequency modulation signal, obtain wideband radio frequency linear frequency modulation signal set;These signals are simultaneously input digital phased array receiving channel, obtain multiple baseband data sets;According to the real-time total delay of receiving channel, integer times clock period delay is carried out to each signal data in baseband data set, and obtain delay linear frequency modulation signal set;According to multiple baseband data sets and delay linear frequency modulation signal set, determine the peak position of each data in correlation peak data set by peak search algorithm model, and then calculate phase difference set;Using the algorithm of removing slope, remove first-order linear component and direct current component in phase difference set, obtain the phase nonlinearities set of digital phased array receiving channel.The application can realize to digital phased array receiving channel phase nonlinearities high efficiency, accurate and low cost measurement.

Description

Technical Field

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

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

[0003] Traditional measurement methods are typically based on vector network analyzers. This approach assesses 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 significant limitations when applied to digital phased array receiving channels. Vector network analyzers are primarily suitable for measuring linear, passive networks; they cannot directly measure phase nonlinearity in digital phased array receiving channels that include mixed-signal and frequency conversion systems. Furthermore, this method is inefficient, requiring measurements at each frequency point, and is costly, as vector network analyzers themselves are high-precision, high-cost instruments.

[0004] Another existing technique is a digital-domain-based method for measuring phase nonlinearity. This method typically 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 using complex digital signal processing algorithms. However, this method is hardware-intensive, requiring not only an ADC with extremely short sampling intervals but also a powerful DSP to process large amounts of data. This increases both system complexity and cost. Furthermore, the short sampling interval places high demands on the system's clock accuracy and stability; otherwise, additional measurement errors will be introduced.

[0005] Therefore, existing technologies for measuring the phase nonlinearity of digital phased array receiving channels suffer from several problems, including the inability to directly measure the phase nonlinearity of hybrid analog-digital and frequency conversion systems, low testing efficiency, high cost, high hardware requirements, and system complexity due to short sampling intervals. These issues make it difficult to meet the practical application requirements for efficient, accurate, and low-cost measurement of the phase nonlinearity of digital phased array receiving channels. Summary of the Invention

[0006] This invention provides a method, apparatus, and device for measuring the phase nonlinearity of a digital phased array receiving channel, which can achieve efficient, accurate, and low-cost measurement of the phase nonlinearity of a digital phased array receiving channel.

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

[0008] Multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated, and each baseband LFM signal is subjected to separate digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency LFM signals.

[0009] Each signal in the broadband radio frequency linear frequency modulation signal set is simultaneously input into the digital phased array receiving channel, so that each input signal can be independently down-converted, filtered, amplified and digitally preprocessed through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0010] Based on 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 modulated signal set;

[0011] Based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through the peak search algorithm model, the peak position of each data in the relevant peak data set is measured;

[0012] Based on the peak position of each data point in the relevant peak data set, the phase difference set is calculated;

[0013] The phase difference set is processed by a deskewing algorithm to remove the first-order linear component and the DC component, resulting in the phase nonlinear set of the digital phased array receiving channel.

[0014] As an improvement to the above scheme, the step of predicting the peak position of each data point in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through a peak search algorithm model, includes:

[0015] Perform multidimensional cross-correlation operation between each signal data in the multi-path baseband I / Q data set and the corresponding signal in the delayed linear frequency modulated signal set to obtain the correlation peak data set;

[0016] The peak search algorithm model is used to search for the peak position of each data point in the relevant peak data set.

[0017] As an improvement to the above scheme, the step of calculating the phase difference set based on the peak position of each data point in the relevant peak data set includes:

[0018] Interpolation is performed at the peak position of each data point in the relevant peak data set to obtain the delay set at the peak.

[0019] Based on the delay set and dynamic integer multiple delay, a set of synchronous linear frequency modulation signals is generated;

[0020] Phase calculations are performed on the corresponding signal data in both the synchronous linear frequency modulated signal set and the multi-channel baseband I / Q data set to obtain a set of phase calculation results for both.

[0021] Based on the set of phase calculation results of the two, the set of phase differences between the two is calculated.

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

[0023] For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n Performing a Fast Fourier Transform on (n)} yields the frequency domain representation ΔΦ(f); where, let Δφ i The discrete Fourier transform of (n) is ΔΦ i (f), then N is the data length, i.e., the number of data points in the phase difference set; f is the frequency variable used for frequency domain analysis; n represents the index of discrete time points in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at frequency f in the frequency domain after discrete Fourier transform, and is the sum of all ΔΦ values. i The sum of (f);

[0024] Analyze frequency domain data to determine the characteristics of the first-order linear component and the DC component; among which, the DC component characteristic is: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristic is: observe the changing trend of the frequency domain data in the low-frequency part; assuming that the first-order linear component is expressed in the frequency domain as a1f+b1, the coefficients a1 and b1 are determined by fitting the data in 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 This 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, the transfer function value is 1, indicating that the input signal at that frequency passes through the filter with almost no attenuation; when f ≤ f cutoff When the value is 0, the transfer function value is 0, indicating that the input signal at that frequency is completely attenuated;

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

[0027] For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the deskewed 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 the DC component, the calculation formula is:

[0028] As an improvement to the above scheme, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated, and each baseband LFM signal undergoes separate digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a broadband radio frequency LFM signal set, including:

[0029] Using a signal generation module, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated; for each of these baseband LFM signals a i (t), whose expression is A i This represents the amplitude of the i-th signal, which determines the signal strength. The amplitude is chosen based on actual requirements and the dynamic range of the system; f 0i μ is the starting frequency of the i-th signal; different starting frequencies cause each signal to cover a different frequency range. iLet be the frequency modulation slope of the i-th signal, which determines the rate of change of the signal's frequency over time. The choice of the frequency modulation slope is determined by combining the signal's bandwidth and duration to meet the requirements of the time-bandwidth product. t is a time variable representing the signal's change over time. Assume that the bandwidth of each signal is the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel. The time-bandwidth product satisfies a specific optimization threshold, which is set to (360 / δ). 2 / 8, where δ is the phase nonlinearity measurement error; the time-width-bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the signal duration and frequency modulation slope, the time-width-bandwidth product can meet the threshold requirement, thereby improving the measurement accuracy.

[0030] Each baseband linear frequency modulated signal is individually converted from digital to analog, up-converted, filtered, and amplified to obtain a set of broadband radio frequency linear frequency modulated signals.

[0031] As an improvement to the above scheme, the method involves simultaneously inputting each signal from the broadband radio frequency linear frequency modulated signal set into the digital phased array receiving channel. The receiving channel then performs independent down-conversion, filtering, signal amplification, and digital preprocessing on each input signal to obtain a multi-channel baseband I / Q data set, including:

[0032] Let the set of broadband radio frequency linear frequency modulated signals be B = {b1(t), b2(t), ..., b n Each signal in (t)} is simultaneously input into the digital phased array receiving channel; this is achieved through multiplexers or parallel connections to ensure that each signal enters the receiving channel at the same time.

[0033] Each input signal undergoes independent processing steps, 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, and each c i (n) corresponds to the input signal b i (t) Processed baseband data; wherein, the down-conversion operation converts the radio frequency signal into a baseband signal; for signal b i (t), down-conversion is achieved by mixing the signal 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, The signal after down-conversion is the input radio frequency signal b. i (t) The baseband signal obtained after mixing with the local oscillator signal; b i (t) is the input radio frequency signal, exp(-j2πfLOi t) is the complex exponential signal generated by the local oscillator; f LOi t is the frequency of the local oscillator, used to downconvert the radio frequency signal to baseband; t is the time variable, representing the change of the signal over time; filtering operation uses digital or analog filters to remove unwanted frequency components and improve the purity of the signal; signal amplification uses an amplifier to linearly amplify the signal to enhance its strength; digital preprocessing is achieved through an analog-to-digital converter, thereby converting the analog signal into a digital signal.

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

[0035] Each signal in the baseband linear frequency modulation (LFM) signal set is dynamically delayed by an integer multiple of the clock period to obtain a delayed LFM signal set; the delay time is dynamically adjusted based on the real-time total delay of the correction source generation module and the digital phased array receiving channel; where the clock period is set to T. c For each signal a in the baseband linear frequency modulated signal set i (t), its delayed signal d i The expression for (t) is: d i (t) is the delayed signal, which is obtained by dynamically delaying the baseband linear frequency modulated signal by an integer multiple of the clock cycle; A i The amplitude of the i-th signal is consistent with the amplitude parameter in the baseband linear frequency modulated signal, which determines the signal strength; μ i is the frequency modulation slope of the i-th signal, consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal, which determines the rate of change of the signal's frequency over time; t is the time variable, representing the change of the signal over time; k i The dynamic integer delay factor k is used to adjust the delay time of the i-th signal, ensuring that the delayed signal is time-aligned with the signal processed by the receiving channel; i The method for determining it is as follows:

[0036] First, measure the total real-time delay T of the measurement correction source generation module and the digital phased array receiving channel. delay It can be estimated by inserting a time measurement module into the system, or by utilizing known signal propagation and processing times.

[0037] Then, calculate the dynamic integer delay factor. in This indicates a rounding down function that ensures the delayed signal is as close as possible in time to the signal processed by the receiving channel.

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

[0039] The signal generation module is used to generate multiple baseband linear frequency modulated signals with different center frequencies, and to perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification on each baseband linear frequency modulated signal to obtain a set of broadband radio frequency linear frequency modulated signals.

[0040] The signal processing module is used to simultaneously input each signal in the broadband radio frequency linear frequency modulation signal set into the digital phased array receiving channel, so that each input signal can be independently down-converted, filtered, amplified and digitally preprocessed through the receiving channel to obtain a multi-channel baseband I / Q data set;

[0041] The 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 based on the real-time total delay of the digital phased array receiving channel, so as to obtain a delayed linear frequency modulated signal set.

[0042] The peak measurement module is used to measure the peak position of each data point in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through the peak search algorithm model.

[0043] The calculation module is used to calculate the phase difference set based on the peak position of each data point in the relevant peak data set;

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

[0045] Another embodiment of the present invention provides a measurement device for phase nonlinearity of a digital phased array receiving channel, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the measurement method for phase nonlinearity of a digital phased array receiving channel as described in the above-described embodiment of the invention.

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

[0047] First, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated and subjected to digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency LFM signals. This approach allows for wider frequency coverage, enabling more comprehensive detection of the phase nonlinearity characteristics of the digital phased array receiving channel through multiple signals at different frequencies. Compared to traditional methods, this scheme is better suited to the complex systems of digital phased arrays because it can generate signals appropriate for the system. Then, these broadband radio frequency LFM signals are simultaneously input into the digital phased array receiving channel. 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. Simultaneous input and parallel processing of multiple signals significantly improves measurement efficiency and avoids the inefficiency of measuring each signal individually in traditional methods. Furthermore, a series of preprocessing operations yields more accurate baseband data. Next, based on 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 LFM signal set. This dynamic delay adjustment ensures accurate signal alignment in time, providing a reliable basis for subsequent phase calculations. This solves the measurement error problem caused by signal asynchrony in existing technologies. Subsequently, using a multi-channel baseband I / Q data set and a delayed linear frequency modulated signal set, a peak position of each data point in the relevant peak data set is determined using a peak search algorithm model. This precise peak search algorithm accurately finds the correlation peaks between signals, improving measurement accuracy. Finally, the phase difference set is calculated based on the peak positions, and a de-skew algorithm is used to remove the first-order linear component and DC component, obtaining the phase nonlinearity set of the digital phased array receiving channel. The application of the de-skew algorithm effectively removes interference factors, making the measurement results more accurately reflect the phase nonlinearity characteristics of the digital phased array receiving channel. As can be seen from the above analysis, this invention overcomes the limitations of existing technologies through multi-frequency signal generation, parallel processing, dynamic delay adjustment, precise peak search, and an effective de-skew algorithm, providing a reliable and efficient solution for the performance evaluation and calibration of digital phased array receiving channels. Therefore, this invention can achieve efficient, accurate, and low-cost measurement of the phase nonlinearity of digital phased array receiving channels. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a method for measuring phase nonlinearity of a digital phased array receiving channel according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the structure of a measurement device for phase nonlinearity of a digital phased array receiving channel according to an embodiment of the present invention;

[0050] Figure 3This is a schematic diagram of the structure of a measurement device for phase nonlinearity of a digital phased array receiving channel provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] See Figure 1 This is a flowchart illustrating a method for measuring the phase nonlinearity of a digital phased array receiving channel according to an embodiment of the present invention. The method for measuring the phase nonlinearity of a digital phased array receiving channel includes steps S10 to S15:

[0053] S10 generates multiple baseband linear frequency modulation (LFM) signals with different center frequencies, and performs separate digital-to-analog conversion, up-conversion, filtering, and signal amplification on each baseband LFM signal to obtain a set of broadband radio frequency LFM signals.

[0054] S11, input each signal in the broadband radio frequency linear frequency modulation signal set into the digital phased array receiving channel at the same time, so that each input signal can be independently down-converted, filtered, amplified and digitally preprocessed through the receiving channel to obtain a multi-channel baseband I / Q data set;

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

[0056] S13, 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, the peak position of each data in the relevant peak data set is measured;

[0057] S14, calculate the phase difference set based on the peak position of each data point in the relevant peak data set;

[0058] S15, the first-order linear component and DC component of the phase difference set are removed by the de-skewing algorithm to obtain the 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 modulated (LFM) signals with different center frequencies are generated and subjected to digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency LFM signals. This approach allows for wider frequency coverage, enabling more comprehensive detection of the phase nonlinearity characteristics of the digital phased array receiving channel through multiple signals at different frequencies. Compared to traditional methods, this scheme is better suited to the complex systems of digital phased arrays because it can generate signals appropriate for the system. Then, these broadband radio frequency LFM signals are simultaneously input into the digital phased array receiving channel. 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. Simultaneous input and parallel processing of multiple signals significantly improves measurement efficiency and avoids the inefficiency of measuring each signal individually in traditional methods. Furthermore, a series of preprocessing operations yields more accurate baseband data. Next, based on 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 LFM signal set. This dynamic delay adjustment ensures accurate signal alignment in time, providing a reliable basis for subsequent phase calculations. This solves the measurement error problem caused by signal asynchrony in existing technologies. Subsequently, using a multi-channel baseband I / Q data set and a delayed linear frequency modulated signal set, a peak position of each data point in the relevant peak data set is determined using a peak search algorithm model. This precise peak search algorithm accurately finds the correlation peaks between signals, improving measurement accuracy. Finally, the phase difference set is calculated based on the peak positions, and a de-skew algorithm is used to remove the first-order linear component and DC component, obtaining the phase nonlinearity set of the digital phased array receiving channel. The application of the de-skew algorithm effectively removes interference factors, making the measurement results more accurately reflect the phase nonlinearity characteristics of the digital phased array receiving channel. As can be seen from the above analysis, this invention overcomes the limitations of existing technologies through multi-frequency signal generation, parallel processing, dynamic delay adjustment, precise peak search, and an effective de-skew algorithm, providing a reliable and efficient solution for the performance evaluation and calibration of digital phased array receiving channels. Therefore, this invention can achieve efficient, accurate, and low-cost measurement of the phase nonlinearity of digital phased array receiving channels.

[0061] As an improvement to the above embodiment, the generation of multiple baseband linear frequency modulated (LFM) signals with different center frequencies, and the separate digital-to-analog conversion, up-conversion, filtering, and signal amplification processing of each baseband LFM signal, to obtain a broadband radio frequency LFM signal set, including:

[0062] Using a signal generation module, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated; for each of these baseband LFM signals a i (t), whose expression is A represents the amplitude of the i-th signal, which determines the signal strength. The amplitude is chosen based on actual requirements and the dynamic range of the system; f 0i μ is the starting frequency of the i-th signal; different starting frequencies cause each signal to cover a different frequency range. i Let be the frequency modulation slope of the i-th signal, which determines the rate of change of the signal's frequency over time. The choice of the frequency modulation slope is determined by combining the signal's bandwidth and duration to meet the requirements of the time-bandwidth product. t is a time variable representing the signal's change over time. Assume that the bandwidth of each signal is the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel. The time-bandwidth product satisfies a specific optimization threshold, which is set to (360 / δ). 2 / 8, where δ is the phase nonlinearity measurement error; the time-width-bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the signal duration and frequency modulation slope, the time-width-bandwidth product can meet the threshold requirement, thereby improving the measurement accuracy.

[0063] Each baseband linear frequency modulated signal is individually converted from digital to analog, up-converted, filtered, and amplified to obtain a set of broadband radio frequency linear frequency modulated signals.

[0064] In this embodiment, a signal generation module generates multiple baseband linear frequency modulated (LFM) signals with different center frequencies. These are then processed through a series of steps to obtain a broadband radio frequency (RF) LFM signal set, providing the basic input signal for subsequent phase nonlinearity measurements of the digital phased array receiving channel. Measurement accuracy is improved by appropriately setting signal parameters and meeting specific time-bandwidth product threshold requirements. Simultaneously, each baseband LFM signal is processed individually to ensure accuracy and stability. Specifically, each baseband LFM signal undergoes individual digital-to-analog conversion, converting the digital signal to an analog signal. Then, an up-conversion operation is performed to boost the signal frequency to the RF band. Filtering is then applied to remove unwanted frequency components, ensuring signal purity. Finally, signal amplification is performed to obtain the broadband RF LFM signal set.

[0065] To facilitate understanding, an example is provided: In the field of wireless communication, the performance of a digital phased array receiver channel is crucial to communication quality. In this embodiment, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are first generated. Assuming a specific frequency range needs to be covered, different starting frequencies f are set according to the actual situation. 0i and frequency modulation slope μ iSimultaneously, ensuring the time-bandwidth product meets threshold requirements improves measurement accuracy. For example, in a communication system, minimizing phase nonlinearity measurement error is achieved by adjusting signal duration and frequency modulation slope to ensure the time-bandwidth product meets a specific optimized threshold. These baseband signals are then subjected to digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency linear frequency modulated (RFFM) signals. These signals are used as input signals to the digital phased array receiving channel for subsequent phase nonlinearity measurement and analysis. This allows for optimization and calibration of the receiving channel, improving communication system performance, reducing signal distortion and interference, and enhancing communication quality and reliability.

[0066] As an improvement to the above embodiments, the step of simultaneously inputting each signal in the broadband radio frequency linear frequency modulated signal set into the digital phased array receiving channel, so as to perform 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, includes:

[0067] Let the set of broadband radio frequency linear frequency modulated signals be B = {b1(t), b2(t), ..., b n Each signal in (t)} is simultaneously input into the digital phased array receiving channel; this is achieved through multiplexers or parallel connections to ensure that each signal enters the receiving channel at the same time.

[0068] Each input signal undergoes independent processing steps, 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, and each c i (n) corresponds to the input signal b i (t) Processed baseband data; wherein, the down-conversion operation converts the radio frequency signal into a baseband signal; for signal b i (t), down-conversion is achieved by mixing the signal 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, The signal after down-conversion is the input radio frequency signal b. i (t) The baseband signal obtained after mixing with the local oscillator signal; b i (t) is the input radio frequency signal, exp(-j2πf LOi t) is the complex exponential signal generated by the local oscillator; f LOit is the frequency of the local oscillator, used to downconvert the radio frequency signal to baseband; t is the time variable, representing the change of the signal over time; filtering operation uses digital or analog filters to remove unwanted frequency components and improve the purity of the signal; signal amplification uses an amplifier to linearly amplify the signal to enhance its strength; digital preprocessing is achieved through an analog-to-digital converter, thereby converting the analog signal into a digital signal.

[0069] In this embodiment, all signals from the broadband radio frequency linear frequency modulated signal set are simultaneously input into the digital phased array receiving channel. A parallel processing architecture performs an independent series of processing steps on each input signal to obtain a multi-channel baseband data set, providing an accurate data foundation for subsequent phase nonlinearity measurements. The use of multiplexers or parallel connections ensures simultaneous signal input and independent processing of each signal, improving processing efficiency and data accuracy.

[0070] To facilitate understanding, an example is provided: In a radar system, the digital phased array receiving channel needs to process multiple different input signals. In this embodiment, a broadband radio frequency linear frequency modulated (RFLM) signal is simultaneously input into the receiving channel. Assume there are multiple targets to be detected, with different RF signals corresponding to different target echo signals. Through a parallel processing architecture, each signal undergoes independent down-conversion, filtering, amplification, and digital preprocessing. For example, for a specific RF signal, it is down-converted to baseband after mixing with a local oscillator, then filtered to remove clutter, amplified, and converted into a digital signal by 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 target detection and tracking capabilities, and improving the reliability and accuracy of the radar system.

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

[0072] Each signal in the baseband linear frequency modulation (LFM) signal set is dynamically delayed by an integer multiple of the clock period to obtain a delayed LFM signal set; the delay time is dynamically adjusted based on the real-time total delay of the correction source generation module and the digital phased array receiving channel; where the clock period is set to T. c For each signal a in the baseband linear frequency modulated signal set i (t), its delayed signal d i The expression for (t) is: d i (t) is the delayed signal, which is obtained by dynamically delaying the baseband linear frequency modulated signal by an integer multiple of the clock cycle; Ai The amplitude of the i-th signal is consistent with the amplitude parameter in the baseband linear frequency modulated signal, which determines the signal strength; μ i is the frequency modulation slope of the i-th signal, consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal, which determines the rate of change of the signal's frequency over time; t is the time variable, representing the change of the signal over time; k i The dynamic integer delay factor k is the delay factor for the i′-th signal, used to adjust the delay time so that the delayed signal is time-aligned with the signal processed by the receiving channel; i The method for determining it is as follows:

[0073] First, measure the total real-time delay T of the measurement correction source generation module and the digital phased array receiving channel. delay It can be estimated by inserting a time measurement module into the system, or by utilizing known signal propagation and processing times.

[0074] Then, calculate the dynamic integer delay factor. in This indicates a rounding down function that ensures the delayed signal is as close as possible in time to the signal processed by the receiving channel.

[0075] In this embodiment, based on the real-time total delay of the digital phased array receiving channel, the signals in the baseband data set are dynamically delayed by an integer multiple of the clock cycle to obtain a delayed linear frequency modulated signal set. This ensures that the delayed signals are time-aligned with the signals processed by the receiving channel, preparing for subsequent phase nonlinearity calculations. By dynamically adjusting the delay, different system delay variations can be accommodated, improving the accuracy and stability of the measurement. Simultaneously, by accurately calculating the dynamic integer delay multiple, the signals are ensured to be aligned as closely as possible in time.

[0076] To facilitate understanding, an example is provided: In a communication system, the signal transmission delay of a digital phased array receiving channel may be unstable due to various factors. This embodiment dynamically adjusts the signal delay based on the real-time total delay. Assume a complex communication environment where the real-time total delay of the calibration source generation module and the receiving channel is constantly changing. By measuring this total delay, a dynamic integer delay multiple is calculated, allowing for precise delay adjustment of the baseband signal. This ensures that even in a dynamically changing environment, the delayed signal is time-aligned with the signal processed by the receiving channel, enabling accurate phase nonlinearity calculations. This helps improve the performance of the communication system, reduce signal distortion and interference, and ensure communication stability and reliability.

[0077] As an improvement to the above embodiments, the step of predicting the peak position of each data point in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through a peak search algorithm model, includes:

[0078] Each signal data point in the multi-channel baseband I / Q data set is cross-correlated with the corresponding signal in the delayed linear frequency modulated (LFM) signal set to obtain a correlation peak data set. Specifically, this step uses multi-dimensional cross-correlation to find the time delay relationship between the multi-channel baseband I / Q data and the delayed LFM signal, providing a foundation for subsequent phase nonlinearity calculations. For the multi-channel baseband I / Q data set C = {c1(n), c2(n), ..., c...}, ... n The set of time-delayed linear frequency modulated signals D = {d1(t), d2(t), ..., dn} and the set of time-delayed linear frequency modulated signals D = {d1(t), d2(t), ..., dn} n For each d, perform the following operations: i (t) is flipped in the time domain to obtain This means reversing the signal on the time axis to facilitate subsequent cross-correlation calculations; for c i (n) Perform the conjugate operation to obtain Taking the conjugate is to achieve signal matching in the cross-correlation operation; then, multidimensional convolution operation is performed, i.e. Here e i (n) represents the set of related peak data E = {e1(n), e2(n), ..., e...} n The elements in (n)} are obtained by taking the conjugate of the baseband data. and the inverted delayed linear frequency modulated signal The result obtained by performing multidimensional convolution operation reflects the correlation between the multi-channel baseband data and the delayed linear frequency modulated signal. Its peak position will be used for subsequent time delay estimation and phase nonlinearity calculation. n represents the discrete time point, which is used to traverse the correlated peak data in the time domain to determine the correlation at different time points. m is a variable used to traverse the baseband data after taking the conjugate. In the convolution operation, it, together with the time point n, determines the product and accumulation of the two signals at different time points. It is a multi-path baseband I / Q data set C = {c1(n), c2(n), ..., c n The element c in (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 their similarity in the convolution operation. For a set of time-delayed linear frequency modulated signals D = {d1(t), d2(t), ..., d...} n The element d in (t)}i (t) is the result obtained after time-domain flipping. Time-domain flipping reverses the signal on the time axis to match it with the conjugate baseband data in the convolution operation, thereby finding the time delay relationship between the two signals. This formula means performing a convolution operation on the conjugate baseband data and the flipped delayed linear frequency modulated signal to obtain the correlation peak data. Convolution can be viewed as a process of matching and integrating two signals in the time domain, which allows us to find the similarity and time delay between the two signals.

[0079] The peak search algorithm model searches for peaks in each data point in the relevant peak dataset to predict the peak position of each data point. Specifically, in this step, an intelligent peak search algorithm is used to search for peaks in each data point in the relevant peak dataset. This algorithm combines the use of algorithms such as Support Vector Machine (SVM) or Random Forest to classify and predict the peaks in the relevant peak data. Specifically, features of the relevant peak data, such as peak height, width, and symmetry, are extracted as input features, and then the peak position is predicted by a trained machine learning model.

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

[0081] I. Data Preprocessing: The relevant peak data set is normalized, mapping the data values ​​to the [0,1] interval for subsequent analysis and comparison. Let the original data be e. i (n), with a maximum value of e max The minimum value is e min Then the normalized data

[0082] II. Feature Extraction: Extract features from relevant peak data. These features may include, but are not limited to, the following aspects:

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

[0084] Peak width W: Determined by finding positions where the peak width drops to a certain percentage (e.g., half the peak height) on both sides. First, find the peak position n. p Make e i (n p = H. Let H / 2 be half the peak height. Search outwards from the peak position to both sides to find the 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 between the data on both sides of the peak, which can be measured by comparing the differences in data values ​​at corresponding positions on both sides of the peak. Specifically, it calculates the sum of differences on the left side. Calculate the sum of differences on the right. symmetry When the left and right sides are perfectly symmetrical, S = 1. The greater the difference, the closer S is to 0.

[0086] Local rate of change R: Calculates the rate of change of data near the peak, reflecting the sharpness of the peak. Specifically, a small range [n] is selected near the peak location. p -Δn, n p +Δn], calculate the sum of the absolute values ​​of the first differences of the data within this range.

[0087] III. 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] Choosing a machine learning algorithm: You can choose from algorithms such as Support Vector Machine (SVM), Random Forest, and Neural Network. Here, we will take Random Forest as an example.

[0090] Model Training: The random forest model is trained using training data, and the model's parameters are adjusted to improve prediction accuracy. Training Phase: Assuming 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 Label Composition. A random forest consists of multiple decision trees. For training each tree, n samples (usually n < N) are randomly drawn with replacement from N samples as the training set for that tree. At each node, when splitting, a subset of features is randomly selected from the four features of the feature vector (peak height, peak width, symmetry, and local rate of change). The optimal splitting features and split point are chosen based on metrics such as information gain. This process is repeated to construct multiple decision trees, forming a random forest.

[0091] Prediction phase: For new relevant peak data, extract its feature vector x. new =(H new W new S new Rnew ). x new Input each decision tree in the random forest and obtain the predicted peak position for each decision tree. (t represents the t-th tree). The final predicted peak location is... Where T is the number of decision trees in the random forest.

[0092] IV. Peak Search: For new relevant peak data, extract its features. Input the features into the trained machine learning model to obtain the predicted peak positions.

[0093] In this embodiment, a set of correlated peak data is obtained by performing multi-dimensional cross-correlation operations on a multi-path baseband I / Q data set and a set of delayed linear frequency modulated (LFM) signals. Then, an intelligent peak search algorithm is used to predict the peak position of each correlated peak data. Specifically, the multi-dimensional cross-correlation operation involves time-domain flipping, conjugation, and multi-dimensional convolution operations on the multi-path baseband data and the delayed LFM signals to accurately find the time delay relationship between the signals, laying the foundation for subsequent phase nonlinearity calculations. The intelligent peak search algorithm combines machine learning techniques, extracting features of the correlated peak data (such as peak height, width, symmetry, and local rate of change) and using algorithms like random forests for training and prediction, thus improving the accuracy and efficiency of peak position prediction.

[0094] To facilitate understanding, an example is provided: In a digital phased array radar system, it is necessary to measure the phase nonlinearity of the receiving channel. First, multiple baseband I / Q data and delayed linear frequency modulated signals are generated. Then, the peak positions of the correlated peak data are determined using the aforementioned multidimensional cross-correlation operation and intelligent peak search algorithm. Assume a set of correlated peak data, after data preprocessing and feature extraction, yields features such as peak height H = 5, peak width W = 10, symmetry S = 0.8, and local rate of change R = 20. These features are input into a trained random forest model. Based on the previously learned relationship between features and peak positions, the model predicts the peak position of the correlated peak data. In this way, the peak position of each correlated peak data can be accurately found, and the phase difference can be calculated. This provides key parameters for subsequent removal of the first-order linear component and DC component to obtain the set of phase nonlinearities 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 to the above embodiment, the step of calculating the phase difference set based on the peak position of each data point in the relevant peak data set includes:

[0096] Interpolation is performed at the peak position of each data point in the relevant peak data set to obtain the delay set at the peak. Specifically, this step involves performing high-precision interpolation, for example, using cubic spline interpolation. Let y(x) be the function of the relevant peak data within a certain interval [x1, x2]. A cubic polynomial function S(x) can be constructed using cubic spline interpolation, such 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 finer interpolation near the peak, improving the accuracy of delay estimation; resulting in the delay set F = {f1, f2, ..., f} at the peak. n}, where f i This represents the estimated delay value of the corresponding signal;

[0097] Based on the delay set and dynamic integer multiple delay, a set of synchronous linear frequency modulated (LFM) signals is generated. Specifically, this step involves generating a set of LFM signals synchronized with multiple baseband data streams, based on the delay set and dynamic integer multiple delay, to provide an accurate reference signal for subsequent phase nonlinearity calculations. The delay set F = {f1, f2, ..., f...} is used to generate the LFM signals. n} and dynamic integer multiple delays, generating a set of synchronous linear frequency modulated signals G = {g1(t), g2(t), ..., g n (t)};For the i-th synchronous linear frequency modulation signal g i (t), whose expression is A i f is the amplitude of the i-th signal, which determines the signal strength and is usually set during signal generation based on system requirements and signal propagation characteristics; 0i is the starting frequency of the i-th signal. Different starting frequencies give each signal a specific position in the frequency domain, used to cover different frequency ranges to improve measurement accuracy; μ i is the frequency modulation slope of the i-th signal, which determines the rate of change of the signal's frequency over time and is related to parameters such as the signal's bandwidth and duration; t is the time variable, representing the signal's change over time; f i k is the i-th delay value in the delay set F, representing the time delay of the corresponding signal determined after preprocessing; i T is the dynamic integer delay multiple of the i-th signal, used to further adjust the signal delay for more precise synchronization; c It is the clock cycle, which is the basic unit of time measurement in the system and is used to determine the accuracy of the delay;

[0098] For the set of synchronous linear frequency modulated signals G and the set of multiple baseband I / Q data C = {c1(n), c2(n), ..., c...} n (n)} Phase calculations are performed on the corresponding signal data in both sets to obtain a set of phase calculation results for both sets; specifically, in this step: phase calculations are performed on each signal in the synchronous linear frequency modulated 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 modulated signal g i The phase of (t) is calculated as follows: Here, ∠ represents the argument of a complex number, i.e., the phase of the signal; by analyzing the expression of the synchronous linear frequency modulated 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 follows Multi-channel baseband I / Q data is usually represented in complex form, and the corresponding data phase is obtained by calculating its argument.

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

[0100] In this embodiment, interpolation is first performed at the peak positions of the relevant peak data set to obtain a delay set. Then, a synchronous linear frequency modulated (LFM) signal set is generated based on this delay set. Next, the phases of the corresponding signals in the synchronous LFM signal set and the multi-channel baseband I / Q data set are calculated respectively. Finally, the phase difference set between the two is calculated. The technical innovation lies in using cubic spline interpolation to improve the delay estimation accuracy, and in accurately measuring phase nonlinearity by generating synchronous LFM 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] To facilitate understanding, an example is provided: In a wireless communication system, the phase nonlinearity of the digital phased array receiving channel can affect the signal reception quality. This embodiment allows for the precise measurement of the phase nonlinearity of the receiving channel. Assume that at a certain moment, a set of relevant peak data is obtained. After cubic spline interpolation, a delay set F at the peak value is obtained. Then, a set of synchronous linear frequency modulated (LFM) signals G is generated based on these delay values. Next, the phase of the LFM signals and the multiple baseband data streams is calculated; for example, the phase of the LFM signals at a certain moment... The corresponding phase of the multi-channel baseband I / Q data Then their phase difference Δφ i (n) = 0.2. By performing this calculation on multiple data points, the phase difference set ΔΦ is obtained. This phase difference set reflects the phase nonlinearity 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 to the above embodiment, the step of using a de-skew algorithm to remove the first-order linear component and DC component from the phase difference set to obtain the phase nonlinearity set of the digital phased array receiving channel includes:

[0103] For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n Performing a Fast Fourier Transform on (n)} yields the frequency domain representation ΔΦ(f); where, let Δφ i The discrete Fourier transform of (n) is ΔΦ i (f), then N is the data length, i.e., the number of data points in the phase difference set; f is the frequency variable used for frequency domain analysis; n represents the index of discrete time points in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at frequency f in the frequency domain after discrete Fourier transform, and is the sum of all ΔΦ values. i The sum of (f);

[0104] Analyze frequency domain data to determine the characteristics of the first-order linear component and the DC component; among which, the DC component characteristic is: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristic is: observe the changing trend of the frequency domain data in the low-frequency part; assuming that the first-order linear component is expressed in the frequency domain as a1f+b1, the coefficients a1 and b1 are determined by fitting the data in 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 This 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, the transfer function value is 1, indicating that the input signal at that frequency passes through the filter with almost no attenuation; when f ≤ f cutoff When the value is 0, the transfer function value is 0, indicating that the input signal at that frequency is completely attenuated;

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

[0107] For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the deskewed 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 the DC component, the calculation formula is:

[0108] In this embodiment, by performing spectral analysis on the phase difference set, the characteristics of the first-order linear component and the DC component are determined. Then, a high-pass filter and a linear phase filter are designed to remove the DC component and the first-order linear component, respectively, ultimately obtaining the phase nonlinearity set of the digital phased array receiving channel. The technical innovation lies in the use of a deskewing algorithm based on spectral analysis and filter design, which can more accurately remove interference components from the phase difference set and improve the accuracy of phase nonlinearity measurement. This method combines Fast Fourier Transform (FFT), filter design, and Inverse Fast Fourier Transform (IFFT) to achieve precise processing of complex signals.

[0109] To facilitate understanding, an example is provided: In satellite communication, the phase nonlinearity of the digital phased array receiving channel can affect signal transmission quality. This embodiment allows for precise measurement and correction of the phase nonlinearity of the receiving channel. Assuming a set of phase differences is obtained, after a Fast Fourier Transform (FFT), significant DC and first-order linear components are found in the frequency domain. These interference components are removed by designing appropriate high-pass and linear phase filters. For example, the cutoff frequency of the high-pass filter is set to an appropriate value to effectively remove the DC component; the linear phase filter is designed based on the slope estimate of the first-order linear component to accurately remove it. After an inverse Fast Fourier Transform (IFFT), the phase nonlinearity data after interference removal is obtained. Based on this data, the digital phased array receiving channel in the satellite communication system can be optimized and calibrated, improving signal reception quality, reducing the bit error rate, and ensuring communication stability and reliability. This provides better protection for both high-definition video transmission and critical data communication, enhancing the user experience in satellite communication.

[0110] See Figure 2 This is a schematic diagram of a device for measuring the phase nonlinearity of a digital phased array receiving channel according to an embodiment of the present invention. The device for measuring the phase nonlinearity of a digital phased array receiving channel includes:

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

[0112] Signal processing module 11 is used to simultaneously input each signal in the broadband radio frequency linear frequency modulation signal set into the digital phased array receiving channel, so that each input signal can be independently down-converted, filtered, amplified and digitally preprocessed 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, so as to obtain a delayed linear frequency modulated signal set.

[0114] The peak measurement module 13 is used to measure the peak position of each data in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through the peak search algorithm model.

[0115] Calculation module 14 is used to calculate the phase difference set based on the peak position of each data in the relevant peak data set;

[0116] The removal module 15 is used to remove the first-order linear component and DC component of the phase difference set using a de-skewing algorithm to obtain the phase nonlinear 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 modulated (LFM) signals with different center frequencies are generated and subjected to digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency LFM signals. This approach allows for wider frequency coverage, enabling more comprehensive detection of the phase nonlinearity characteristics of the digital phased array receiving channel through multiple signals at different frequencies. Compared to traditional methods, this scheme is better suited to the complex systems of digital phased arrays because it can generate signals appropriate for the system. Then, these broadband radio frequency LFM signals are simultaneously input into the digital phased array receiving channel. 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. Simultaneous input and parallel processing of multiple signals significantly improves measurement efficiency and avoids the inefficiency of measuring each signal individually in traditional methods. Furthermore, a series of preprocessing operations yields more accurate baseband data. Next, based on 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 LFM signal set. This dynamic delay adjustment ensures accurate signal alignment in time, providing a reliable basis for subsequent phase calculations. This solves the measurement error problem caused by signal asynchrony in existing technologies. Subsequently, using a multi-channel baseband I / Q data set and a delayed linear frequency modulated signal set, a peak position of each data point in the relevant peak data set is determined using a peak search algorithm model. This precise peak search algorithm accurately finds the correlation peaks between signals, improving measurement accuracy. Finally, the phase difference set is calculated based on the peak positions, and a de-skew algorithm is used to remove the first-order linear component and DC component, obtaining the phase nonlinearity set of the digital phased array receiving channel. The application of the de-skew algorithm effectively removes interference factors, making the measurement results more accurately reflect the phase nonlinearity characteristics of the digital phased array receiving channel. As can be seen from the above analysis, this invention overcomes the limitations of existing technologies through multi-frequency signal generation, parallel processing, dynamic delay adjustment, precise peak search, and an effective de-skew algorithm, providing a reliable and efficient solution for the performance evaluation and calibration of digital phased array receiving channels. Therefore, this invention can achieve efficient, accurate, and low-cost measurement of the phase nonlinearity of digital phased array receiving channels.

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

[0120] See Figure 3 This is a schematic diagram of a device for measuring the phase nonlinearity of a digital phased array receiving channel according to an embodiment of the present invention. The device 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 program for measuring the phase nonlinearity of a digital phased array receiving channel. When the processor 100 executes the computer program, it implements the steps in the various embodiments of the methods for measuring the phase nonlinearity of a digital phased array receiving channel described above. Alternatively, when the processor 100 executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0121] For example, 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 performing specific functions, which describe the execution process of the computer program in the measurement device for the phase nonlinearity of the digital phased array receiving channel.

[0122] The device for measuring the phase nonlinearity of the digital phased array receiving channel can be a control device for the digital phased array, a desktop computer, a laptop, a handheld computer, or a cloud server, etc. This device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the schematic diagram is merely an example of a device for measuring the phase nonlinearity of the digital phased array receiving channel and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the device may also include input / output devices, network access devices, buses, etc.

[0123] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the digital phased array receiving channel phase nonlinearity measurement device, connecting all parts of the device via various interfaces and lines.

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

[0125] The module / unit integrated into the digital phased array receiving channel phase nonlinearity measurement device, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0126] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0127] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for measuring the phase nonlinearity of a digital phased array receiving channel, characterized in that, include: Multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated, and each baseband LFM signal is subjected to separate digital-to-analog conversion, up-conversion, filtering, and signal amplification to obtain a set of broadband radio frequency LFM signals. Each signal in the broadband radio frequency linear frequency modulation signal set is simultaneously input into the digital phased array receiving channel, so that each input signal is independently down-converted, filtered, amplified and digitally preprocessed through the receiving channel to obtain a multi-channel baseband I / Q data set; Based on 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 modulated signal set; Based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through the peak search algorithm model, the peak position of each data in the relevant peak data set is measured; Based on the peak position of each data point in the relevant peak data set, the phase difference set is calculated; The phase difference set is processed by a deskewing algorithm to remove the first-order linear component and the DC component, resulting in the phase nonlinearity set of the digital phased array receiving channel. The step of predicting the peak position of each data point in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through a peak search algorithm model, includes: Perform multidimensional cross-correlation operation between each signal data in the multi-path baseband I / Q data set and the corresponding signal in the delayed linear frequency modulated signal set to obtain the correlation peak data set; The peak search algorithm model is used to search for the peak position of each data point in the relevant peak data set and predicts the peak position of each data point. The peak search algorithm model is a random forest model that is pre-trained to find the peak position.

2. The method for measuring the phase nonlinearity of a digital phased array receiving channel as described in claim 1, characterized in that, The step of calculating the phase difference set based on the peak position of each data point in the relevant peak data set includes: Interpolation is performed at the peak position of each data point in the relevant peak data set to obtain the delay set at the peak. Based on the delay set and dynamic integer multiple delay, a set of synchronous linear frequency modulation signals is generated; Phase calculations are performed on the corresponding signal data in both the synchronous linear frequency modulated signal set and the multi-channel baseband I / Q data set to obtain a set of phase calculation results for both. Based on the set of phase calculation results of the two, the set of phase differences between the two is calculated.

3. The method for measuring the phase nonlinearity of the digital phased array receiving channel as described in claim 1, characterized in that, The de-skew algorithm is used to remove the first-order linear component and DC component from the phase difference set, resulting in the phase nonlinearity set of the digital phased array receiving channel, including: For the phase difference set ΔΦ={Δφ1(n), Δφ2(n),..., Δφ n Performing a Fast Fourier Transform on (n)} yields the frequency domain representation ΔΦ(f); where, let Δφ i The discrete Fourier transform of (n) is ΔΦ i (f), then N is the data length, i.e., the number of data points in the phase difference set; f is the frequency, used for frequency domain analysis; n represents the index of discrete time points in the time domain; Δφ i (n) represents the value of the i-th phase difference data in the phase difference set at discrete time point n in the time domain; ΔΦ i (f) represents the value of the i-th phase difference data at frequency f in the frequency domain after discrete Fourier transform; ΔΦ(f) represents the value of the entire phase difference set at frequency f in the frequency domain after discrete Fourier transform, and is the sum of all ΔΦ values. i The sum of (f); Analyze frequency domain data to determine the characteristics of the first-order linear component and the DC component; where, the DC component characteristic is: the amplitude value ΔΦ(0) at f=0 is the amplitude of the DC component; the first-order linear component characteristic is: observe the changing trend of the frequency domain data in the low-frequency part; assuming that the first-order linear component is expressed in the frequency domain as a1f+b1, the coefficients a1 and b1 are determined by fitting the data in 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 The cutoff frequency of the high-pass filter is ΔΦ; 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 frequency; when the frequency f > f cutoff When f ≤ f, the transfer function value is 1, indicating that the input signal at that frequency passes through the filter with almost no attenuation; when f ≤ f cutoff When the value is 0, the transfer function value is 0, indicating that the input signal at that frequency is completely attenuated; Design a linear-phase filter to remove the first-order linear component: where the transfer function of the linear-phase filter is designed as 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, and b2 is the slope estimate of the first-order linear component to be removed, obtained by linear fitting of the low-frequency part; the purpose of this transfer function is to adjust according to the slope of the first-order linear component to remove it; where the coefficient a2 = -a1, and b2 is adjusted as needed to ensure the linear phase characteristics of the filter; the filtered frequency domain data is then passed through this filter again to obtain ΔΦ. final (f)=ΔΦ filtered (f)H lp (f); For ΔΦ final (f) Perform inverse fast Fourier transform to obtain the deskewed 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 the DC component, the calculation formula is:

4. The method for measuring the phase nonlinearity of a digital phased array receiving channel as described in claim 1, characterized in that, The process generates multiple baseband linear frequency modulated (LFM) signals with different center frequencies, and performs individual digital-to-analog conversion, up-conversion, filtering, and signal amplification on each baseband LFM signal to obtain a set of broadband radio frequency LFM signals, including: Using a signal generation module, multiple baseband linear frequency modulated (LFM) signals with different center frequencies are generated; for each of these baseband LFM signals a i (t), whose expression is A i f represents the amplitude of the i-th signal; 0i μ is the starting frequency of the i-th signal; different starting frequencies cause each signal to cover a different frequency range. i Let be the frequency modulation slope of the i-th signal, which determines the rate of change of the signal's frequency over time. The choice of the frequency modulation slope is determined by combining the signal's bandwidth and duration to meet the requirements of the time-bandwidth product. t is a time variable representing the signal's change over time. Assume that the bandwidth of each signal is the same as or slightly wider than the channel bandwidth of the digital phased array receiving channel. The time-bandwidth product satisfies a specific optimization threshold, which is set to (360 / δ). 2 / 8, where δ is the phase nonlinearity measurement error; the time-width-bandwidth product is TB, where T is the signal duration, B = |μ i T| is the signal bandwidth; by adjusting the signal duration and frequency modulation slope, the time-width-bandwidth product can meet the threshold requirement, thereby improving the measurement accuracy. Each baseband linear frequency modulated signal is individually converted from digital to analog, up-converted, filtered, and amplified to obtain a set of broadband radio frequency linear frequency modulated signals.

5. The method for measuring the phase nonlinearity of a digital phased array receiving channel as described in claim 1, characterized in that, The process involves simultaneously inputting each signal from the broadband radio frequency linear frequency modulated signal set into a digital phased array receiving channel. Each input signal undergoes independent down-conversion, filtering, signal amplification, and digital preprocessing through the receiving channel to obtain a multi-channel baseband I / Q data set, including: Let the set of broadband radio frequency linear frequency modulated signals be B = {b1(t), b2(t), ..., b n Each signal in (t)} is simultaneously input into the digital phased array receiving channel; this is achieved through multiplexers or parallel connections to ensure that each signal enters the receiving channel at the same time. Each input signal undergoes independent processing steps, 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, and each c i (n) corresponds to the input signal b i (t) Processed baseband data; wherein, the down-conversion operation converts the radio frequency signal into a baseband signal; for input signal b i (t), down-conversion is achieved by mixing the signal 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, This represents the signal after down-conversion, which is the input signal b. i (t) The baseband signal obtained after mixing with the local oscillator signal; exp(-j2πf LOi t) is the complex exponential signal generated by the local oscillator; f LOi t is the frequency of the local oscillator, used to downconvert the radio frequency signal to baseband; t is the time variable, representing the change of the signal over time; filtering operation uses digital or analog filters to remove unwanted frequency components and improve the purity of the signal; signal amplification uses an amplifier to linearly amplify the signal to enhance its strength; digital preprocessing is achieved through an analog-to-digital converter, thereby converting the analog signal into a digital signal.

6. The method for measuring the phase nonlinearity of a digital phased array receiving channel as described in claim 1, characterized in that, The method of dynamically delaying each signal data in the baseband I / Q data set by an integer multiple of the clock cycle based on the real-time total delay of the digital phased array receiving channel to obtain a delayed linear frequency modulated signal set includes: Each signal in the baseband linear frequency modulation (LFM) signal set is dynamically delayed by an integer multiple of the clock period to obtain a delayed LFM signal set; the delay time is dynamically adjusted based on the real-time total delay of the correction source generation module and the digital phased array receiving channel; where the clock period is set to T. c For each signal a in the baseband linear frequency modulated signal set i (t), its delayed signal d i The expression for (t) is: d i (t) is the delayed signal, which is obtained by dynamically delaying the baseband linear frequency modulated signal by an integer multiple of the clock cycle; A i The amplitude of the i-th signal is consistent with the amplitude parameter in the baseband linear frequency modulated signal, which determines the signal strength; μ i is the frequency modulation slope of the i-th signal, consistent with the frequency modulation slope parameter in the baseband linear frequency modulation signal, which determines the rate of change of the signal's frequency over time; t is the time variable, representing the change of the signal over time; k i The dynamic integer delay factor k is used to adjust the delay time of the i-th signal, ensuring that the delayed signal is time-aligned with the signal processed by the receiving channel; i The method for determining it is as follows: First, measure the total real-time delay T of the measurement correction source generation module and the digital phased array receiving channel. delay Estimate using a time measurement module, or by utilizing known signal propagation and processing times; Then, calculate the dynamic integer delay factor. in This indicates a rounding down function that ensures the delayed signal is as close as possible in time to the signal processed by the receiving channel.

7. A measuring device for phase nonlinearity of a digital phased array receiving channel, characterized in that, include: The signal generation module is used to generate multiple baseband linear frequency modulated signals with different center frequencies, and to perform separate digital-to-analog conversion, up-conversion, filtering and signal amplification on each baseband linear frequency modulated signal to obtain a set of broadband radio frequency linear frequency modulated signals. The signal processing module is used to simultaneously input each signal in the broadband radio frequency linear frequency modulation signal set into the digital phased array receiving channel, so that each input signal can be independently down-converted, filtered, amplified and digitally preprocessed through the receiving channel to obtain a multi-channel baseband I / Q data set; The 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 based on the real-time total delay of the digital phased array receiving channel, so as to obtain a delayed linear frequency modulated signal set. The peak measurement module is used to measure the peak position of each data point in the relevant peak data set based on the multi-channel baseband I / Q data set and the delayed linear frequency modulated signal set, and through the peak search algorithm model. The calculation module is used to calculate the phase difference set based on the peak position of each data point in the relevant peak data set; The removal module is used to remove the first-order linear component and DC component of the phase difference set using a deskewing algorithm, so as to obtain the phase nonlinear set of the digital phased array receiving channel. Specifically, the peak measurement module is used for: Perform multidimensional cross-correlation operation between each signal data in the multi-path baseband I / Q data set and the corresponding signal in the delayed linear frequency modulated signal set to obtain the correlation peak data set; The peak search algorithm model is used to search for the peak position of each data point in the relevant peak data set and predicts the peak position of each data point. The peak search algorithm model is a random forest model that is pre-trained to find the peak position.

8. A measuring device for phase nonlinearity of a digital phased array receiving channel, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method for measuring the phase nonlinearity of a digital phased array receiving channel as described in any one of claims 1 to 6.

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