GaAs-based W-band SSPP filter performance evaluation method and system

Abstract

The invention discloses a W-band SSPP filter performance evaluation method and system based on GaAs, relates to the technical field of radio frequency and microwave communication, improves the high-precision evaluation capability of frequency deviation and phase change through multi-point sampling and parallel data processing, and solves the problem that the precision of a traditional system is limited in W-band frequency response. The interference suppression module effectively obstructs thermal noise and electromagnetic interference by using a multi-layer shielding and signal isolation technology, and calculates a signal purity index Sjch by collecting parameters such as signal total intensity Xzqd, environmental noise amplitude Znf and random noise component Rns in real time so as to improve the anti-interference capability of the system; besides, the rapid data processing module adopts parallel processing and feature compression, analyzes and compresses a series of lower parameters of frequency fluctuation amplitude Fwd and phase change rate PhR, and generates advanced real-time data, so that the data processing pressure is reduced, and the system can realize more efficient real-time performance evaluation under the high-frequency requirement.

Description

Technical Field

[0001] The present invention relates to the field of radio frequency and microwave communication technology, and in particular to a W-band SSPP filter performance evaluation method and system based on GaAs. Background Art

[0002] The background technology of the W-band SSPP (based on periodic surface of sub-waveguide structure) filter performance evaluation system originates from the demand for millimeter wave and sub-millimeter wave frequency band signal processing. The W-band frequency range is widely used in high-speed communication, imaging radar, aerospace exploration and other fields, but high-frequency signals are often affected by loss, interference and other problems. The SSPP filter utilizes the sub-waveguide characteristics of the periodic surface structure, has the advantages of suppressing clutter and selective filtering, and exhibits the characteristics of low loss, high selectivity and wide bandwidth in high-frequency applications. The SSPP filter based on GaAs material further improves the working stability and frequency range of the device due to its high electron mobility and excellent electrical properties. To ensure the effectiveness of the filter in different environments, the performance evaluation system measures key parameters such as insertion loss, return loss, and frequency response to achieve accurate evaluation and optimization of the filter.

[0003] The W-band SSPP filter performance evaluation system has a wide range of applications, but when used in high-frequency communications, it often has the following technical disadvantages:

[0004] 1. Limited evaluation accuracy: The W band is in a relatively high frequency range, and the evaluation system requires extremely high frequency resolution. However, existing evaluation systems often have difficulty achieving high-precision measurements when dealing with small frequency offsets and phase changes, which may lead to deviations in performance evaluation results.

[0005] 2. Insufficient anti-interference ability: W-band frequencies are more sensitive to interference from the external environment, such as thermal noise, electromagnetic interference, etc. Existing systems usually have deficiencies in interference filtering and isolation, which affects the evaluation results of the actual performance of the filter.

[0006] 3. Data processing speed bottleneck: The high-frequency response and precise frequency selectivity of the SSPP filter lead to the generation of a large amount of data. The existing evaluation system is difficult to match the high-frequency requirements in terms of data processing speed, resulting in poor real-time evaluation results. Summary of the invention

[0007] The present invention aims to provide a GaAs-based W-band SSPP filter performance evaluation method and system to solve the above-mentioned problems.

[0008] The present invention is achieved through the following technical solutions:

[0009] A W-band SSPP filter performance evaluation system based on GaAs, comprising a signal acquisition module, a fast data acquisition module, a frequency resolution module, an interference suppression module and a performance evaluation module;

[0010] The signal acquisition module is used to collect high-frequency response signals in real time through a W-band SSPP filter constructed of GaAs material, perform multi-point sampling for small frequency offsets and phase changes, and generate high-precision spectrum data, and finally establish a primary real-time tracking data set for the multi-point sampled spectrum data;

[0011] The fast data processing module is used to process the primary real-time tracking data set; by using parallel data processing technology and compression coding technology, the spectrum data in the primary real-time tracking data set is quickly cleaned and classified; then, the key spectrum feature related data in the primary real-time tracking data set is extracted by a feature extraction algorithm, and the data compression index Sjy is calculated and evaluated, and the redundant data is compressed and then the advanced real-time tracking data set is reconstructed;

[0012] The frequency resolution module is used to refine the advanced real-time tracking data set, calculate the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd of the adjacent frequency offset by analyzing the change of the specific frequency offset, and associate the high-resolution evaluation coefficient Fgrc with the phase deviation parameter Fxxd, and finally construct the frequency response accuracy index value Fzjb;

[0013] The interference suppression module is used to adjust the interference susceptibility of the W-band frequency under different environmental conditions by using multi-layer shielding and signal isolation technology, and at the same time construct a signal purity index value Sjch;

[0014] The performance evaluation module is used to fit the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

[0015] Preferably, during the signal acquisition process, the signal acquisition module sets a number of sampling points for the slight frequency offset and phase change of the W-band signal, performs multi-point synchronous sampling according to a preset sampling frequency, and captures the subtle change characteristics in the high-frequency response signal through a multi-point sampling method;

[0016] The generated spectrum data is organized and formatted to eventually construct a primary real-time tracking data set; the primary real-time tracking data set is sorted according to time series to form continuous and traceable real-time spectrum data, and finally the primary tracking data set is transmitted in real time to the fast data processing module through the internal data transmission channel.

[0017] Preferably, the fast data processing module includes a parallel processing unit and a feature compression unit;

[0018] The parallel processing unit is used to perform preliminary and rapid cleaning and classification of the spectrum data in the primary tracking data set; by adopting parallel data processing technology, the data is classified according to frequency and phase changes and redundant noise data is cleared.

[0019] Preferably, the feature compression unit is used to calculate the data compression index Sjy; by extracting key spectrum feature related data in the real-time tracking data set, including the frequency fluctuation amplitude Fwd, the phase change rate PhR, the noise energy ratio Nrl and the peak power density Ppd, the data compression index Sjy is calculated using the following formula:

[0020]

[0021] The redundant data is compressed by comparing and evaluating the preset data compression threshold Q with the data compression index Sjy. The specific evaluation contents are as follows:

[0022] When the data compression index Sjy>data compression threshold Q, it means that there is redundant information in the current data, and the current data is compressed at this time;

[0023] When the data compression index Sjy ≤ the data compression threshold Q, it indicates that there is no redundant information in the current data and no further compression is required.

[0024] Preferably, the frequency resolution module includes a frequency offset analysis unit and a phase accuracy calculation unit;

[0025] The frequency offset analysis unit is used to receive the advanced real-time tracking data set, extract the offset changes of adjacent frequency points and perform resolution analysis, and obtain the offset change related data in real time, including the frequency offset Xpl of adjacent frequency points, the phase difference Xpc of adjacent frequency points and the normalized signal strength ratio Xhq;

[0026] Extract the advanced real-time tracking data set and the offset change related data, and calculate the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd by the following formulas:

[0027]

[0028] Wherein, Zpl represents the center frequency value in the advanced real-time tracking data set; Plb represents the frequency step in the advanced real-time tracking data set; Pmax represents the maximum frequency offset value in the advanced real-time tracking data set; Zsf represents the noise floor amplitude in the advanced real-time tracking data set;

[0029] ln means natural logarithm.

[0030] Preferably, the phase accuracy calculation unit is used to calculate the frequency response accuracy index value Fzjb, and the specific calculation formula is as follows:

[0031]

[0032] Preferably, the interference suppression module first uses a multi-layer shielding technology to perform preliminary shielding processing on the W-band frequency signal, wherein the multi-layer shielding material is arranged in layers according to the characteristics of the signal frequency band, so that different layers of materials can effectively absorb or reflect interference signals in a specific frequency band; then the shielded signal is further processed by a signal isolation technology, including inserting an isolator in the W-band signal path to create an independent signal channel to block the entry of external thermal noise and electromagnetic signals, and the configuration of the isolator is adjusted according to the frequency characteristics of the W-band; after the signal isolation processing is completed, the total signal strength Xzqd is recorded in real time, and then the interference noise related data is collected and the environmental noise amplitude Znf and the random noise component Rns are extracted therefrom;

[0033] The signal purity index value Sjch is obtained by fitting the total signal intensity Xzqd, the environmental noise amplitude Znf and the random noise component Rns. The specific calculation formula is as follows:

[0034]

[0035] Here, 1 represents the highest purity of the signal and is used as an ideal reference value without interference.

[0036] Preferably, the performance evaluation module includes a performance fitting unit and a performance judgment unit;

[0037] The performance fitting unit is used to receive the frequency response accuracy index Fzjb and the signal purity index Sjch, and calculate the comprehensive performance evaluation index Pzpj of the filter through the following formula:

[0038]

[0039] Preferably, the performance judgment unit compares and evaluates the preset performance evaluation threshold value Px with the comprehensive performance evaluation index Pzpj, and the specific evaluation content is as follows:

[0040] When the performance evaluation threshold Px ≤ the comprehensive performance evaluation index Pzpj, it indicates that the frequency response and signal purity of the filter have reached the set performance requirements; it indicates that the filter performs normally in the current environment and operates normally in high-frequency communication;

[0041] When the performance evaluation threshold Px>the comprehensive performance evaluation index Pzpj, it means that the frequency response and signal purity of the filter do not meet the set performance requirements, indicating that the filter performance is abnormal in the current environment; at this time, a performance warning signal is issued, indicating that the filter is subject to external interference or insufficient design parameters, thereby affecting its reliability in high-frequency communications.

[0042] A W-band SSPP filter performance evaluation method based on GaAs comprises the following steps:

[0043] Step 1: Use the W-band SSPP filter constructed of GaAs material to collect high-frequency response signals in real time, perform multi-point sampling for small frequency offsets and phase changes, and generate high-precision spectrum data. Finally, establish a primary real-time tracking data set for the multi-point sampled spectrum data;

[0044] Step 2: Process the primary real-time tracking data set; quickly clean and classify the spectrum data in the primary real-time tracking data set by using parallel data processing technology and compression coding technology; then extract the key spectrum feature related data in the primary real-time tracking data set by using the feature extraction algorithm, calculate and evaluate the data compression index Sjy, compress the redundant data and reconstruct the advanced real-time tracking data set;

[0045] Step 3: refine the advanced real-time tracking data set, calculate the high-resolution evaluation coefficient Fgrc and phase deviation parameter Fxxd of adjacent frequency offsets through resolution analysis of specific frequency offset changes, associate the high-resolution evaluation coefficient Fgrc with the phase deviation parameter Fxxd, and finally construct the frequency response accuracy index value Fzjb;

[0046] Step 4: By using multi-layer shielding and signal isolation technology, the interference susceptibility of the W-band frequency under different environmental conditions is adjusted, and the signal purity index value Sjch is constructed;

[0047] Step 5. Fit the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] 1. In solving the problem of limited evaluation accuracy, the present invention obtains the spectrum data of the high-frequency response signal through multi-point synchronous sampling of the signal acquisition module; the fast data processing module performs feature extraction and real-time tracking through parallel processing and compression coding technology; the frequency resolution module uses the frequency offset analysis unit and the phase accuracy calculation unit to refine and analyze key parameters such as the frequency offset Xpl, phase difference Xpc and normalized signal intensity ratio Xhq of adjacent frequency points; thereby calculating the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd and constructing the frequency response accuracy index value Fzjb; the system realizes high-precision measurement of small frequency offsets and phase changes within the W-band frequency range, effectively improving the accuracy of frequency response accuracy evaluation;

[0050] 2. In solving the problem of insufficient anti-interference ability, the interference suppression module of the present invention adopts multi-layer shielding technology to absorb or reflect interference signals of a specific frequency band in material structures at different levels; uses signal isolation technology to create an independent signal channel to block external thermal noise and electromagnetic interference; and calculates the signal purity index Sjch by collecting parameters such as the total signal intensity Xzqd, the ambient noise amplitude Znf, and the random noise component Rns; thereby providing better anti-interference protection in terms of frequency response accuracy and signal purity, and ensuring the stability and reliability of the filter at the W-band frequency;

[0051] 3. In solving the bottleneck problem of data processing speed, the fast data processing module of the present invention classifies and cleans the spectrum data in the real-time tracking data set through the parallel processing unit; the feature compression unit extracts the key spectrum features of the frequency fluctuation amplitude Fwd, the phase change rate PhR, the noise energy ratio Nrl and the peak power density Ppd through the feature extraction algorithm, and calculates the data compression index Sjy to evaluate the redundant data, and finally constructs an advanced real-time tracking data set; this module effectively reduces the time and calculation burden of data processing, so that the system can maintain real-time and high efficiency when processing high-frequency response signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0053] Figure 1 A schematic diagram of the framework structure of a GaAs-based W-band SSPP filter performance evaluation system of the present invention;

[0054] Figure 2The figure is a schematic diagram of the steps of a GaAs-based W-band SSPP filter performance evaluation method of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0056] Example 1

[0057] See also Figure 1 , a W-band SSPP filter performance evaluation system based on GaAs, including a signal acquisition module, a fast data acquisition module, a frequency resolution module, an interference suppression module and a performance evaluation module;

[0058] The signal acquisition module is used for real-time acquisition of W-band SSPP filters constructed with GaAs materials. High frequency response Signal ,against Multi-point sampling with small frequency offset and phase change , and generate high-precision spectrum data, and finally establish the spectrum data of multi-point sampling Primary real-time tracking data collection ;

[0059] The fast data processing module is used to process the primary real-time tracking data set; by using parallel data processing technology and compression coding technology, the spectrum data in the primary real-time tracking data set is quickly cleaned and classified; then the key spectrum feature related data in the primary real-time tracking data set is extracted by the feature extraction algorithm, and the data compression index Sjy is calculated and evaluated, and the redundant data is compressed and then the advanced real-time tracking data set is reconstructed.

[0060] The frequency resolution module is used to refine the advanced real-time tracking data set by Specific frequency Offset Resolution analysis of changes, calculation Adjacent frequency offset The high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd are Associate, finally build Frequency response accuracy index value Fzjb;

[0061] The interference suppression module is used to adjust the interference susceptibility of the W-band frequency under different environmental conditions by using multi-layer shielding and signal isolation technology, and at the same time construct a signal purity index value Sjch;

[0062] The performance evaluation module is used to Fitting frequency response accuracy index Fzjb and signal purity index SjchCalculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

[0063] In this embodiment, the signal acquisition module realizes real-time acquisition of high-frequency response signals through a W-band SSPP filter constructed of GaAs material, and uses multi-point sampling to effectively capture the subtle features of frequency offset and phase change and generate high-precision spectrum data, thereby ensuring the accuracy of the primary real-time tracking data set; the fast data processing module accelerates the data processing speed through parallel data processing and compression coding technology, extracts key spectrum features such as frequency fluctuation amplitude Fwd, phase change rate PhR, noise energy ratio Nrl and peak power density Ppd, calculates the data compression index Sjy, and then efficiently cleans and compresses redundant data, thereby optimizing the efficiency and reliability of the advanced real-time tracking data set; the frequency resolution module calculates the high-resolution evaluation of adjacent frequency offsets through the refinement of the advanced real-time tracking data set. The coefficient Fgrc and phase deviation parameter Fxxd are calculated, and the frequency response accuracy index value Fzjb is constructed, so as to improve the frequency response accuracy of the evaluation system in the high frequency band; the interference suppression module effectively reduces the influence of thermal noise and electromagnetic interference on the W-band signal through multi-layer shielding and signal isolation technology, and calculates the signal purity index Sjch by real-time recording of the total signal intensity Xzqd, the environmental noise amplitude Znf and the random noise component Rns, so as to improve the anti-interference ability of the system; the performance evaluation module fits the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter, and makes a comprehensive judgment on the stability and anti-interference ability of the filter after setting the performance evaluation threshold Px, so as to effectively ensure the reliability and stability of the filter in the high-frequency communication environment.

[0064] Example 2

[0065] During the signal acquisition process, the signal acquisition module sets a number of sampling points for the slight frequency offset and phase change of the W-band signal, performs multi-point synchronous sampling according to a preset sampling frequency, and captures the subtle change characteristics in the high-frequency response signal through a multi-point sampling method;

[0066] The generated spectrum data is organized and formatted to eventually construct a primary real-time tracking data set; the primary real-time tracking data set is sorted according to time series to form continuous and traceable real-time spectrum data, and finally the primary tracking data set is transmitted in real time to the fast data processing module through the internal data transmission channel.

[0067] The fast data processing module includes a parallel processing unit and a feature compression unit;

[0068] The parallel processing unit is used to perform preliminary and rapid cleaning and classification of the spectrum data in the primary tracking data set; by adopting parallel data processing technology, the data is classified according to frequency and phase changes and redundant noise data is cleared.

[0069] The feature compression unit is used to calculate the data compression index Sjy; by extracting key spectrum feature related data in the real-time tracking data set, including the frequency fluctuation amplitude Fwd, the phase change rate PhR, the noise energy ratio Nrl and the peak power density Ppd, the data compression index Sjy is calculated using the following formula:

[0070]

[0071] The redundant data is compressed by comparing and evaluating the preset data compression threshold Q with the data compression index Sjy. The specific evaluation contents are as follows:

[0072] When the data compression index Sjy>data compression threshold Q, it means that there is redundant information in the current data, and the current data is compressed at this time;

[0073] When the data compression index Sjy ≤ the data compression threshold Q, it indicates that there is no redundant information in the current data and no further compression is required.

[0074] In this embodiment, the accuracy and efficiency of high-frequency signal acquisition and processing are effectively improved through the parallel processing unit and feature compression unit in the signal acquisition module and the fast data processing module; the signal acquisition module can capture the tiny features of frequency offset and phase change by setting multiple sampling points and multi-point synchronous sampling in the W-band signal, and the primary real-time tracking data set finally generated is organized and formatted to form continuous and traceable real-time spectrum data and transmitted to the fast data processing module, which provides a basis for the subsequent processing of high-frequency signals; in the fast data processing module, the parallel processing unit quickly cleans and classifies the spectrum data, effectively removes redundant noise data, and improves the data processing speed and reliability; the feature compression unit extracts the frequency fluctuation amplitude Fwd, the phase change rate PhR, the noise energy ratio Nrl and the peak power The frequency fluctuation amplitude Fwd reflects the frequency variation range in the spectrum, indicating the stability of the signal; the phase change rate PhR is used to describe the dynamic change of the signal phase, which helps to identify the phase consistency of the frequency response; the noise energy ratio Nrl measures the proportion of noise components in the signal, revealing the signal purity level of the data; the peak power density Ppd represents the concentrated energy of the signal at a specific frequency point, which is used to measure the spectral characteristic strength of the signal; the data compression index Sjy obtained by calculating the above characteristics can effectively evaluate the data redundancy; after comparing with the preset data compression threshold Q, when the data compression index Sjy is higher than the data compression threshold Q, redundant data compression is performed to optimize data storage and transmission efficiency, thereby ensuring the performance evaluation accuracy and processing efficiency of the system in high-frequency communication.

[0075] Example 3

[0076] The frequency resolution module includes a frequency offset analysis unit and a phase accuracy calculation unit;

[0077] The frequency offset analysis unit is used to receive the advanced real-time tracking data set, extract the offset changes of adjacent frequency points and perform resolution analysis, and obtain the offset change related data in real time, including the frequency offset Xpl of adjacent frequency points, the phase difference Xpc of adjacent frequency points and the normalized signal strength ratio Xhq;

[0078] Extract the advanced real-time tracking data set and the offset change related data, and calculate the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd by the following formulas:

[0079]

[0080] Wherein, Zpl represents the center frequency value in the advanced real-time tracking data set; Plb represents the frequency step in the advanced real-time tracking data set; Pmax represents the maximum frequency offset value in the advanced real-time tracking data set; Zsf represents the noise floor amplitude in the advanced real-time tracking data set;

[0081] ln means natural logarithm.

[0082] The phase accuracy calculation unit is used to calculate the frequency response accuracy index value Fzjb, and the specific calculation formula is as follows:

[0083]

[0084] In this embodiment, the frequency offset analysis unit and the phase accuracy calculation unit work together to greatly improve the ability to perform detailed analysis on high-frequency signals. The frequency offset analysis unit receives the advanced real-time tracking data set and extracts the frequency offset Xpl of adjacent frequency points, the phase difference Xpc of adjacent frequency points, and the normalized signal strength ratio Xhq in real time. The frequency offset Xpl reflects the frequency change characteristics of adjacent frequency points, which helps to identify subtle frequency fluctuations in the spectrum. The phase difference Xpc of adjacent frequency points measures the phase consistency of the frequency points and supports the accuracy evaluation of the phase response. The normalized signal strength ratio Xhq is used to standardize the signal strength and help accurately determine the energy distribution of the signal during the frequency offset process. Based on these data, the frequency offset analysis The unit further calculates the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd by combining parameters such as the center frequency value Zpl, the frequency step size Plb, the maximum frequency offset value Pmax and the noise floor amplitude Zsf. The high-resolution evaluation coefficient Fgrc provides high-resolution frequency response accuracy through characteristic fitting of frequency and signal strength; the phase deviation parameter Fxxd reflects the accuracy difference of frequency and phase in different frequency bands; subsequently, the phase accuracy calculation unit calculates the frequency response accuracy index value Fzjb based on the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd, which provides high-precision and stable indicator support for the overall performance of the system, and realizes the effective response and accurate evaluation of the W-band SSPP filter under high-frequency signals.

[0085] Example 4

[0086] The interference suppression module first uses a multi-layer shielding technology to perform preliminary shielding processing on the W-band frequency signal, wherein the multi-layer shielding materials are arranged in layers according to the characteristics of the signal frequency band, so that different layers of materials can effectively absorb or reflect interference signals in a specific frequency band; then the shielded signal is further processed by a signal isolation technology, including inserting an isolator in the W-band signal path to create an independent signal channel to block the entry of external thermal noise and electromagnetic signals, and the configuration of the isolator is adjusted according to the frequency characteristics of the W-band; after the signal isolation processing is completed, the total signal strength Xzqd is recorded in real time, and then the interference noise related data is collected and the environmental noise amplitude Znf and the random noise component Rns are extracted therefrom;

[0087] The signal purity index value Sjch is obtained by fitting the total signal intensity Xzqd, the environmental noise amplitude Znf and the random noise component Rns. The specific calculation formula is as follows:

[0088]

[0089] Here, 1 represents the highest purity of the signal and is used as an ideal reference value without interference.

[0090] In this embodiment, multi-layer shielding and signal isolation technology is used to achieve effective interference suppression of W-band signals, ensuring the signal purity of the system in a complex environment; this module plays a key role in anti-interference protection in the system, and by layering the multi-layer shielding materials of the W-band frequency signal, it effectively absorbs and reflects interference signals in a specific frequency band, reducing the direct impact of external noise; on this basis, the signal isolation technology further creates an independent signal channel by inserting an isolator, blocking the entry of external thermal noise and electromagnetic interference signals, and ensuring the stability of signal transmission;

[0091] After the signal isolation is completed, the system records the total signal strength Xzqd in real time to characterize the baseline strength of the signal for subsequent interference analysis; then the interference noise related data is collected to extract the environmental noise amplitude Znf and random noise component Rns, where the environmental noise amplitude Znf is used to describe the fixed interference amount from the external environment, revealing the constant impact of the environment on the signal; the random noise component Rns represents the transient random interference level, which helps to determine the fluctuation amplitude of the signal affected by random noise; by fitting the total signal strength Xzqd, the environmental noise amplitude Znf and the random noise component Rns, the signal purity index value Sjch is calculated, where the signal purity index value Sjch is used to evaluate the signal quality of the system in an interference environment, with 1 as the highest purity benchmark, reflecting the ideal interference-free state of the signal; overall, the interference suppression module effectively protects the purity and transmission quality of the W-band signal in a complex environment.

[0092] Example 5

[0093] The performance evaluation module includes a performance fitting unit and a performance judgment unit;

[0094] The performance fitting unit is used to receive the frequency response accuracy index Fzjb and the signal purity index Sjch, and calculate the comprehensive performance evaluation index Pzpj of the filter through the following formula:

[0095]

[0096] The performance judgment unit compares and evaluates the preset performance evaluation threshold Px with the comprehensive performance evaluation index Pzpj. The specific evaluation content is as follows:

[0097] When the performance evaluation threshold Px ≤ the comprehensive performance evaluation index Pzpj, it indicates that the frequency response and signal purity of the filter have reached the set performance requirements; it indicates that the filter performs normally in the current environment and operates normally in high-frequency communication;

[0098] When the performance evaluation threshold Px>the comprehensive performance evaluation index Pzpj, it means that the frequency response and signal purity of the filter do not meet the set performance requirements, indicating that the filter performance is abnormal in the current environment; at this time, a performance warning signal is issued, indicating that the filter is subject to external interference or insufficient design parameters, thereby affecting its reliability in high-frequency communications.

[0099] In this embodiment, the system's performance evaluation and judgment accuracy are comprehensively improved through the performance fitting unit and the performance judgment unit, and the comprehensive performance of the filter is evaluated efficiently and accurately; the performance fitting unit receives the frequency response accuracy index Fzjb and the signal purity index Sjch, and calculates the comprehensive performance evaluation index Pzpj of the filter, wherein the frequency response accuracy index Fzjb reflects the filter's precise response capability at different frequencies, which helps to judge the accuracy of the frequency response; the signal purity index Sjch is used to evaluate the signal quality of the filter in an interference environment to determine the filter's anti-interference ability; the performance fitting unit fits these two key indicators into a comprehensive performance evaluation index Pzpj to characterize the overall performance of the filter in a high-frequency environment;

[0100] The performance judgment unit then compares and analyzes the comprehensive performance evaluation index Pzpj according to the preset performance evaluation threshold Px. Based on the evaluation content, the performance judgment unit issues a performance warning signal, indicating that the filter may be affected by external interference or the design parameters are insufficient, thereby ensuring the reliability and stability of the system in high-frequency communication applications.

[0101] Example 6

[0102] See also Figure 2 , a W-band SSPP filter performance evaluation method based on GaAs, comprising the following steps:

[0103] Step 1: Use the W-band SSPP filter constructed of GaAs material to collect high-frequency response signals in real time, perform multi-point sampling for small frequency offsets and phase changes, and generate high-precision spectrum data. Finally, establish a primary real-time tracking data set for the multi-point sampled spectrum data;

[0104] Step 2: Process the primary real-time tracking data set; quickly clean and classify the spectrum data in the primary real-time tracking data set by using parallel data processing technology and compression coding technology; then extract the key spectrum feature related data in the primary real-time tracking data set by using the feature extraction algorithm, calculate and evaluate the data compression index Sjy, compress the redundant data and reconstruct the advanced real-time tracking data set;

[0105] Step 3: refine the advanced real-time tracking data set, calculate the high-resolution evaluation coefficient Fgrc and phase deviation parameter Fxxd of adjacent frequency offsets through resolution analysis of specific frequency offset changes, associate the high-resolution evaluation coefficient Fgrc with the phase deviation parameter Fxxd, and finally construct the frequency response accuracy index value Fzjb;

[0106] Step 4: By using multi-layer shielding and signal isolation technology, the interference susceptibility of the W-band frequency under different environmental conditions is adjusted, and the signal purity index value Sjch is constructed;

[0107] Step 5. Fit the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

[0108] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A GaAs-based W-band SSPP filter performance evaluation system, characterized by: It includes signal acquisition module, fast data acquisition module, frequency resolution module, interference suppression module and performance evaluation module; The signal acquisition module is used to collect high-frequency response signals in real time through a W-band SSPP filter constructed of GaAs material, perform multi-point sampling for small frequency offsets and phase changes, and generate high-precision spectrum data, and finally establish a primary real-time tracking data set for the multi-point sampled spectrum data; The fast data processing module is used to process the primary real-time tracking data set; By using parallel data processing technology and compression coding technology, the spectrum data in the primary real-time tracking data set is quickly cleaned and classified; then the key spectrum feature related data in the primary real-time tracking data set is extracted through the feature extraction algorithm, and the data compression index Sjy is calculated and evaluated, and the redundant data is compressed and then the advanced real-time tracking data set is reconstructed; The frequency resolution module is used to refine the advanced real-time tracking data set, calculate the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd of the adjacent frequency offset by analyzing the change of the specific frequency offset, and associate the high-resolution evaluation coefficient Fgrc with the phase deviation parameter Fxxd, and finally construct the frequency response accuracy index value Fzjb; The interference suppression module is used to adjust the interference susceptibility of the W-band frequency under different environmental conditions by using multi-layer shielding and signal isolation technology, and at the same time construct a signal purity index value Sjch; The performance evaluation module is used to fit the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

2. The GaAs-based W-band SSPP filter performance evaluation system according to claim 1, characterized in that: During the signal acquisition process, the signal acquisition module sets a number of sampling points for the slight frequency offset and phase change of the W-band signal, performs multi-point synchronous sampling according to a preset sampling frequency, and captures the subtle change characteristics in the high-frequency response signal through a multi-point sampling method; Organize and format the generated spectrum data, and finally construct a primary real-time tracking data set; The primary real-time tracking data set is sorted according to the time series to form continuous and traceable real-time spectrum data. Finally, the primary tracking data set is transmitted to the fast data processing module in real time through the internal data transmission channel.

3. The GaAs-based W-band SSPP filter performance evaluation system according to claim 2, characterized in that: The fast data processing module includes a parallel processing unit and a feature compression unit; The parallel processing unit is used to perform preliminary rapid cleaning and classification of the spectrum data in the primary real-time tracking data set; by adopting parallel data processing technology, the data is classified according to frequency and phase changes and redundant noise data is cleared.

4. The GaAs-based W-band SSPP filter performance evaluation system according to claim 3, characterized in that: The feature compression unit is used to calculate the data compression index Sjy; by extracting key spectrum feature related data in the real-time tracking data set, including the frequency fluctuation amplitude Fwd, the phase change rate PhR, the noise energy ratio Nrl and the peak power density Ppd, the data compression index Sjy is calculated using the following formula: The redundant data is compressed by comparing and evaluating the preset data compression threshold Q with the data compression index Sjy. The specific evaluation contents are as follows: When the data compression index Sjy>data compression threshold Q, it means that there is redundant information in the current data, and the current data is compressed at this time; When the data compression index Sjy ≤ the data compression threshold Q, it indicates that there is no redundant information in the current data and no further compression is required.

5. The GaAs-based W-band SSPP filter performance evaluation system according to claim 4, characterized in that: The frequency resolution module includes a frequency offset analysis unit and a phase accuracy calculation unit; The frequency offset analysis unit is used to receive the advanced real-time tracking data set, extract the offset changes of adjacent frequency points and perform resolution analysis, and obtain the offset change related data in real time, including the frequency offset Xpl of adjacent frequency points, the phase difference Xpc of adjacent frequency points and the normalized signal strength ratio Xhq; Extract the advanced real-time tracking data set and the offset change related data, and calculate the high-resolution evaluation coefficient Fgrc and the phase deviation parameter Fxxd by the following formulas: Wherein, Zpl represents the center frequency value in the advanced real-time tracking data set; Plb represents the frequency step in the advanced real-time tracking data set; Pmax represents the maximum frequency offset value in the advanced real-time tracking data set; Zsf represents the noise floor amplitude in the advanced real-time tracking data set; ln means natural logarithm.

6. The GaAs-based W-band SSPP filter performance evaluation system according to claim 5, characterized in that: The phase accuracy calculation unit is used to calculate the frequency response accuracy index value Fzjb, and the specific calculation formula is as follows:

7. The GaAs-based W-band SSPP filter performance evaluation system according to claim 6, characterized in that: The interference suppression module first uses a multi-layer shielding technology to perform preliminary shielding processing on the W-band frequency signal, wherein the multi-layer shielding materials are arranged in layers according to the characteristics of the signal frequency band, so that different layers of materials can effectively absorb or reflect interference signals in a specific frequency band; then the shielded signal is further processed by a signal isolation technology, including inserting an isolator in the W-band signal path to create an independent signal channel to block the entry of external thermal noise and electromagnetic signals, and the configuration of the isolator is adjusted according to the frequency characteristics of the W-band; after the signal isolation processing is completed, the total signal strength Xzqd is recorded in real time, and then the interference noise related data is collected and the environmental noise amplitude Znf and the random noise component Rns are extracted therefrom; The signal purity index value Sjch is obtained by fitting the total signal intensity Xzqd, the environmental noise amplitude Znf and the random noise component Rns. The specific calculation formula is as follows: Here, 1 represents the highest purity of the signal and is used as an ideal reference value without interference.

8. The GaAs-based W-band SSPP filter performance evaluation system according to claim 7, characterized in that: The performance evaluation module includes a performance fitting unit and a performance judgment unit; The performance fitting unit is used to receive the frequency response accuracy index Fzjb and the signal purity index Sjch, and calculate the comprehensive performance evaluation index Pzpj of the filter through the following formula:

9. The GaAs-based W-band SSPP filter performance evaluation system according to claim 8, characterized in that: The performance judgment unit compares and evaluates the preset performance evaluation threshold Px with the comprehensive performance evaluation index Pzpj. The specific evaluation content is as follows: When the performance evaluation threshold Px ≤ the comprehensive performance evaluation index Pzpj, it indicates that the frequency response and signal purity of the filter have reached the set performance requirements; it indicates that the filter performs normally in the current environment and operates normally in high-frequency communication; When the performance evaluation threshold Px>the comprehensive performance evaluation index Pzpj, it means that the frequency response and signal purity of the filter do not meet the set performance requirements, indicating that the filter performance is abnormal in the current environment; at this time, a performance warning signal is issued, indicating that the filter is subject to external interference or insufficient design parameters, thereby affecting its reliability in high-frequency communications.

10. A W-band SSPP filter performance evaluation method based on GaAs, according to a W-band SSPP filter performance evaluation system based on GaAs according to claims 1-9, characterized in that: The following steps are involved: Step 1: Use the W-band SSPP filter constructed of GaAs material to collect high-frequency response signals in real time, perform multi-point sampling for small frequency offsets and phase changes, and generate high-precision spectrum data. Finally, establish a primary real-time tracking data set for the multi-point sampled spectrum data; Step 2: Processing the primary real-time tracking data set; By using parallel data processing technology and compression coding technology, the spectrum data in the primary real-time tracking data set is quickly cleaned and classified; then the key spectrum feature related data in the primary real-time tracking data set is extracted through the feature extraction algorithm, and the data compression index Sjy is calculated and evaluated, and the redundant data is compressed and then the advanced real-time tracking data set is reconstructed; Step 3: refine the advanced real-time tracking data set, calculate the high-resolution evaluation coefficient Fgrc and phase deviation parameter Fxxd of adjacent frequency offsets through resolution analysis of specific frequency offset changes, associate the high-resolution evaluation coefficient Fgrc with the phase deviation parameter Fxxd, and finally construct the frequency response accuracy index value Fzjb; Step 4: By using multi-layer shielding and signal isolation technology, the interference susceptibility of the W-band frequency under different environmental conditions is adjusted, and the signal purity index value Sjch is constructed; Step 5. Fit the frequency response accuracy index Fzjb and the signal purity index Sjch to calculate the comprehensive performance evaluation index Pzpj of the filter; and set the performance evaluation threshold Px, compare and analyze the comprehensive performance evaluation index Pzpj with the performance evaluation threshold Px, and judge the stability and anti-interference ability of the GaAs-based W-band SSPP filter in the actual environment. If the comprehensive performance evaluation index Pzpj is lower than the performance evaluation threshold Px, a performance warning signal is issued.

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