Nonlinear Electronic Node Covert Detection System Based on S-band Harmonic Fingerprint Recognition

Abstract

This invention relates to the field of nonlinear node detection technology, and particularly to a nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting. The system includes: acquiring fundamental and second harmonic echo signals from multiple detection nodes within the detection area in the S-band to ensure a sufficient data source for the entire detection process; constructing harmonic excitation response features and generating a fingerprint database to improve the accuracy and targeting of subsequent node identification; dynamically capturing the node's current characteristic information by transmitting S-band frequency sweep detection signals and collecting echo harmonic components to extract real-time fingerprints, enhancing the timeliness of node detection; identifying matching concealed electronic nodes by performing multi-dimensional time-series correlation matching between real-time harmonic fingerprints and fingerprints in the fingerprint database, effectively filtering out irrelevant interference; and generating a topology map and marking abnormal nodes, enabling users to quickly grasp the overall situation and rapidly locate anomalies.

Description

Technical Field

[0001] This invention relates to the field of nonlinear node detection technology, and in particular to a nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting. Background Technology

[0002] Nonlinear node detectors are widely used in security inspections and counter-eavesdropping to detect concealed electronic devices. Existing detection methods typically employ single-frequency or narrowband sweep modes, determining the presence of nonlinear nodes in the target area by detecting the presence and intensity of second harmonics. This method is effective in open, low-interference environments.

[0003] However, existing methods face fundamental limitations when multiple electronic nodes or complex electromagnetic interference exist in the detection area: harmonic response signals from different electronic nodes are superimposed at the receiving end, and traditional amplitude detection cannot distinguish the harmonic contributions from different nodes, let alone identify the individual characteristics of specific electronic nodes; in addition, passive nonlinear interference sources in the environment, such as metal corrosion contact points and oxide connections, can also generate false harmonic responses, leading to frequent false alarms; existing technologies lack refined modeling of the harmonic response characteristics of electronic nodes and the ability to identify individual fingerprints, making it difficult to accurately identify hidden target nodes in complex electromagnetic environments, and also unable to dynamically track the state evolution of nodes, such as nonlinear characteristic degradation and instability precursors, which seriously restricts the practicality and reliability of nonlinear node detection systems in scenarios such as confidentiality inspection and electronic forensics.

[0004] To address this, the present invention proposes a nonlinear electronic node concealment detection system based on S-band harmonic fingerprint recognition. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing a nonlinear electronic node concealment detection system based on S-band harmonic fingerprint recognition.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting includes: Signal acquisition and processing module: acquires the fundamental echo signal and second harmonic echo signal in the S-band of multiple detection nodes within the detection area; Fingerprint database construction module: Based on the amplitude ratio of the second harmonic echo signal to the fundamental echo signal, construct the harmonic excitation response characteristics of each detection node, and generate a nonlinear node fingerprint database based on the harmonic excitation response characteristics; Real-time fingerprint extraction module: When the system receives an externally triggered covert scanning command, it transmits an S-band sweep frequency detection signal to the detection area and collects the harmonic components in the echo signal in real time to extract the real-time harmonic fingerprint; wherein, the echo signal includes the fundamental echo signal and the second harmonic echo signal; Node identification and matching module: performs time-series correlation matching between real-time harmonic fingerprints and fingerprints in the nonlinear node fingerprint database, and identifies hidden electronic nodes that match fingerprints in the nonlinear node fingerprint database; Topology graph generation and marking module: Based on the identified hidden electronic nodes, analyze their spatial distribution and harmonic response stability, generate a hidden node topology graph, and mark nodes with nonlinear response anomalies.

[0007] Furthermore, the step of constructing harmonic excitation response characteristics for each detection node based on the amplitude ratio of the second harmonic echo signal to the fundamental echo signal, and generating a nonlinear node fingerprint database based on the harmonic excitation response characteristics, includes: For each detection node, the amplitude of its fundamental echo signal and the amplitude of its second harmonic echo signal are obtained at multiple transmission power levels; Calculate the ratio of the second harmonic echo signal amplitude to the fundamental echo signal amplitude at each transmit power level, and use this ratio as the harmonic conversion efficiency at that power level. The harmonic conversion efficiency at each power level is arranged in time sequence to generate harmonic excitation response curves. The slope of the nonlinear segment and the inflection point of the saturation segment of the harmonic excitation response curve are extracted as the harmonic excitation response characteristics of the detection node. The spatial coordinates of each detection node are associated with and stored with the corresponding harmonic excitation response characteristics to construct a nonlinear node fingerprint database.

[0008] Furthermore, the step of transmitting an S-band swept frequency detection signal to the detection area and acquiring harmonic components in the echo signal in real time to extract a real-time harmonic fingerprint includes: Based on the scanning bandwidth and step frequency carried in the covert scanning command, an S-band sweep frequency detection signal sequence is generated; From the S-band sweep frequency detection signal sequence, each sweep frequency detection signal is extracted sequentially according to the preset frequency step order, and the echo signal corresponding to each transmission frequency is received synchronously. Each transmission frequency corresponds to a frequency point. Separate the fundamental component and the second harmonic component from the echo signal, and record the fundamental amplitude and the second harmonic amplitude corresponding to each frequency point; By analyzing the ratio of the second harmonic amplitude to the fundamental amplitude at each frequency point, a frequency-harmonic response sequence is formed; Peak detection is performed on the frequency-harmonic response sequence, and the frequency point corresponding to the peak value and the amplitude ratio are used as real-time harmonic fingerprints.

[0009] Furthermore, the step of performing time-series correlation matching between the real-time harmonic fingerprint and the fingerprints in the nonlinear node fingerprint database to identify hidden electronic nodes that match the fingerprints in the nonlinear node fingerprint database includes: Based on the frequency point corresponding to the peak value, the peak frequency point in the real-time harmonic fingerprint is determined, and the peak frequency point in the real-time harmonic fingerprint is compared with the peak frequency points of each fingerprint in the nonlinear node fingerprint database to obtain the frequency deviation value. When the frequency deviation value is less than the preset frequency deviation threshold, the correlation coefficient is obtained by analyzing the correlation between the real-time harmonic fingerprint and the harmonic excitation response curve of the corresponding fingerprint. The detection nodes corresponding to fingerprints with correlation coefficients greater than a preset correlation coefficient threshold are marked as candidate matching nodes; Obtain the real-time harmonic fingerprints of candidate matching nodes over multiple consecutive scan cycles and analyze their harmonic response stability. When the harmonic response stability is greater than the preset stability threshold, the candidate matching node is identified as a hidden electronic node that matches the fingerprint in the nonlinear node fingerprint database.

[0010] Furthermore, the process of analyzing the spatial distribution and harmonic response stability of the identified hidden electronic nodes, generating a hidden node topology map, and marking nonlinear response anomalous nodes includes: The identified hidden electronic nodes are mapped onto the spatial coordinate system of the detection area to form a spatial distribution map of the nodes; Using the node spatial distribution map as the initial topology, the stability difference gradient between each hidden electron node is calculated based on the harmonic response stability of each hidden electron node. Connect adjacent hidden electron nodes whose stability difference gradient is greater than a preset gradient threshold to construct a hidden node topology graph. In the hidden node topology diagram, detect whether the harmonic response stability of each hidden electronic node shows a monotonically decreasing trend within a continuous scanning period; For a hidden electronic node exhibiting a monotonically decreasing trend, if the difference in harmonic response stability between the hidden electronic node and its directly connected hidden electronic nodes exceeds a preset difference threshold, the hidden electronic node is marked as a nonlinear response anomalous node and highlighted in the hidden node topology diagram.

[0011] Furthermore, the system also includes: Obtain the real-time harmonic fingerprint of nodes marked as nonlinear response anomalies, and extract the slope change rate of the nonlinear segment in the harmonic excitation response curve of the nonlinear response anomaly node. When the rate of change of the slope of the nonlinear segment exceeds the preset rate of change threshold, the nonlinear response abnormal node is determined to be in the state transition stage. For nonlinear response anomaly nodes in the state transition stage, the transmission power of the S-band sweep frequency detection signal within a preset radius centered on the nonlinear response anomaly node is adjusted to a preset high power level, and the sampling frequency is adjusted to a preset high frequency sampling level. Based on the adjusted transmit power and sampling frequency, the real-time harmonic fingerprint of the nonlinear response abnormal node is re-acquired, and the feature parameters of the corresponding fingerprint in the nonlinear node fingerprint database are updated.

[0012] Furthermore, the system also includes: After generating the hidden node topology graph, the comprehensive feature drift of the harmonic excitation response characteristics of the hidden electronic nodes in the hidden node topology graph over time is detected. By analyzing the spatial propagation path of the comprehensive feature drift, key relay hidden electronic nodes on the propagation path are identified. When the cumulative drift of the comprehensive feature of a key relay hidden electronic node exceeds the preset drift cumulative threshold, beam focusing scanning is performed on the detection area associated with the key relay hidden electronic node. Based on the high-resolution harmonic fingerprint obtained by beam focusing scanning, the spatial coordinates of key relay hidden electronic nodes and their adjacent hidden electronic nodes in the hidden node topology map are corrected.

[0013] Furthermore, the system also includes: Obtain the harmonic response phase information of each hidden electronic node in the hidden node topology graph; Based on the acquired harmonic response phase information, the harmonic response phase difference between each pair of adjacent hidden electron nodes is calculated, and the phase difference distribution field is constructed according to the calculation results. In the phase difference distribution field, identify regions where the phase difference changes abruptly, and mark these regions as nonlinear node boundary regions; Perform multi-angle scanning on the nonlinear node boundary region to obtain the harmonic response directionality characteristics of the region; Based on the directional characteristics of harmonic response, the connection relationships of hidden electronic nodes in the boundary region of the hidden node topology diagram are corrected.

[0014] Furthermore, the system also includes: After marking the nonlinear response anomalous node, obtain the family of harmonic excitation response curves of the nonlinear response anomalous node in multiple consecutive scan cycles; Determine the area difference between adjacent harmonic excitation response curves in the family of curves, and form a time series of the difference values; If the time series of difference values ​​shows a monotonically increasing trend, then the state of the nonlinear response abnormal node is marked as an instability precursor state. For nonlinear response anomaly nodes marked as instability precursor states, early warning information is generated, and the spatial coordinates of the nonlinear response anomaly node are associated with the instability precursor state and output.

[0015] Compared with existing technologies, the advantages of the nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting provided by this invention are as follows: 1) By acquiring the fundamental and second harmonic echo signals of multiple detection nodes within the detection area, the system comprehensively grasps the original signal information, ensuring data integrity and accuracy. Harmonic excitation response characteristics are constructed by calculating the amplitude ratio, generating a nonlinear node fingerprint database. This provides a standard reference for identifying hidden electronic nodes. Different nodes have unique fingerprints, facilitating subsequent accurate matching and identification, thus improving the accuracy and targeting of the detection. Upon receiving a hidden scanning command, a frequency sweep signal is transmitted and echo harmonic components are collected. Real-time harmonic fingerprints are extracted, allowing for timely capture of the characteristic information of hidden nodes under specific conditions. This provides real-time and effective data for subsequent node identification, enhancing the system's dynamic detection capability of hidden nodes. 2) By performing time-series correlation matching between real-time fingerprints and fingerprints in the fingerprint database, and through multi-step comparison analysis, the system accurately identifies the matched hidden electronic nodes, effectively eliminates interference, improves recognition accuracy, and ensures that the system can reliably find the target hidden electronic nodes. Based on the recognition results, the system analyzes the spatial distribution and harmonic response stability, generates a topology map, and marks abnormal nodes, intuitively presenting the positional relationships and response characteristics of hidden nodes. This helps users quickly understand the overall situation, focus on abnormal nodes, and provide a strong basis for further analysis and processing. Attached Figure Description

[0016] Figure 1 This is a block diagram of the nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting proposed in this invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1 The present invention provides a nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting, comprising: Signal acquisition and processing module: acquires the fundamental echo signal and second harmonic echo signal in the S-band of multiple detection nodes within the detection area; Fingerprint database construction module: Based on the amplitude ratio of the second harmonic echo signal to the fundamental echo signal, construct the harmonic excitation response characteristics of each detection node, and generate a nonlinear node fingerprint database based on the harmonic excitation response characteristics; Real-time fingerprint extraction module: When the system receives an externally triggered covert scanning command, it transmits an S-band sweep frequency detection signal to the detection area and collects the harmonic components in the echo signal in real time to extract the real-time harmonic fingerprint; wherein, the echo signal includes the fundamental echo signal and the second harmonic echo signal; Node identification and matching module: performs time-series correlation matching between real-time harmonic fingerprints and fingerprints in the nonlinear node fingerprint database, and identifies hidden electronic nodes that match fingerprints in the nonlinear node fingerprint database; Topology graph generation and marking module: Based on the identified hidden electronic nodes, analyze their spatial distribution and harmonic response stability, generate a hidden node topology graph, and mark nodes with nonlinear response anomalies.

[0019] In this embodiment of the invention, the process of constructing the harmonic excitation response characteristics of each detection node based on the amplitude ratio of the second harmonic echo signal to the fundamental echo signal, and generating a nonlinear node fingerprint database based on the harmonic excitation response characteristics, includes: S11. For each detection node, obtain the amplitude of its fundamental echo signal and the amplitude of its second harmonic echo signal at multiple transmission power levels; Understandably, during the initialization phase, the transmitter is controlled to output multiple preset transmission power levels sequentially. The transmission power level setting method is as follows: a transmission power level list is pre-stored, which contains multiple power values ​​distributed at equal or logarithmic intervals from the minimum transmission power to the maximum transmission power, with each power value corresponding to a transmission power level. Based on the maximum detection distance and background noise level of the detection area, the first power level in the transmission power level list is automatically selected as the initial transmission power level, and subsequent levels are traversed sequentially. For each transmission power level, the transmitter radiates an S-band continuous wave signal into the detection area via a probe antenna; the receiving antenna synchronously captures the echo signal reflected back from the nonlinear electronic nodes in the detection area; the receiver front end amplifies the echo signal with low noise, and then sends the amplified echo signal to a dual-channel downconverter to extract the fundamental frequency component and the second harmonic frequency component; the fundamental intermediate frequency signal output from the fundamental channel is sampled by an analog-to-digital converter, and the amplitude of the fundamental signal is calculated by a digital signal processor; the second harmonic intermediate frequency signal output from the second harmonic channel is also processed by analog-to-digital conversion and amplitude extraction to obtain the amplitude of the second harmonic signal; at the same time, the fundamental amplitude and second harmonic amplitude corresponding to each transmission power level are recorded to form the original data record of that detection node.

[0020] S12. Calculate the ratio of the second harmonic echo signal amplitude to the fundamental echo signal amplitude at each transmit power level, and use it as the harmonic conversion efficiency at that power level.

[0021] S13. Arrange the harmonic conversion efficiencies at each power level in a time sequence to generate harmonic excitation response curves.

[0022] S14. Extract the slope of the nonlinear segment and the inflection point of the saturation segment of the harmonic excitation response curve as the harmonic excitation response characteristics of the detection node, specifically: Multiple transmit power levels and their corresponding harmonic conversion efficiency data pairs are plotted on a two-dimensional coordinate system, with the transmit power level on the horizontal axis and the harmonic conversion efficiency on the vertical axis. A piecewise linear fit is performed on all data pairs using the least squares method: the data pairs in the lower transmit power level range are fitted to obtain a fitted straight line for that low-power range, and the slope of this fitted line is the slope of the linear segment. Subsequently, the fitted data pairs are added sequentially. When the newly added data pair causes the fitting residual to exceed a preset residual threshold, the transmit power level corresponding to the newly added data pair is marked as the starting point of the nonlinear segment. The residual threshold is used to determine whether the change in harmonic conversion efficiency with increasing transmit power level has entered a plateau phase; its value is pre-calibrated based on the range of harmonic conversion efficiency fluctuations at the electron node in saturation. The data pairs in subsequent transmit power level ranges are then fitted to obtain a fitted straight line for the nonlinear segment, and the slope of this fitted line is the slope of the nonlinear segment. For the identification of saturation sections, the increment of harmonic conversion efficiency between adjacent transmission power levels is calculated. When the increment of harmonic conversion efficiency between multiple consecutive transmission power levels is less than a preset increment threshold, the transmission power level section is marked as a saturation section, and the data pair corresponding to the starting transmission power level of the saturation section is marked as the inflection point of the saturation section.

[0023] S15. The detailed implementation of associating and storing the spatial coordinates of each detection node with the corresponding harmonic excitation response characteristics to construct a nonlinear node fingerprint database includes: A unique node identifier is generated for each detection node. After extracting the slope of the nonlinear segment and the inflection point of the saturation segment of the harmonic excitation response curve, the spatial coordinates of the detection node, the slope of the nonlinear segment, the transmit power level corresponding to the inflection point of the saturation segment, and the harmonic conversion efficiency corresponding to the inflection point of the saturation segment are combined to form a complete fingerprint record. The generated fingerprint record is stored in the fingerprint database, which adopts a key-value pair storage structure, with the node identifier as the key and the composite data structure composed of spatial coordinates and harmonic excitation response features as the value. After the signal acquisition and feature extraction of all detection nodes are completed, the fingerprint database is built with a nonlinear node fingerprint database containing the feature information of all detection nodes, which can be called in the subsequent matching and identification stage.

[0024] In the specific implementation process, when the system receives an externally triggered covert scanning command, it transmits an S-band frequency sweep detection signal to the detection area and collects the harmonic components in the echo signal in real time. The detailed implementation steps for extracting real-time harmonic fingerprints include: S21. The steps for generating the S-band swept frequency detection signal sequence based on the scanning bandwidth and step frequency carried in the covert scanning command are as follows: Upon receiving the covert scan command, the scan bandwidth parameter and step frequency parameter are parsed from the covert scan command data packet. The scan bandwidth parameter defines the start and end frequencies of the S-band sweep detection signal, while the step frequency parameter defines the frequency interval between two adjacent frequency points. Based on the start frequency, step frequency, and end frequency, the center frequency value of each frequency point is calculated sequentially using an arithmetic sequence generation method. The center frequency value of each frequency point is encapsulated with a preset transmission duration to generate an S-band sweep detection signal descriptor. The S-band sweep detection signal descriptors corresponding to all frequency points are arranged in ascending order of frequency to form a complete S-band sweep detection signal sequence, which is stored in the transmission control buffer, awaiting the transmission scheduling command.

[0025] S22. Extract each frequency sweep detection signal sequentially from the S-band frequency sweep detection signal sequence according to the preset frequency step order, and synchronously receive the echo signal corresponding to each transmission frequency. Each transmission frequency corresponds to a frequency point. Specifically, the first S-band sweep probe signal descriptor is read from the S-band sweep probe signal sequence. Based on its center frequency value and transmission duration, the frequency synthesizer is controlled to generate a radio frequency carrier of the corresponding frequency. The radio frequency carrier is amplified by a power amplifier and radiated through the probe antenna. At the start of transmission, the synchronous sampling timer of the receiving channel is started. After the transmission duration ends, transmission is immediately stopped and the system switches to receiving mode. The echo signal corresponding to the frequency point is collected through the receiving antenna. The length of the receiving window is preset according to the maximum detection distance of the detection area. After reception, the collected time-domain sampling sequence is used as the echo data of the frequency point, and the echo data is associated with and stored with the current transmission frequency point. Then, the S-band sweep probe signal descriptor of the next frequency point is extracted from the S-band sweep probe signal sequence, and the transmission and reception process is repeated until the S-band sweep probe signals corresponding to all frequency points have been transmitted and the echoes have been collected.

[0026] S23. The process of separating the fundamental component and the second harmonic component from the echo signal and recording the fundamental amplitude and the second harmonic amplitude corresponding to each frequency point includes: The echo data collected at each frequency point is sent to the digital downconversion submodule. The digital downconversion submodule generates a local oscillator signal at the fundamental frequency corresponding to the current transmission frequency point. This signal is mixed with the echo data, and after low-pass filtering, the in-phase and quadrature components of the fundamental frequency are extracted. The fundamental amplitude corresponding to that frequency point is obtained by calculating the square root of the sum of the squares of the in-phase and quadrature components. Simultaneously, the digital downconversion submodule generates a local oscillator signal at twice the fundamental frequency, mixes it with the echo data, and after low-pass filtering, extracts the in-phase and quadrature components of the second harmonic component, thereby calculating the second harmonic amplitude. The fundamental amplitude and second harmonic amplitude at each frequency point are recorded in a temporary data table, forming the amplitude record for that frequency point.

[0027] S24. By analyzing the ratio of the second harmonic amplitude to the fundamental amplitude at each frequency point, a frequency-harmonic response sequence is formed: traverse the amplitude record of each frequency point, calculate the ratio of the second harmonic amplitude to the fundamental amplitude, and obtain the harmonic conversion efficiency value at that frequency point; combine the frequency point values ​​and the corresponding harmonic conversion efficiency values ​​into two-dimensional data pairs, and arrange all the frequency point data pairs in frequency order to form a frequency-harmonic response sequence.

[0028] S25. The steps for peak detection of the frequency-harmonic response sequence and using the frequency point corresponding to the peak and the amplitude ratio as the real-time harmonic fingerprint are as follows: The frequency-harmonic response sequence is smoothed using a sliding window to eliminate measurement noise. Then, each element in the sequence, containing a frequency value and its corresponding harmonic conversion efficiency (HCE) value, is traversed, and is called a frequency-harmonic response data point. The HCE value of the current HCE data point is compared with the HCE values ​​of the two adjacent data points in the sequence. If the HCE value of the current data point is greater than the HCE values ​​of all its adjacent data points, it is marked as a candidate peak point. The signal-to-noise ratio (SNR) of each candidate peak point is calculated, and candidate peak points with an SNR greater than a preset SNR threshold are determined as valid peak points. The SNR threshold is used to determine whether the HCE value of a candidate peak point is significantly higher than the background noise fluctuation level; its value is determined by multiplying the standard deviation of the HCE measurement noise in an environment without electronic nodes by a preset factor. The frequency value and HCE value corresponding to the valid peak points are extracted as real-time harmonic fingerprints.

[0029] The steps for performing time-series correlation matching between real-time harmonic fingerprints and fingerprints in a nonlinear node fingerprint database to identify hidden electronic nodes that match fingerprints in the nonlinear node fingerprint database include: S31. Based on the frequency point corresponding to the peak value, determine the peak frequency point in the real-time harmonic fingerprint, and compare the peak frequency point in the real-time harmonic fingerprint with the peak frequency points of each fingerprint in the nonlinear node fingerprint database to obtain the frequency deviation value. Specifically, the peak frequency points are extracted from the real-time harmonic fingerprint and recorded as the real-time peak frequency; the first fingerprint record is read from the nonlinear node fingerprint database, and the transmit power level value corresponding to the inflection point of the saturation section stored in the fingerprint is extracted, and the power level value is converted into a frequency value and marked as the database peak frequency; the absolute value of the difference between the real-time peak frequency and the database peak frequency is calculated to obtain the frequency deviation value; all fingerprint records in the nonlinear node fingerprint database are traversed, and the frequency deviation value corresponding to each fingerprint is calculated in turn, and all calculation results are temporarily stored in the frequency deviation array.

[0030] S32. When the frequency deviation value is less than the preset frequency deviation threshold, the correlation coefficient is obtained by analyzing the correlation between the real-time harmonic fingerprint and the harmonic excitation response curve of the corresponding fingerprint. The frequency deviation threshold is used to measure the allowable deviation between the peak frequency of the real-time harmonic fingerprint and the peak frequency of the fingerprint in the fingerprint database. The value of the frequency deviation threshold is pre-calibrated based on the frequency measurement accuracy of the detection system and the inherent frequency offset characteristics of the harmonic response of the electronic node. Understandably, the process involves extracting the frequency-harmonic response sequence of the real-time harmonic fingerprint and the harmonic excitation response curve data of the current candidate fingerprint in the nonlinear node fingerprint database. Since the frequency sampling points of both are consistent, interpolation is performed on the frequency-harmonic response sequence of the real-time harmonic fingerprint to align its frequency sampling points with those of the harmonic excitation response curve in the nonlinear node fingerprint database. The covariance of the two sequences after interpolation and alignment is calculated by summing the products of the harmonic conversion efficiency values ​​of the two sequences at each frequency point, dividing by the total number of frequency points, and then subtracting the product of the mean values ​​of the harmonic conversion efficiency values ​​of the two sequences. The standard deviations of the two sequences are calculated separately, and the covariance is divided by the product of the two standard deviations to obtain the correlation coefficient. The correlation coefficient ranges from -1 to +1; the closer the value is to +1, the more consistent the trend of the frequency-harmonic response sequence of the real-time harmonic fingerprint with the harmonic excitation response curve in the fingerprint database.

[0031] S33. Mark the detection nodes corresponding to fingerprints with correlation coefficients greater than the preset correlation coefficient threshold as candidate matching nodes; wherein, the correlation coefficient threshold is used to determine whether the similarity between the harmonic excitation response curve of the real-time harmonic fingerprint and the harmonic excitation response curve of the fingerprint in the nonlinear node fingerprint database meets the matching requirements; at the same time, the value of the correlation coefficient threshold can be determined by statistically analyzing the correlation coefficient distribution of multiple measurement results of known nodes.

[0032] S34. The steps of obtaining the real-time harmonic fingerprints of candidate matching nodes over multiple consecutive scan cycles and analyzing their harmonic response stability include: Within multiple consecutive scan cycles, the real-time harmonic fingerprint of the same candidate matching node is repeatedly extracted. After each scan cycle, the peak frequency point and the corresponding harmonic conversion efficiency value extracted within that cycle are recorded. When the accumulated number of scan cycles reaches the preset stability analysis window length, the peak frequency point values ​​of all scan cycles within the stability analysis window are extracted, and the standard deviation of the peak frequency point values ​​is calculated to obtain the frequency stability. The stability analysis window length is used to determine the number of consecutive scan cycles participating in the harmonic response stability calculation. Simultaneously, the peak harmonic conversion efficiency values ​​of all scan cycles within the stability analysis window are extracted, and the standard deviation of the peak harmonic conversion efficiency values ​​is calculated to obtain the efficiency stability. The frequency stability and efficiency stability are weighted and summed to obtain the harmonic response stability, where the weighting coefficient of frequency stability is greater than that of efficiency stability.

[0033] S35. When the harmonic response stability is greater than the preset stability threshold, the candidate matching node is identified as a hidden electronic node that matches the fingerprint in the nonlinear node fingerprint database. The stability threshold is used to evaluate the consistency level of the harmonic response of the candidate matching node in multiple scan cycles. The value of the stability threshold can be determined based on the comprehensive requirements for the false match rate and the missed match rate. In this invention, a hidden electronic node refers to an electronic structural unit distributed within the detection area. The specific location and state of this electronic structural unit are unknown to the system before detection, and it possesses nonlinear electromagnetic response characteristics. The electronic structural unit includes, but is not limited to, discrete electronic components composed of semiconductor PN junctions, metal-semiconductor Schottky junctions, metal-oxide-metal tunnel structures, and oxide layer interface junctions formed by the contact of different metal materials. When the hidden electronic node receives S-band fundamental wave irradiation, it can generate a second harmonic echo signal, and its harmonic response intensity exhibits a nonlinear relationship with the incident power.

[0034] In this embodiment, based on the identified hidden electronic nodes, their spatial distribution and harmonic response stability are analyzed to generate a hidden node topology map, and the implementation methods for marking nonlinear response anomalous nodes include: S41. Map the identified hidden electron nodes to the spatial coordinate system of the detection area to form a node spatial distribution map; S42. Using the node spatial distribution map as the initial topology, calculate the stability difference gradient between each hidden electron node based on the harmonic response stability of each hidden electron node. Specifically, in the node spatial distribution map, for each hidden electron node, other hidden electron nodes that are directly adjacent to it in space are retrieved; for each pair of adjacent hidden electron nodes, the harmonic response stability values ​​of the two hidden electron nodes are read, the absolute value of the difference between the two harmonic response stability values ​​is calculated, and the stability difference value of the pair of adjacent hidden electron nodes is obtained; the stability difference value is divided by the Euclidean distance between the two hidden electron nodes to obtain the stability difference gradient of the pair of adjacent hidden electron nodes.

[0035] S43. Connect adjacent hidden electron nodes whose stability difference gradient is greater than a preset gradient threshold to construct a hidden node topology map. The gradient threshold is used to determine whether the stability change between adjacent hidden electron nodes is significant enough to form a topological connection relationship. Its value is determined based on the statistical distribution of stability difference between normal nodes in the detection area. Usually, the gradient value corresponding to 95% of the statistical distribution of stability difference is taken. The process of constructing a hidden node topology graph includes: initializing an empty graph data structure, where the node set and edge set are empty; traversing all adjacent pairs of hidden electronic nodes and calculating the stability difference gradient for each pair of hidden electronic nodes; if the calculated stability difference gradient is greater than a preset gradient threshold, then the two hidden electronic nodes are added to the node set, and an undirected edge is established between the two hidden electronic nodes and added to the edge set; after all adjacent pairs of hidden electronic nodes that meet the conditions have been processed, a graph structure with hidden electronic nodes as nodes and the established undirected edges as edges is obtained, which is the hidden node topology graph.

[0036] S44. In the hidden node topology diagram, the process of detecting whether the harmonic response stability of each hidden electronic node shows a monotonically decreasing trend within a continuous scanning period is as follows: A stability history queue is maintained for each hidden electron node. This stability history queue stores the harmonic response stability values ​​calculated within the most recent consecutive scan periods. When a new harmonic response stability value is stored in the stability history queue, the harmonic response stability sequence in the stability history queue is traversed. Starting from the second harmonic response stability value, the difference between the current harmonic response stability value and the previous harmonic response stability value is calculated sequentially. If all differences are less than or equal to zero, and the last harmonic response stability value in the harmonic response stability value sequence is less than the first harmonic response stability value, then it is determined that the harmonic response stability of the hidden electron node shows a monotonically decreasing trend within the consecutive scan periods.

[0037] S45. For a hidden electronic node that shows a monotonically decreasing trend, if the difference in harmonic response stability between the hidden electronic node and its directly connected hidden electronic nodes exceeds a preset difference threshold, the hidden electronic node is marked as a nonlinear response abnormal node and highlighted in the hidden node topology diagram. Among them, the harmonic response stability difference is the difference in harmonic response stability values ​​between the current hidden electron node and its directly connected adjacent hidden electron nodes. This difference value is used to measure the consistency of the stable state between nodes. The difference threshold is used to determine whether the stability difference between two directly connected hidden electron nodes has reached an abnormal level. The value of the difference threshold can be determined by analyzing the statistical distribution of the stability difference between adjacent nodes under normal conditions. Usually, the difference value corresponding to 90% of the statistical distribution is taken.

[0038] The specific process for detecting state transitions and adjusting parameters at nonlinear response anomaly nodes is as follows: S51. Obtain the real-time harmonic fingerprint of the node marked as an anomalous node in nonlinear response, and extract the rate of change of the slope of the nonlinear segment in the harmonic excitation response curve of the anomalous node, including: From the historical fingerprint records of nodes marked as having abnormal nonlinear responses, the harmonic excitation response curves of these nodes are retrieved over multiple consecutive scan cycles. For each scan cycle's harmonic excitation response curve, the slope value of the nonlinear segment is extracted to form a time series sequence of the nonlinear segment slope. This time series sequence is then differentially processed to calculate the difference in the nonlinear segment slope between two adjacent scan cycles. This difference is then divided by the time interval between adjacent scan cycles to obtain the rate of change of the nonlinear segment slope in each time interval. The rate of change of the nonlinear segment slope in the most recent time interval is taken as the current judgment criterion.

[0039] S52. When the rate of change of the slope of the nonlinear segment exceeds the preset rate of change threshold, the nonlinear response abnormal node is determined to be in the state transition stage. The rate of change threshold is used to determine whether the rate of change of the slope of the nonlinear segment of the harmonic excitation response curve of the nonlinear response abnormal node has reached the critical level of state transition. The value of the rate of change threshold can be determined based on the upper limit of the rate of change of the slope of a normal electronic node in a stable working state.

[0040] S53. For nonlinear response abnormal nodes in the state transition stage, adjust the transmission power of the S-band sweep frequency detection signal in the detection area within a preset radius centered on the nonlinear response abnormal node to a preset high power level, and adjust the sampling frequency to a preset high frequency sampling level. Understandably, after determining that the current nonlinear response anomaly node is in the state transition stage, the spatial coordinates of the nonlinear response anomaly node are obtained; a circular detection area is delineated with the spatial coordinates as the center point and a preset radius value; the power control parameters of the transmitter are adjusted to a preset high power level, the corresponding transmission power value of which is three times the normal scanning power value, to ensure that it can penetrate any possible obstructions and obtain a sufficiently strong echo signal; at the same time, the sampling clock frequency of the receiving channel is adjusted to a preset high-frequency sampling level, the corresponding sampling frequency of which is eight times the fundamental frequency, to meet the requirements of high-resolution harmonic analysis.

[0041] S54. Based on the adjusted transmit power and sampling frequency, re-acquire the real-time harmonic fingerprint of the nonlinear response abnormal node and update the feature parameters of the corresponding fingerprint in the nonlinear node fingerprint database. Specifically, based on the re-acquired harmonic excitation response curves of the nonlinear response anomaly nodes, new nonlinear segment slopes and saturation segment inflection points are obtained; according to the spatial coordinates of the nonlinear response anomaly node, the corresponding fingerprint record is retrieved in the nonlinear node fingerprint database; the original nonlinear segment slope in the fingerprint record is replaced with the newly extracted nonlinear segment slope, and the original saturation segment inflection point is replaced with the newly extracted saturation segment inflection point, while the timestamp of this update is recorded, thus completing the update of the fingerprint parameters.

[0042] The detailed implementation method for identifying key relay nodes and correcting their positions based on feature drift is as follows: S61. After generating the hidden node topology graph, detect the comprehensive feature drift of the harmonic excitation response characteristics of the hidden electronic nodes in the hidden node topology graph over time. Specifically: For each hidden electronic node, the harmonic excitation response features extracted during its initial construction are stored as reference features, including the slope of the reference nonlinear segment and the inflection point of the reference saturation segment. After each subsequent scan cycle, the current harmonic excitation response features of the hidden electronic node are extracted again. The difference between the current slope of the nonlinear segment and the slope of the reference nonlinear segment is calculated to obtain the slope drift. At the same time, the difference between the transmit power level corresponding to the inflection point of the current saturation segment and the transmit power level corresponding to the inflection point of the reference saturation segment is calculated to obtain the inflection point drift. The slope drift and the inflection point drift are normalized and then weighted and summed to obtain the comprehensive feature drift of the hidden electronic node.

[0043] S62. Specific methods for identifying key relay hidden electronic nodes along the propagation path by analyzing the spatial propagation path of the comprehensive characteristic drift include: Obtain the comprehensive feature drift of all hidden electronic nodes in the hidden node topology graph, and mark hidden electronic nodes with a comprehensive feature drift greater than a preset drift threshold as active drift nodes. Using the topology graph as the spatial structure basis, start from each active drift node and traverse its adjacent active drift nodes using a breadth-first search algorithm. During the traversal, record the temporal order in which each hidden electronic node is first visited by an active drift node. Count the frequency of each hidden electronic node being visited by different active drift nodes, and mark hidden electronic nodes whose frequency exceeds a preset frequency threshold. Mark the intersection points of the propagation paths; further analyze the correlation of the comprehensive feature drift between each intersection point and its adjacent nodes, and calculate the cross-correlation coefficient of the time series of drift values ​​between the intersection points and the adjacent hidden electronic nodes; if the cross-correlation coefficient is greater than the preset correlation coefficient threshold, and the intersection point is located at the convergence point of the propagation paths of multiple active drift nodes, then the hidden electronic node corresponding to the intersection point is identified as a key relay hidden electronic node; where the correlation coefficient threshold is used to determine whether the correlation between the time series of comprehensive feature drift values ​​of the two nodes is significant.

[0044] S63. When the cumulative drift of the comprehensive characteristics of the key relay hidden electronic node exceeds the preset drift accumulation threshold, beam focusing scanning is performed on the detection area associated with the key relay hidden electronic node; wherein, the drift accumulation threshold is used to determine whether the change in the harmonic excitation response characteristics of the hidden electronic node is significant enough to affect the stability of the system. Understandably, the spatial coordinates of the key relay concealed electronic node are obtained and used as the center point for beam focusing. The phased array antenna array is controlled, and the phase delay of each antenna element is adjusted so that the main lobe direction of the transmitted beam is aligned with the center point of beam focusing, and the beam width is compressed to one-third of the normal scanning beam width to achieve energy focusing. In this focused beam state, an S-band swept frequency detection signal is transmitted and the echo is received. Since beam focusing improves the spatial resolution and signal-to-noise ratio of the detection area, the acquired echo signal contains more refined harmonic response details.

[0045] S64. Based on the high-resolution harmonic fingerprint obtained by beam focusing scanning, correct the spatial coordinates of key relay hidden electronic nodes and their adjacent hidden electronic nodes in the hidden node topology diagram. Specifically, the harmonic fingerprint of the key relay hidden electronic node is re-extracted; the time difference positioning method is used to calculate the three-dimensional spatial coordinates of the key relay hidden electronic node relative to the antenna array by analyzing the phase difference of the fundamental echo signal arriving at different receiving array elements, thus obtaining a high-precision position estimate; the spatial coordinates corresponding to the high-precision position estimate are used as the corrected spatial coordinates; for adjacent hidden electronic nodes directly adjacent to the key relay hidden electronic node, the historical positioning data of the adjacent hidden electronic nodes are called, and the corrected spatial coordinates of the key relay hidden electronic node are used as reference points. The relative positioning method is used to recalculate the spatial coordinates of each adjacent hidden electronic node based on the known distance and azimuth relationship between the key relay hidden electronic node and the adjacent hidden electronic nodes, thus completing the position correction.

[0046] It should be noted that the specific analysis process for correcting the nodal connection relationship of the boundary region based on the phase difference distribution field is as follows: S71. Obtain the harmonic response phase information of each hidden electronic node in the hidden node topology diagram: When transmitting the S-band sweep frequency detection signal, the initial phase of the transmitted signal is recorded synchronously; after the receiving channel performs down-conversion processing on the echo signal, the in-phase component and quadrature component of the fundamental component are extracted, and the phase value of the echo signal is obtained through arctangent operation; the echo phase is subtracted from the initial phase of transmission to obtain the harmonic response phase information.

[0047] S72. Based on the acquired harmonic response phase information, calculate the harmonic response phase difference between each pair of adjacent hidden electron nodes, and construct the phase difference distribution field based on the calculation results. Specifically, for each pair of adjacent hidden electron nodes in the hidden node topology graph, the harmonic response phase values ​​of the two hidden electron nodes are obtained, and the absolute value of the difference between the two harmonic response phase values ​​is calculated to obtain the harmonic response phase difference of the pair of adjacent hidden electron nodes. The phase difference values ​​of all adjacent hidden electron node pairs are spatially gridded according to the position of each hidden electron node in the spatial coordinate system, and each phase difference value is assigned to the midpoint of the line connecting the corresponding hidden electron node pair. The inverse distance weighted interpolation method is used to interpolate and fill the unassigned points in the grid to form a phase difference distribution field covering the entire topology graph region.

[0048] S73. Identify regions where the phase difference changes abruptly in the phase difference distribution field and mark these regions as nonlinear node boundary regions.

[0049] S74. The steps for performing multi-angle scanning on the nonlinear node boundary region to obtain the harmonic response directionality characteristics of this region are as follows: After identifying the region where the phase difference changes abruptly, the geometric center of this region is used as the scanning center point. The mechanical rotating platform carrying the detection antenna is controlled to rotate sequentially to multiple preset angles in the horizontal plane with the center point as the center, with each angle spaced at 15 degrees. At each angle position, an S-band sweep frequency detection signal is transmitted and the echo is received. The phase distribution and amplitude distribution of the harmonic response in the boundary region at that angle are recorded. By analyzing the echo characteristics at different angles, the directional characteristics of the harmonic response in the boundary region are obtained, that is, the law of the change of harmonic response intensity with the detection direction.

[0050] S75. Based on the directional characteristics of harmonic response, the process of correcting the connection relationship of hidden electronic nodes in the boundary region of the hidden node topology diagram is as follows: Based on the harmonic response directional characteristics obtained from multi-angle scanning, hidden electron nodes with obvious direction-dependent harmonic responses in the boundary region are identified. The stability of the harmonic responses of hidden electron nodes under different detection directions is analyzed, and the direction of stable response is determined as the main response direction of the hidden electron node. The connection relationship of hidden electron nodes in the boundary region in the hidden node topology map is compared with the main response direction of each hidden electron node. If the main response directions of two adjacent hidden electron nodes point to each other, the connection relationship between the two hidden electron nodes is retained or strengthened. If the main response directions of two adjacent hidden electron nodes are opposite to each other and there are other hidden electron nodes with inconsistent directional characteristics in between, the original direct connection relationship is broken, and the connection is re-established based on the similarity of directional characteristics.

[0051] The specific implementation of determining the precursory state of instability and outputting early warning information is as follows: S81. After marking the nonlinear response abnormal node, obtain the family of harmonic excitation response curves of the nonlinear response abnormal node in multiple consecutive scan cycles. Specifically, continuous monitoring is maintained on nodes with abnormal nonlinear responses; after each scan cycle, the harmonic excitation response curve of that node is extracted, including complete data points on the correspondence between transmit power level and harmonic conversion efficiency; the harmonic excitation response curve data extracted in each cycle is stored in the dedicated historical record of that node with abnormal nonlinear response; when the number of stored harmonic excitation response curves reaches the preset curve family analysis window length, all harmonic excitation response curves in the window are retrieved to form a curve family, where each curve corresponds to one scan cycle.

[0052] S82. Determine the area difference between adjacent harmonic excitation response curves in the curve family, forming a time series of difference values, including: The harmonic excitation response curves in the curve family are arranged in chronological order according to the scanning period. For two adjacent harmonic excitation response curves, the harmonic conversion efficiency values ​​of the two harmonic excitation response curves within the same transmit power level range are subtracted to obtain the difference curve. The difference curve is numerically integrated, and the area enclosed by the difference curve and the horizontal axis is calculated. This area value is taken as the area difference value between two adjacent harmonic excitation response curves. The above calculation is repeated for all adjacent harmonic excitation response curves in the curve family to obtain a sequence of area difference values ​​arranged in chronological order, which is the difference value time series sequence.

[0053] S83. If the time series of difference values ​​shows a monotonically increasing trend, then the state of the nonlinear response abnormal node is marked as an instability precursor state. Understandably, after obtaining the time series of difference values, a linear trend fitting is performed on the time series of difference values, and the slope of the fitted line is calculated. If the slope is greater than zero, and the last three values ​​in the time series of difference values ​​are all greater than the maximum value among the first three values, then it is determined that the time series of difference values ​​shows a monotonically increasing trend. The state of the current nonlinear response abnormal node is marked as an unstable precursor state, and the time point when the state is first triggered is recorded.

[0054] S84. For nonlinear response anomaly nodes marked as instability precursor states, generate early warning information and associate the spatial coordinates of the nonlinear response anomaly nodes with the instability precursor states and output them. The spatial coordinates of the nonlinear response anomaly node are derived from the position coordinates determined when the hidden electronic node was first identified. These position coordinates are obtained based on a multi-angle positioning algorithm. By controlling the detection antenna to transmit signals at different spatial locations and receiving the echoes, the three-dimensional spatial coordinates of the hidden electronic node are obtained by jointly calculating the time difference of arrival and the angle of arrival. In subsequent monitoring, if the hidden electronic node is marked as an anomaly and its position coordinates are corrected by beam focusing scanning, the corrected high-precision coordinates are used as the spatial coordinates of the hidden electronic node.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0056] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0057] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting, characterized in that: Signal acquisition and processing module: acquires the fundamental echo signal and second harmonic echo signal in the S-band of multiple detection nodes within the detection area; Fingerprint database construction module: Based on the amplitude ratio of the second harmonic echo signal to the fundamental echo signal, it constructs the harmonic excitation response characteristics of each detection node, and generates a nonlinear node fingerprint database based on the harmonic excitation response characteristics, including: For each detection node, the amplitudes of the fundamental echo signal and the second harmonic echo signal are obtained at multiple transmission power levels. The ratio of the second harmonic echo signal amplitude to the fundamental echo signal amplitude at each transmission power level is calculated as the harmonic conversion efficiency at that power level. The harmonic conversion efficiencies at each power level are arranged in time sequence to generate harmonic excitation response curves. The slope of the nonlinear segment and the inflection point of the saturation segment of the harmonic excitation response curve are extracted as the harmonic excitation response characteristics of the detection node. The spatial coordinates of each detection node are associated and stored with the corresponding harmonic excitation response characteristics to construct a nonlinear node fingerprint database. Real-time fingerprint extraction module: When the system receives an externally triggered covert scanning command, it transmits an S-band sweep frequency detection signal to the detection area and collects the harmonic components in the echo signal in real time to extract the real-time harmonic fingerprint; wherein, the echo signal includes the fundamental echo signal and the second harmonic echo signal; Node identification and matching module: performs time-series correlation matching between real-time harmonic fingerprints and fingerprints in the nonlinear node fingerprint database, and identifies hidden electronic nodes that match fingerprints in the nonlinear node fingerprint database; Topology graph generation and marking module: Based on the identified hidden electronic nodes, analyze their spatial distribution and harmonic response stability, generate a hidden node topology graph, and mark nodes with nonlinear response anomalies.

2. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprint recognition according to claim 1, characterized in that, The process of transmitting an S-band swept frequency detection signal to the detection area and acquiring harmonic components in the echo signal in real time to extract real-time harmonic fingerprints includes: Based on the scanning bandwidth and step frequency carried in the covert scanning command, an S-band sweep frequency detection signal sequence is generated; From the S-band sweep frequency detection signal sequence, each sweep frequency detection signal is extracted sequentially according to the preset frequency step order, and the echo signal corresponding to each transmission frequency is received synchronously. Each transmission frequency corresponds to a frequency point. Separate the fundamental component and the second harmonic component from the echo signal, and record the fundamental amplitude and the second harmonic amplitude corresponding to each frequency point; By analyzing the ratio of the second harmonic amplitude to the fundamental amplitude at each frequency point, a frequency-harmonic response sequence is formed; Peak detection is performed on the frequency-harmonic response sequence, and the frequency point corresponding to the peak value and the amplitude ratio are used as real-time harmonic fingerprints.

3. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprint recognition according to claim 2, characterized in that, The step of performing time-series correlation matching between real-time harmonic fingerprints and fingerprints in a nonlinear node fingerprint database to identify hidden electronic nodes that match fingerprints in the nonlinear node fingerprint database includes: Based on the frequency point corresponding to the peak value, the peak frequency point in the real-time harmonic fingerprint is determined, and the peak frequency point in the real-time harmonic fingerprint is compared with the peak frequency points of each fingerprint in the nonlinear node fingerprint database to obtain the frequency deviation value. When the frequency deviation value is less than the preset frequency deviation threshold, the correlation coefficient is obtained by analyzing the correlation between the real-time harmonic fingerprint and the harmonic excitation response curve of the corresponding fingerprint. The detection nodes corresponding to fingerprints with correlation coefficients greater than a preset correlation coefficient threshold are marked as candidate matching nodes; Obtain the real-time harmonic fingerprints of candidate matching nodes over multiple consecutive scan cycles and analyze their harmonic response stability. When the harmonic response stability is greater than the preset stability threshold, the candidate matching node is identified as a hidden electronic node that matches the fingerprint in the nonlinear node fingerprint database.

4. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting according to claim 3, characterized in that, The process involves analyzing the spatial distribution and harmonic response stability of the identified hidden electronic nodes, generating a hidden node topology map, and marking nodes with abnormal nonlinear responses, including: The identified hidden electronic nodes are mapped onto the spatial coordinate system of the detection area to form a spatial distribution map of the nodes; Using the node spatial distribution map as the initial topology, the stability difference gradient between each hidden electron node is calculated based on the harmonic response stability of each hidden electron node. Connect adjacent hidden electron nodes whose stability difference gradient is greater than a preset gradient threshold to construct a hidden node topology graph. In the hidden node topology diagram, detect whether the harmonic response stability of each hidden electronic node shows a monotonically decreasing trend within a continuous scanning period; For a hidden electronic node exhibiting a monotonically decreasing trend, if the difference in harmonic response stability between the hidden electronic node and its directly connected hidden electronic nodes exceeds a preset difference threshold, the hidden electronic node is marked as a nonlinear response anomalous node and highlighted in the hidden node topology diagram.

5. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting according to claim 4, characterized in that, The system also includes: Obtain the real-time harmonic fingerprint of nodes marked as nonlinear response anomalies, and extract the slope change rate of the nonlinear segment in the harmonic excitation response curve of the nonlinear response anomaly node. When the rate of change of the slope of the nonlinear segment exceeds the preset rate of change threshold, the nonlinear response abnormal node is determined to be in the state transition stage. For nonlinear response anomaly nodes in the state transition stage, the transmission power of the S-band sweep frequency detection signal within a preset radius centered on the nonlinear response anomaly node is adjusted to a preset high power level, and the sampling frequency is adjusted to a preset high frequency sampling level. Based on the adjusted transmit power and sampling frequency, the real-time harmonic fingerprint of the nonlinear response abnormal node is re-acquired, and the feature parameters of the corresponding fingerprint in the nonlinear node fingerprint database are updated.

6. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting according to claim 4, characterized in that, The system also includes: After generating the hidden node topology graph, the comprehensive feature drift of the harmonic excitation response characteristics of the hidden electronic nodes in the hidden node topology graph over time is detected. By analyzing the spatial propagation path of the comprehensive feature drift, key relay hidden electronic nodes on the propagation path are identified. When the cumulative drift of the comprehensive feature of a key relay hidden electronic node exceeds the preset drift cumulative threshold, beam focusing scanning is performed on the detection area associated with the key relay hidden electronic node. Based on the high-resolution harmonic fingerprint obtained by beam focusing scanning, the spatial coordinates of key relay hidden electronic nodes and their adjacent hidden electronic nodes in the hidden node topology map are corrected.

7. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting according to claim 4, characterized in that, The system also includes: Obtain the harmonic response phase information of each hidden electronic node in the hidden node topology graph; Based on the acquired harmonic response phase information, the harmonic response phase difference between each pair of adjacent hidden electron nodes is calculated, and the phase difference distribution field is constructed according to the calculation results. In the phase difference distribution field, identify regions where the phase difference changes abruptly, and mark these regions as nonlinear node boundary regions; Perform multi-angle scanning on the nonlinear node boundary region to obtain the harmonic response directionality characteristics of the region; Based on the directional characteristics of harmonic response, the connection relationships of hidden electronic nodes in the boundary region of the hidden node topology diagram are corrected.

8. The nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting according to claim 4, characterized in that, The system also includes: After marking the nonlinear response anomalous node, obtain the family of harmonic excitation response curves of the nonlinear response anomalous node in multiple consecutive scan cycles; Determine the area difference between adjacent harmonic excitation response curves in the family of curves, and form a time series of the difference values; If the time series of difference values ​​shows a monotonically increasing trend, then the state of the nonlinear response abnormal node is marked as an instability precursor state. For nonlinear response anomaly nodes marked as instability precursor states, early warning information is generated, and the spatial coordinates of the nonlinear response anomaly node are associated with the instability precursor state and output.

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