A multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring

Abstract

This invention relates to the field of radio communication and signal processing technology, specifically to a multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks. The system includes: acquiring broadband digital data to generate a time-frequency matrix and constructing steady-state time-frequency reference data accordingly; generating simulation excitation parameters based on preset parameters; calculating the theoretical residual between the excitation parameters and the reference data, as well as the actual residual between the real-time data and the reference data; calculating the time-frequency structural similarity between the theoretical and actual residuals; triggering the generation of narrowband data using channelization resources when the matching degree reaches a preset threshold, otherwise not triggering; performing parameter estimation and modulation identification on the narrowband data, and dynamically updating the reference data or simulation excitation parameters based on the decision and identification results. This invention significantly reduces the probability of false triggering by misjudging environmental noise as a frequency hopping target.

Description

Technical Field

[0001] This invention relates to the field of radio communication and signal processing technology, and specifically to a multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks. Background Technology

[0002] In the current complex electromagnetic environment such as border ports, mobile broadband monitoring terminals need to continuously receive broadband wireless signals in order to identify communication network activities suspected of using adaptive frequency hopping.

[0003] To intercept such frequency-hopping targets, existing reconnaissance schemes generally adopt a processing architecture that directly sets a fixed energy threshold for detection, and uses manual frequency selection or full-band fine scanning. Although this scheme has basic signal reception capabilities, real frequency-hopping communication is often intertwined with random pulse interference and short-range legitimate burst traffic, which makes the system prone to misjudging transient energy rises as suspicious targets, resulting in a large amount of invalid narrowband data capture. Due to the lack of environmental steady-state stripping mechanisms and structured theoretical prediction models, the existing processing flow is slow to respond and has a lot of blind scanning. Under limited computing power, it is easy to over-consume concurrent channelization processing resources, making it difficult to support the rapid screening of high-confidence targets.

[0004] Therefore, how to effectively separate occasional interference services from real frequency hopping targets in complex wireless environments, reduce the probability of false triggering, and improve the utilization efficiency of limited channelization processing resources has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring, solving the following technical problems:

[0006] By constructing a topology matching mechanism between a steady-state time-frequency reference and theoretical simulation excitation, we can accurately identify frequency-hopping targets in complex electromagnetic environments. This reduces false triggering and concurrent resource consumption while improving the effectiveness of intercepting suspected targets and environmental adaptability.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks, the system comprising:

[0009] The data acquisition module includes a broadband radio frequency front-end and a global clock unit, used to acquire broadband digital sampling data within a preset monitoring frequency band, and generate a time-frequency matrix based on the broadband digital sampling data; the time-frequency matrix is ​​a two-dimensional energy distribution matrix containing time series and frequency units;

[0010] The reference reconstruction module is communicatively connected to the data acquisition module and is used to construct steady-state time-frequency reference data based on the broadband digital sampling data and the time-frequency matrix.

[0011] The parameter generation module is used to generate simulation excitation parameters based on preset modulation type parameters, symbol rate parameters, and frequency change parameters.

[0012] The theoretical residual generation module is communicatively connected to the parameter generation module and is used to generate theoretical residual data relative to the steady-state time-frequency reference data based on the steady-state time-frequency reference data and the simulation excitation parameters.

[0013] The real residual generation module is used to generate real residual data based on the difference relationship between the real-time time-frequency data contained in the time-frequency matrix and the steady-state time-frequency reference data;

[0014] The time-frequency topology matching module is used to calculate the time-frequency structural similarity between the theoretical residual data and the actual residual data, and generate a judgment result containing a matching degree index based on the calculation result.

[0015] The channel scheduling module is used to allocate channelization processing resources to generate narrowband data when the matching degree index is greater than or equal to a preset trigger threshold, and to keep the channelization processing resources from being triggered when the matching degree index is less than the preset trigger threshold.

[0016] The identification module is used to perform parameter estimation and modulation identification on the narrowband data and output the identification results;

[0017] The feedback update module is used to update the steady-state time-frequency reference data and / or the simulation excitation parameters according to the decision result and the identification result and a preset update rule.

[0018] Preferably, the data acquisition module includes:

[0019] A broadband receiving unit is used to acquire analog radio frequency signals within a preset monitoring frequency band;

[0020] An analog-to-digital converter unit, connected to the broadband receiving unit, is used to convert the analog radio frequency signal into the broadband digital sampling data;

[0021] The time-frequency conversion unit, connected to the analog-to-digital conversion unit, is used to perform time-frequency conversion on the broadband digital sampling data and generate the time-frequency matrix.

[0022] Preferably, the baseline reconstruction module includes:

[0023] Envelope tracking unit is used to extract the envelope data of the broadband digital sampling data after smoothing by a preset time window;

[0024] An energy cancellation unit, connected to the envelope tracking unit, is used to subtract the moving average value of the envelope data to eliminate transient energy components and generate background energy data.

[0025] A reference modeling unit, connected to the energy cancellation unit, is used to generate the steady-state time-frequency reference data based on the background energy data and the time-frequency matrix.

[0026] Preferably, the parameter generation module includes:

[0027] The modulation parameter storage unit is used to store preset modulation type parameters and symbol rate parameters;

[0028] The frequency variation parameter storage unit is used to store preset dwell time parameters, frequency step parameters, and bandwidth parameters;

[0029] An excitation construction unit, connected to the modulation parameter storage unit and the frequency variation parameter storage unit, is used to generate the simulation excitation parameters based on the modulation type parameter, the symbol rate parameter, the dwell time parameter, the frequency step parameter, and the bandwidth parameter.

[0030] The preset parameters are determined by at least one of the following: statistical results of historical monitoring samples, standard signal system parameter library, or manual configuration.

[0031] Preferably, the theoretical residual generation module is also used for:

[0032] Based on the simulation excitation parameters, the steady-state time-frequency reference data is superimposed or mapped to generate simulated time-frequency data.

[0033] The simulated time-frequency data and the steady-state time-frequency reference data are differentially processed to generate the theoretical residual data;

[0034] The theoretical residual data is used to characterize the time-frequency structure features corresponding to the frequency variation pattern defined by the residence time parameter, frequency step parameter, and bandwidth parameter.

[0035] Preferably, the real residual generation module is also used for:

[0036] The real-time time-frequency data contained in the time-frequency matrix is ​​differentially processed with the steady-state time-frequency reference data to generate the actual residual data;

[0037] The real residual data is used to characterize the transient energy distribution features in a real-time wireless communication environment.

[0038] Preferably, the time-frequency topology matching module includes:

[0039] The relevant calculation unit is used to generate a relevant score by calculating the cross-correlation coefficient between the theoretical residual data and the actual residual data;

[0040] A coherence judgment unit, connected to the relevant calculation unit, is used to perform a moving average processing on the relevant scores within multiple consecutive time windows to generate a coherence index as the matching degree index.

[0041] The decision unit, connected to the continuity judgment unit, is used to generate a pass decision when the continuity index is greater than or equal to a preset trigger threshold, and to generate a suppress decision when the continuity index is less than the preset trigger threshold.

[0042] Preferably, the channel scheduling module includes:

[0043] A parameter extraction unit is used to extract the center frequency parameter and bandwidth parameter from the actual residual data during the decision generation process.

[0044] A resource allocation unit, connected to the parameter extraction unit, is used to allocate concurrent channelization processing resources based on the center frequency parameter and the bandwidth parameter;

[0045] A data extraction unit, connected to the resource allocation unit, is used to output narrowband digital data obtained through channelization processing;

[0046] The resource allocation unit is also used to prevent the channelization processing resources from being triggered when the suppression decision is generated.

[0047] Preferably, the identification module includes:

[0048] The parameter estimation unit is used to perform symbol rate estimation, carrier frequency estimation, and bandwidth estimation on the narrowband digital data.

[0049] A feature matching unit, connected to the parameter estimation unit, is used to match the estimation results with a preset feature library;

[0050] A modulation recognition unit, connected to the feature matching unit, is used to output a modulation recognition result based on the matching result;

[0051] The preset feature library includes at least one of modulation scheme feature template, symbol rate range template, carrier frequency offset template, and bandwidth template.

[0052] Preferably, the feedback update module is also used for:

[0053] When the identification result meets the preset steady-state conditions, the steady-state time-frequency reference data is updated;

[0054] When the recognition result meets the preset change conditions, the simulation excitation parameters are updated;

[0055] When the number of consecutive suppression decisions reaches a preset number, the preset trigger threshold is reduced, and the reduced threshold is not lower than the lower limit of the noise confidence level calculated based on the current background energy data of the system.

[0056] While the decision result remains a pass decision, the currently allocated channelization processing resources and corresponding narrowband extraction parameters are maintained.

[0057] The preset steady-state condition is that the identification results are consistent within a preset number of consecutive time windows and the variance of the actual residual data is less than a preset variance threshold. The preset change condition is that the identification results are inconsistent with the current steady-state time-frequency reference data within a preset number of consecutive time windows and the coherence index is greater than a preset update threshold.

[0058] The beneficial effects of this invention are:

[0059] 1. This invention constructs steady-state time-frequency reference data and uses the difference between real-time time-frequency data and steady-state reference data to generate real residual data, which changes the traditional detection method with fixed energy threshold; combined with theoretical residual data for time-frequency topology matching, it can effectively separate slowly changing background from transient anomalies in complex wireless environments, accurately remove random pulses and occasional burst services, and significantly reduce the probability of false triggering by misjudging environmental noise as frequency hopping targets;

[0060] 2. This invention introduces a channel scheduling mechanism, which generates a pass decision only when the time-frequency structure matching degree index reaches a preset trigger threshold. Then, it extracts the center frequency and bandwidth parameters from the actual residual data and dynamically allocates concurrent channelization processing resources. This closed-loop approach avoids blind full-band scanning and invalid narrowband capture, allowing limited computing power and channelization resources to be precisely focused on high-reliability frequency hopping targets, which greatly improves the utilization efficiency of concurrent resources and the system response speed.

[0061] 3. The present invention adopts a feedback update mechanism, which dynamically updates steady-state time-frequency reference data and simulation excitation parameters according to preset rules based on narrowband parameter estimation and modulation identification results, and adaptively adjusts the trigger threshold. This closed-loop learning capability enables the system to evolve autonomously based on long-term observation results, continuously correct the environmental background model and candidate target prediction template, effectively enhance the system's adaptability to dynamic and complex electromagnetic environments, and reduce the risk of missed and false alarms under long-term operation. Attached Figure Description

[0062] The invention will now be further described with reference to the accompanying drawings.

[0063] Figure 1 This is a structural block diagram of a multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring, as described in an embodiment of this application. Detailed Implementation

[0064] 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.

[0065] Please see Figure 1 A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks is disclosed. The system includes a data acquisition module, comprising a broadband radio frequency front-end and a global clock unit, for acquiring broadband digital sampling data within a preset monitoring frequency band and generating a time-frequency matrix based on the broadband digital sampling data; the time-frequency matrix is ​​a two-dimensional energy distribution matrix containing time series and frequency units.

[0066] The reference reconstruction module is communicatively connected to the data acquisition module and is used to construct steady-state time-frequency reference data based on the broadband digital sampling data and the time-frequency matrix.

[0067] The parameter generation module is used to generate simulation excitation parameters based on preset modulation type parameters, symbol rate parameters, and frequency change parameters.

[0068] The theoretical residual generation module is communicatively connected to the parameter generation module and is used to generate theoretical residual data relative to the steady-state time-frequency reference data based on the steady-state time-frequency reference data and the simulation excitation parameters.

[0069] The real residual generation module is used to generate real residual data based on the difference relationship between the real-time time-frequency data contained in the time-frequency matrix and the steady-state time-frequency reference data; the time-frequency topology matching module is used to perform time-frequency structure similarity calculation on the theoretical residual data and the real residual data, and generate a judgment result containing a matching degree index based on the calculation result.

[0070] The channel scheduling module is used to allocate channelization processing resources to generate narrowband data when the matching degree index is greater than or equal to a preset trigger threshold, and to keep the channelization processing resources from being triggered when the matching degree index is less than the preset trigger threshold.

[0071] The identification module is used to perform parameter estimation and modulation identification on the narrowband data and output the identification result; the feedback update module is used to update the steady-state time-frequency reference data and / or the simulation excitation parameters according to the decision result and the identification result and a preset update rule.

[0072] This embodiment provides a multi-band adaptive frequency hopping reconnaissance signal processing mechanism for continuous monitoring of the radio environment at border ports. Specifically, the system is deployed in a mobile broadband monitoring terminal, which continuously receives broadband wireless signals between the port logistics park, vehicle inspection channel and perimeter liaison area. This signal is used to identify communication network activities suspected of using adaptive frequency hopping from a complex electromagnetic environment, and triggers subsequent narrowband fine processing only for high-confidence targets under limited computing resources.

[0073] Specifically, the system receives broadband digital sampling data within a preset monitoring frequency band and converts the data into a time-frequency matrix; here, the time-frequency matrix represents the two-dimensional energy distribution state interwoven with the time dimension and the frequency dimension;

[0074] For ease of explanation, assuming that only 4 frequency units and 4 time units are observed within a short time window, the real-time time-frequency matrix can be simplified as follows: the first time is [1,1,5,1], the second time is [1,1,1,1], the third time is [1,4,1,1], and the fourth time is [1,1,1,6].

[0075] Among them, the value 1 represents the background energy level, and the values ​​4, 5, and 6 represent certain transient energy increases. If we only look at the energy magnitude, it is difficult to distinguish whether these increases come from real frequency hopping communication, random pulse interference, or other legitimate short burst services in the park.

[0076] Therefore, after obtaining the time-frequency matrix, the system does not directly trigger with a fixed energy threshold, but first reconstructs a steady-state time-frequency reference data; this steady-state reference is used to represent the time-frequency reference distribution composed of background noise and known steady-state services in the current environment, without considering abnormal frequency hopping bursts;

[0077] After the steady-state time-frequency reference is established, the parameter generation module constructs a set of simulation excitation parameters based on the preset modulation type parameters, symbol rate parameters, and frequency change parameters. These parameters are not directly derived from the detection results at the current moment, but rather from historical monitoring experience, known system samples, or operation and maintenance configurations, and are used to describe possible target frequency hopping behavior.

[0078] For example, we can assume that a candidate target uses binary frequency shift keying, with a symbol rate of 50 ksps, a dwell time of one time window, and a frequency stepping pattern that jumps between four candidate frequency points; the system applies these parameters to the aforementioned steady-state time-frequency reference to generate a frequency hopping perturbation pattern that should theoretically occur.

[0079] The theoretical residual generation module generates theoretical residual data based on the steady-state time-frequency reference and the simulation excitation parameters, while the real residual generation module subtracts the real-time observed time-frequency matrix from the steady-state time-frequency reference to obtain the real residual data.

[0080] Taking the 4×4 matrix mentioned above as an example, if the steady-state time-frequency reference is approximately a matrix of all 1s, then the actual residual data can be represented as follows: the first time step is [0,0,4,0], the second time step is [0,0,0,0], the third time step is [0,3,0,0], and the fourth time step is [0,0,0,5]. If the theoretical residual derived from the simulation excitation parameters is: the first time step is [0,0,4,0], the second time step is [0,0,0,0], the third time step is [0,3,0,0], and the fourth time step is [0,0,0,4].

[0081] The topological distributions of the two at time-frequency positions are highly similar, indicating that the measured transient energy is not isolated noise, but rather has a consistency with the preset frequency hopping structure that meets a preset threshold. At this point, the time-frequency topology matching module calculates the structural similarity between the two and forms a matching degree index. This matching can be achieved by methods such as cross-correlation, normalized overlap rate, and connectivity path consistency. Taking the simplified overlap rate as an example, if there are 3 non-zero positions in the theoretical residual, and the actual residual has corresponding energy increases at all 3 positions, then the initial matching degree can be recorded as 1.0; if only 2 positions are matched, then it can be recorded as 0.67.

[0082] When the matching degree index is greater than or equal to the preset trigger threshold, the channel scheduling module allocates channelization processing resources to perform narrowband extraction on the frequency band near the corresponding center frequency and generate narrowband data.

[0083] Subsequently, the identification module performs parameter estimation and modulation identification on the narrowband data to obtain results such as carrier frequency offset, symbol rate, bandwidth, and modulation category. The feedback update module then combines the decision results with the identification results to dynamically update the steady-state time-frequency reference data and simulation excitation parameters. If the identification results are stable and consistent with the current reference multiple times, they can be incorporated into the steady-state background. If new modulation and frequency hopping patterns continue to emerge, the simulation excitation parameters are updated to improve the targeting of subsequent detection.

[0084] Furthermore, in terms of abnormal data processing, if the energy amplitude is greater than the preset energy decision threshold within a certain time window, such as an isolated spike appearing only at a single frequency point, or an irregular burst occurring at multiple frequencies simultaneously, the matching degree index may be locally high, but the continuity is insufficient. The system can output a suppression decision without triggering channelization resources.

[0085] If the actual residual is lower than the preset noise threshold, while the theoretical residual is not empty, it means that the corresponding target activity has not been observed. In this case, the existing benchmark remains unchanged. If the narrowband data obtained by the identification module is of insufficient quality and cannot output reliable modulation results, the feedback update module will only record the decision and will not update the steady-state benchmark or excitation parameters accordingly to avoid incorrect learning.

[0086] For example, during the low-traffic period at border crossings at night, there are always-on intercom links, fixed video backhaul links, and scattered Bluetooth devices in the logistics park. These services form a relatively stable background texture on the broadband time-frequency map. During a certain period, multiple short-staying and cross-frequency point migrating bursts of discrete time-frequency energy clusters suddenly appear near the inspection channel.

[0087] If energy threshold detection is used directly, the system will include forklift remote control devices, temporary sensor reports, and metal spark interference in the candidates, resulting in a large number of invalid narrowband captures. With this embodiment, the system first reconstructs steady-state time-frequency reference data from the overall environment, then synthesizes theoretical residuals using candidate frequency hopping rules, and performs topological comparison with the actual residuals. Only when these sudden discrete time-frequency energy clusters match the candidate models in terms of dwell rhythm, step path, and bandwidth profile will the narrowband extraction and identification task be issued.

[0088] The purpose of this step is to first construct a predictable background in a complex wireless environment, and then use a closed-loop mechanism of theoretical generation and reality comparison to separate random pulses, sporadic burst services and real frequency hopping targets, thereby achieving the technical effects of reducing the probability of false triggering, reducing the overhead of concurrent channelization processing and improving the effectiveness of intercepting suspected targets.

[0089] In a preferred embodiment of the present invention, the data acquisition module includes: a broadband receiving unit for acquiring analog radio frequency signals within a preset monitoring frequency band; an analog-to-digital conversion unit connected to the broadband receiving unit for converting the analog radio frequency signals into broadband digital sampling data; and a time-frequency conversion unit connected to the analog-to-digital conversion unit for performing time-frequency conversion on the broadband digital sampling data and generating the time-frequency matrix.

[0090] This embodiment provides a data acquisition mechanism for the above-mentioned continuous monitoring scenario of port wireless environment; specifically, in the aforementioned overall process, if it is only abstractly described as acquiring broadband data and generating a time-frequency matrix, the connection between analog reception, digital discretization and time-frequency expansion in actual engineering is easily overlooked.

[0091] To ensure consistent input for subsequent benchmark reconstruction and residual comparison, this embodiment breaks down the data acquisition process into three consecutive steps: wideband reception, analog-to-digital conversion, and time-frequency transformation.

[0092] Specifically, the broadband receiving unit receives analog signals within a preset monitoring frequency band through a radio frequency front end; the preset monitoring frequency band can be a continuous frequency band or a spliced ​​interval obtained by time-division scanning of multiple discrete frequency bands; in port scenarios, it can prioritize the coverage of frequency bands that are commonly used for short-range communication, logistics control, industrial sensing, and suspicious portable terminals that may be active; after down-conversion, filtering, and gain control, the received analog radio frequency signal is converted into an intermediate frequency or zero intermediate frequency signal suitable for digitization;

[0093] The analog-to-digital conversion unit samples the analog signal at a preset sampling rate and outputs wideband digital sampled data. For ease of understanding, it can be assumed that within a certain simplified window, the complex sequence obtained after sampling is [1+j0,0+j1,-1+j0,0-j1], which corresponds to the discrete rotation of a single frequency component.

[0094] If the sequence in another window is [2+j0,0+j2,-2+j0,0-j2], it indicates that the energy of that frequency component is enhanced. Through continuous sampling, the system can retain transient change information in time, providing a basis for subsequent short-term signal detection.

[0095] The time-frequency transformation unit performs frame segmentation processing on the broadband digital sampled data and performs time-frequency transformation on each frame to obtain the time-frequency matrix; in actual implementation, short-time Fourier transform can be used.

[0096] Using a simplified explanation as an example, suppose we divide 8 consecutive sampling points into 2 frames, with 4 points per frame. After spectral transformation, the amplitude of the first frame is 5 in the 3rd frequency unit, and the amplitude of the second frame is 4 in the 2nd frequency unit. This forms a simplified 2×4 time-frequency matrix: the first frame is [1,1,5,1], and the second frame is [1,4,1,1]. This type of matrix is ​​the direct input for subsequent steady-state modeling and real residual extraction.

[0097] Compared to simply providing the original in-phase orthogonal data, this embodiment further provides an intermediate representation of the time-frequency matrix. Its advantages are: on the one hand, it preserves the change trajectory in both time and frequency directions, making it easier to observe the dwell time, frequency step, and energy envelope; on the other hand, it also provides a unified coordinate system for the simulation mapping of the subsequent theoretical residuals, avoiding the computational complexity caused by direct comparison in the original time-domain sequence.

[0098] Furthermore, in terms of abnormal data processing, if an overload occurs at the front end during broadband reception, causing certain sampling intervals to be clipped, the analog-to-digital conversion unit can output an overload flag; the time-frequency conversion unit adds an abnormal flag to the corresponding frame when generating the time-frequency matrix, and the weight of this part of the data can be reduced during subsequent reference reconstruction.

[0099] If the sampling rate is lower than the Nyquist requirement of the current monitoring bandwidth, causing spectral aliasing, the data in this time window will not be directly entered into the baseline update process, but will only be used for alarm recording; if the received signal is too weak, causing the matrix to approach the noise floor, the window will still be retained and used as a steady-state background learning sample.

[0100] For example, during the daytime high signal density operation period at the border port, the park simultaneously has forklift control links, gate telemetry, intercom calls and warehouse sensor reporting; the broadband receiving unit continuously receives analog electromagnetic signals in a specified frequency band, the analog-to-digital conversion unit discretizes them into broadband digital sequences, and the time-frequency conversion unit further expands these sequences into a waterfall matrix that can be updated according to time windows;

[0101] When a suspicious device makes short-term dwell hops between different frequency points, this behavior will leave discrete energy regions on adjacent time frames, providing an input basis for subsequent steady-state separation and topology matching;

[0102] The purpose of this step is to transform the complex and continuously changing wireless environment into a computable and traceable digital time-frequency representation, thereby providing a standardized data entry point for subsequent differential modeling and target recognition.

[0103] In a preferred embodiment of the present invention, the reference reconstruction module includes: an envelope tracking unit, used to extract the envelope data of the broadband digital sampling data after smoothing by a preset time window; an energy cancellation unit, connected to the envelope tracking unit, used to subtract the moving average value of the envelope data to remove transient energy components and generate background energy data; and a reference modeling unit, connected to the energy cancellation unit, used to generate the steady-state time-frequency reference data based on the background energy data and the time-frequency matrix.

[0104] This embodiment provides a reconstruction mechanism for generating a steady-state time-frequency reference. Specifically, in the aforementioned data acquisition process, although the time-frequency matrix has been obtained, if it is directly used as a background reference, short bursts, occasional pulses, and even single anomalies will be mixed into the background feature distribution.

[0105] This would excessively weaken the subsequent real residuals, causing the characteristics of the real target to be incorrectly attributed to the steady-state background model. Therefore, this embodiment introduces a hierarchical processing approach of envelope tracking, energy cancellation, and baseline modeling to establish a more stable environmental base.

[0106] Specifically, the envelope tracking unit first performs a preset time window smoothing process on the broadband digital sampling data, and then extracts the envelope data. The smoothing window here is used to suppress the influence of single sampling point spikes. Taking a set of simplified envelope values ​​[2,2,8,2,2] as an example, if the third point corresponds to a short burst, after a sliding smoothing of length 3, an approximation of [2,4,4,4,2] can be obtained. At this time, the original spike is widened, but the numerical fluctuation range is within the preset smoothing threshold, which is convenient for subsequent differentiation between continuously rising background and short burst anomalies.

[0107] The energy cancellation unit calculates the moving average of the envelope data and subtracts the moving average from the smoothed envelope to eliminate transient energy components. To prevent occasional extreme energy spikes from excessively raising the average value within the moving average window, the energy cancellation unit evaluates and analyzes the samples within the data window according to preset calculation rules before calculating the moving average. If there are extreme points within the window that are higher than a preset multiple of the window's median, they are replaced with the median when calculating the mean, thereby ensuring that the calculated moving average base plate is not distorted by extreme isolated pulses.

[0108] Taking the smoothed result [2,4,4,4,2] as an example, if the mean algorithm is properly controlled so that the moving average is approximately [2.7,3.3,4.0,3.3,2.7], then subtracting the two will yield approximately [-0.7,0.7,0,0.7,-0.7]. In actual processing, negative values ​​can be truncated to 0 or treated as low-weighted values ​​to highlight the stable background. If another segment of data presents as a numerical sequence [3,3,3,3,3], subtracting it from the moving average will result in approximately all zeros, indicating that this segment better matches the characteristics of a stable background.

[0109] The baseline modeling unit fuses background energy data with the time-frequency matrix to form steady-state time-frequency baseline data. This can be understood as the system calculating a long-term reliable background value for each time window and frequency unit. Compared to simply averaging the time-frequency matrix, this embodiment first performs smoothing and cancellation on the time-domain envelope before entering baseline modeling. This is because simple time averaging can easily incorporate high-energy bursts directly into the background model, especially in port scenarios where there are short-lived legitimate transactions, making it easier for the background model to overfit instantaneous events. By first tracking the envelope and then removing transients, the subsequent steady-state time-frequency baseline can be made closer to the normal state of the real environment.

[0110] Furthermore, in terms of abnormal data processing, if the overall wireless environment slowly rises within a certain period of time, for example, due to the power-on of large equipment causing an overall increase in noise floor, the smoothed envelope and moving average will increase synchronously, and the residual after energy cancellation will not be misjudged as an abnormal burst. At this time, the reference modeling unit can gradually increase the background value of the corresponding frequency band.

[0111] If a frequency cell experiences intermittent high duty cycle short bursts over a long period, making it difficult to clearly classify it as steady state or abnormal, then the cell can be marked as an intermediate state energy cell. In subsequent calculations of the actual residual, a conservative deduction strategy will be adopted to avoid over-filtering out the real target. If the input data has a front-end saturation marker, then this time window will not participate in the baseline update.

[0112] For example, in the vehicle security check area of ​​a border port, the fixed video backhaul link has a stable carrier at certain frequency points for a long time, while the forklift remote control link exhibits irregular short bursts; the system finds through envelope tracking that the energy change rate is lower than the preset change rate threshold, and the energy is retained as background after cancellation.

[0113] If the energy amplitude is greater than the preset burst decision threshold, it is identified as a transient component after subtracting the moving average and is not included in the steady-state reference; the time-frequency reference formed in this way is more representative of the wireless base map under normal operating conditions.

[0114] The purpose of this step is to separate the slowly changing background from transient anomalies in a complex dynamic environment, thereby achieving the technical effect of constructing a purer steady-state background feature distribution and preserving target sensitivity for subsequent realistic residual extraction.

[0115] In a preferred embodiment of the present invention, the parameter generation module includes: a modulation parameter storage unit for storing preset modulation type parameters and symbol rate parameters; and a frequency variation parameter storage unit for storing preset dwell time parameters, frequency step parameters, and bandwidth parameters.

[0116] An excitation construction unit, connected to the modulation parameter storage unit and the frequency variation parameter storage unit, is used to generate the simulation excitation parameters based on the modulation type parameter, the symbol rate parameter, the dwell time parameter, the frequency step parameter, and the bandwidth parameter; wherein, the preset parameters are determined by at least one of the following: statistical results of historical monitoring samples, a standard signal system parameter library, or manual configuration.

[0117] This embodiment provides a parameter generation mechanism for constructing the theoretical behavior of candidate frequency hopping targets; specifically, a steady-state background alone is insufficient to complete effective screening; without a theoretical template for candidate targets, the actual residual can only passively observe energy anomalies, and it is still difficult to identify the objects that truly need to be monitored from a large number of short burst services;

[0118] Therefore, this embodiment further introduces a joint generation method for modulation parameters, symbol rate parameters, and frequency change parameters to construct simulation excitation parameters;

[0119] Specifically, the modulation parameter storage unit stores several preset modulation types and their corresponding symbol rate ranges; for example, it can store modulation categories such as binary frequency shift keying, quaternary frequency shift keying, binary phase shift keying, and quadrature phase shift keying, and maintain one or more typical symbol rate ranges for each category;

[0120] The frequency variation parameter storage unit stores parameters such as the dwell time, frequency step, and bandwidth of the candidate target. The dwell time describes how long the signal stays on a single frequency point, the frequency step describes the frequency difference or index change between adjacent jumps, and the bandwidth describes the width occupied on the frequency axis during each dwell.

[0121] The excitation construction unit selects one or more combinations from the above parameters to generate simulation excitation parameters. To illustrate with a simplified example, assume that the parameters of candidate group A are: modulation mode binary frequency shift keying, symbol rate 50ksps, dwell time 1 window, frequency step by frequency point 1→3→2→4, and single bandwidth occupies 1 frequency unit.

[0122] After the excitation is constructed, an ideal trajectory can be generated in a 4×4 time-frequency coordinate system: the first window is excited at frequency point 1, the second window is excited at frequency point 3, the third window is excited at frequency point 2, and the fourth window is excited at frequency point 4. If the parameters of candidate group B are a dwell time of 2 windows and a frequency step of 1→2→4, its trajectory will show that the first two windows stay at frequency point 1, the middle two windows stay at frequency point 2, and then move to frequency point 4. In this way, the system does not only maintain one template, but can maintain multiple parallel candidate excitations.

[0123] These preset parameters can be determined from multiple sources; some come from historical monitoring sample statistics, such as the common dwell time of a certain type of equipment being concentrated in 1 to 2 time windows; others come from standard signal system parameter libraries, such as the typical symbol rate and bandwidth relationship corresponding to known modulation methods.

[0124] Another part can be configured manually. For example, before the start of a special monitoring task, the operation and maintenance personnel can input the hopping rate range and frequency band range that they are focusing on. The combination of multi-source parameters makes the simulation excitation both experience-based and task-specific.

[0125] Compared to methods that do not introduce candidate priors and rely entirely on posterior classification, this embodiment first uses structured parameters to generate theoretical behavior and then matches it with real residuals. This changes the system from seeing an anomaly and then guessing what it is to first defining suspicious patterns and then verifying whether they occur, making it more suitable for portable monitoring terminals to perform rapid screening under conditions of limited computing resources.

[0126] If there are insufficient historical samples, resulting in a lack of statistical support for certain modulation or frequency hopping patterns, a more lenient excitation template can be constructed using a conservative range from the standard parameter library. The range can then be gradually narrowed as the identification results accumulate.

[0127] If there is a conflict between manual configuration and historical statistics, such as when the manually specified bandwidth is significantly beyond the reasonable range of this type of system, the system can retain both sets of parameters and assign them different priorities instead of directly overwriting them; if a set of incentives fails to effectively match the actual residuals in multiple consecutive monitoring periods, its calling frequency can be reduced to avoid occupying matching computing resources.

[0128] For example, when conducting special monitoring during key periods at border ports, maintenance personnel discovered, based on monitoring records from the previous week, that a certain type of suspicious portable terminal often uses narrowband frequency shift keying modulation, has a short dwell time, and exhibits an discrete stepwise frequency jump pattern; accordingly, the system pre-sets the corresponding modulation, symbol rate, and frequency change combination in the parameter storage unit.

[0129] After the nighttime monitoring task begins, the excitation building unit generates multiple candidate simulation excitations according to these combinations, which are then used for subsequent theoretical residual calculations.

[0130] The purpose of this mechanism is to integrate historical experience, standard institutional knowledge, and task-side concerns into computable candidate behavioral parameters, thereby providing structured priors for subsequent theoretical residual generation and improving the targeting of the screening process.

[0131] In a preferred embodiment of the present invention, the theoretical residual generation module is further configured to: construct or map the steady-state time-frequency reference data based on the simulation excitation parameters to generate simulated time-frequency data; and perform differential processing on the simulated time-frequency data and the steady-state time-frequency reference data to generate the theoretical residual data; wherein the theoretical residual data is used to characterize the time-frequency structure features corresponding to the frequency change pattern defined by the dwell time parameter, the frequency step parameter, and the bandwidth parameter.

[0132] This embodiment provides a theoretical residual generation mechanism. Specifically, the aforementioned parameter generation only provides abstract parameters of the candidate targets. If these parameters are not further implemented in the same coordinate space as the real time-frequency matrix, direct comparison cannot be carried out. Therefore, this embodiment superimposes or maps the simulation excitation parameters onto the steady-state time-frequency reference to obtain the simulation time-frequency data first, and then forms the theoretical residual data through difference.

[0133] Specifically, assuming the steady-state time-frequency reference is a 4×4 matrix with all elements initially set to 1, representing a uniform background; if a certain set of simulation excitations specifies that the first window resides at frequency 2, the second window resides at frequency 4, the third window resides at frequency 1, and the fourth window resides at frequency 3, and each residency bandwidth covers one frequency unit with an energy boost of 3, then the superimposed simulation time-frequency data can be represented as: first window [1,4,1,1], second window [1,1,1,4], third window [4,1,1,1], fourth window [1,1,4,1];

[0134] Subtracting the all-1 steady-state time-frequency reference from the simulated time-frequency data yields the theoretical residuals: first window [0,3,0,0], second window [0,0,0,3], third window [3,0,0,0], fourth window [0,0,3,0];

[0135] If the bandwidth parameter in the simulation excitation is not just one frequency unit, but covers two adjacent units, then the energy can be raised at two frequency positions simultaneously during superposition; if the dwell time is two time windows, then the same frequency position will have high values ​​in two consecutive rows; this means that the theoretical residual not only records which points the energy appears, but also naturally contains three types of structural information: duration, step trajectory, and frequency domain bandwidth.

[0136] The superposition and mapping constructions described here can be implemented in different ways; the superposition construction is suitable for directly adding candidate target energy to the steady-state time-frequency reference; the mapping construction is suitable for first generating an independent ideal trajectory template according to the parameters, and then projecting it onto the same time-frequency grid as the steady-state reference.

[0137] Both ultimately use the difference result as the theoretical residual, with the aim of zeroing out the background part and retaining only the structural features caused by the candidate frequency hopping mode;

[0138] Compared to directly comparing the simulated trajectory with the real-time time-frequency matrix, this embodiment first subtracts the same steady-state time-frequency reference; this allows both the theoretical space and the real space to be in a unified coordinate system relative to the background change, avoiding the influence of background strength changes on the structure matching results;

[0139] If a set of simulation excitations exceeds the current monitoring time window or frequency range after mapping, for example, if the candidate trajectory jumps to an uncovered frequency band, the excess part will not be included in the current theoretical residual, and only the part that falls into the current grid will be retained.

[0140] If the number of coverage units after bandwidth parameter mapping is too large, causing the theoretical residual to almost fill the entire row or column, the system can determine that the template resolution is insufficient and reduce its priority. If multiple candidate stimuli exist at the same time, multiple sets of theoretical residuals can be generated separately and compared in parallel in the subsequent matching stage, and the set with the highest matching value can be selected for decision.

[0141] For example, near the vehicle inspection area at the port, the system pre-determines candidate patterns for short dwell times, off-road movement, and narrowband occupancy for a certain type of key target; the steady-state time-frequency reference already includes the background texture of the park's permanent intercom and video transmission; after the excitation is constructed, the theoretical residual generation module overlays the candidate frequency hopping trajectory onto the background image, and then subtracts the background image itself to obtain a set of theoretical residual patterns that only retain the frequency hopping trajectory; subsequently, it is only necessary to determine whether the same pattern appears in reality, without being affected by the fixed background business;

[0142] The purpose of this step is to transform abstract candidate behavioral parameters into time-frequency structure templates that can be directly compared with real-time observations, thereby achieving the technical effect of providing a unified theoretical reference surface for topological similarity calculation.

[0143] In a preferred embodiment of the present invention, the real residual generation module is further configured to: perform differential processing on the real-time time-frequency data contained in the time-frequency matrix and the steady-state time-frequency reference data to generate the real residual data; wherein the real residual data is used to characterize the transient energy distribution characteristics in the real-time wireless communication environment.

[0144] This embodiment provides a realistic residual generation mechanism. Specifically, if the original real-time time-frequency matrix is ​​still used directly for comparison after the theoretical residual has been obtained, background noise, continuous wave services and resident narrowband links will significantly interfere with the structure matching. Therefore, this embodiment generates realistic residual data that only emphasizes transient changes by subtracting the steady-state time-frequency reference from the real-time time-frequency data.

[0145] Specifically, assuming the real-time time-frequency matrix under a certain time window is: first window [1,1,5,1], second window [1,1,1,1], third window [1,4,1,1], fourth window [1,1,1,6]; the steady-state time-frequency reference is a matrix of all 1s; then the difference yields the actual residual: first window [0,0,4,0], second window [0,0,0,0], third window [0,3,0,0], fourth window [0,0,0,5]; through this difference, all stable background components are canceled out, and the remaining non-zero elements are the transient rise in the current environment;

[0146] If a radio relay carrier exists on a certain frequency for a long time, the values ​​of the real-time matrix and the steady-state reference at that location are both high, but the difference between the two is close to 0, indicating that although the frequency has strong energy, it does not belong to a newly emerging anomaly. Conversely, if the background value of a certain frequency is originally 1, and suddenly increases to 5 in a certain time window, then the value after differentiation is 4, which is highlighted. In this way, the real residual is more suitable for reflecting dynamic characteristics such as suddenness, jump, and short dwell time.

[0147] Compared to directly thresholding the real-time matrix, this embodiment uses a residual extraction method based on background difference. In this way, even if the background noise of different frequency bands is not uniform, or if certain legitimate services occupy a high-energy state for a long time, the system can still focus on the change rather than the absolute intensity, which is more suitable for low signal-to-noise ratio and complex co-frequency service environments.

[0148] If the overall energy of a certain time window is lower than the overall energy of the reference, the subtraction will result in a negative value. For such negative values, zero-truncation can be performed to retain only the positive residuals, so as to avoid the energy drop having an irrelevant effect on frequency hopping detection. If a medium-amplitude positive residual appears in multiple consecutive windows at a certain frequency point, but does not meet any frequency hopping coherence, it can be recorded as a background drift candidate, and the subsequent feedback loop will decide whether to absorb it into the steady-state reference.

[0149] If the real-time frequency matrix rises simultaneously across the entire frequency band or wideband range due to strong external interference, and the continuous non-zero matrix units with an area exceeding the preset threshold indicate an abnormal environment, it is not directly equivalent to a frequency hopping target and still needs to be identified by subsequent topology matching.

[0150] For example, near the shift change period in the port storage area, a large number of short-range devices are turned on at the same time, and the energy of local frequency bands generally rises; after the actual residual is generated, the permanent video backhaul and intercom links are basically filtered out, leaving only the newly appearing short-term energy blocks.

[0151] If some of the energy blocks migrate along multiple time windows at discrete frequency points, a candidate structure that can be compared with the theoretical trajectory will be formed on the actual residual map; if it is only a brief rise at the same frequency point or a simultaneous rise across the entire frequency band, its shape will be significantly different from the candidate frequency hopping template.

[0152] The purpose of this step is to separate new changes in the real-time environment relative to the norm from the overall time-frequency map, thereby providing a cleaner and more realistic observation surface for subsequent structural similarity determination.

[0153] In a preferred embodiment of the present invention, the time-frequency topology matching module includes: a correlation calculation unit, used to generate a correlation score by calculating the cross-correlation coefficient between the theoretical residual data and the actual residual data;

[0154] A coherence judgment unit, connected to the relevant calculation unit, is used to perform a moving average processing on the relevant scores within multiple consecutive time windows to generate a coherence index as the matching degree index.

[0155] The decision unit, connected to the continuity judgment unit, is used to generate a pass decision when the continuity index is greater than or equal to a preset trigger threshold, and to generate a suppress decision when the continuity index is less than the preset trigger threshold.

[0156] This embodiment provides a time-frequency topology matching and decision mechanism; specifically, simply generating theoretical residuals and actual residuals is not enough, because in complex environments, there are often situations where local similarities occur but the overall structure is inconsistent.

[0157] For example, a random interference may happen to fall on a candidate frequency point, and a high similarity will be obtained when comparing in a single window; if subsequent resources are directly triggered based on this, false alarms will still occur; therefore, this embodiment completes the screening through a three-level structure of relevant score, coherence index and final decision.

[0158] Specifically, the relevant calculation unit first calculates the cross-correlation coefficient for the theoretical residuals and actual residuals within the same time window. For ease of explanation, a simplified overlapping scoring method can be used. Assuming that there are 3 non-zero positions in the theoretical residuals of a certain window, and the actual residuals are also non-zero in 2 of these positions, the correlation score for that window can be recorded as 2 / 3, or 0.67. If all 3 positions correspond, the score is 1.0; if they do not correspond at all, the score is 0.

[0159] In practice, normalized cross-correlation can also be calculated by combining energy amplitude, allowing both position and amplitude to participate in the scoring; specifically, the theoretical residual data within the current time window is expanded into a length of A one-dimensional energy sequence and set as The corresponding real residual data within the same time window are expanded into one-dimensional energy sequences of the same length and set as... ,set up The index of the sequence element and ,by and They represent the first and second digits in the sequence, respectively. The energy value at each location corresponds to the relevant score for that time window. The calculation formula based on normalized cross-correlation is:

[0160] ;

[0161] in, For summation, The square root symbol is used. It should be noted that when using the aforementioned formula to calculate the relevant score or normalized overlap rate, if the theoretical residual data corresponding to the time window is all zero, that is, the candidate template has no frequency hopping activity in the window itself, the denominator of the calculation formula will be zero.

[0162] For such extreme boundary conditions, the relevant computing units do not perform invalid division operations, but instead use data flow judgment and diversion rules: if both the theoretical residual and the actual residual are zero, then a full score matching is directly given; if the theoretical residual is zero but there is a sudden residual energy in reality, then a negative penalty judgment of deduction is directly given; through this strict structured boundary rule, the system bypasses the computational instability problem caused by mathematical anomalies.

[0163] The coherence assessment unit then performs a moving average of the relevant scores over multiple consecutive time windows to obtain the coherence index; assuming the relevant scores for four consecutive time windows are 0.9, 0.8, 0.2, and 0.9 respectively, the four-window moving average is 0.7.

[0164] If the preset trigger threshold is 0.75, then although there are 3 individual time windows with high scores, the overall continuity is insufficient and it will not be triggered; if the scores of the 4 windows corresponding to another candidate trajectory are 0.8, 0.85, 0.78 and 0.82, then the average value is about 0.81, which can be considered to have good continuity and should be generated to pass the decision.

[0165] The moving average is used instead of a single-window threshold because frequency-hopping targets have structural characteristics across time windows; true targets usually do not match the template in only one isolated time window, but maintain consistency in dwell rhythm, step relationship and energy profile across multiple consecutive windows; by combining the results of multiple windows into a coherence index, false triggering caused by accidental overlap can be significantly reduced.

[0166] The decision unit outputs a pass decision or a suppress decision based on the relationship between the coherence index and the preset trigger threshold. If multiple candidate templates participate in the matching at the same time, the group with the highest coherence index can be selected first, or when multiple groups exceed the threshold, a priority queue is provided for subsequent resource scheduling. In this way, when computing resources and channel resources are limited, the most reliable target can be processed first.

[0167] If the variance of the variation exceeds the preset tolerance, such as 0.95, 0.1, 0.93, or 0.12, it indicates that the observation results lack a continuous structure and are likely due to random pulse superposition coincidences. In this case, even if the average value is close to the boundary, a fluctuation constraint rule can be set to output a suppression decision.

[0168] If a window is missing valid data, for example due to front-end overload or sampling abnormality, the continuity judgment can skip the window or use interpolation of the preceding and following windows, but will not be triggered solely by the interpolated value; if the actual residual is lowly correlated with multiple theoretical residuals, a unified suppression judgment will be output.

[0169] For example, during nighttime patrols in the perimeter liaison area of ​​a port, the system continuously observed several sudden energy blocks; one group partially overlapped with the candidate frequency hopping trajectory in the first, third, and fourth time windows, but was completely missing in the second time window; the other group remained consistent with the candidate trajectory in multiple consecutive time windows.

[0170] After relevant calculations, although the former's score was occasionally high, the coherence index after moving average was below the threshold and was suppressed; the latter's coherence index was consistently above the threshold, so it was judged as a high-confidence target and entered the subsequent narrowband extraction stage.

[0171] The purpose of this mechanism is to improve the judgment criteria from single local similarity to continuous consistency across time, thereby reducing the probability of false alarms caused by random coincidences and sudden environmental noise.

[0172] In a preferred embodiment of the present invention, the channel scheduling module includes: a parameter extraction unit, configured to extract a center frequency parameter and a bandwidth parameter from the actual residual data during the decision generation; a resource allocation unit, connected to the parameter extraction unit, configured to allocate concurrent channelization processing resources based on the center frequency parameter and the bandwidth parameter; and a data extraction unit, connected to the resource allocation unit, configured to output narrowband digital data obtained through channelization processing; wherein the resource allocation unit is further configured to prevent the channelization processing resources from being triggered during the suppression decision generation.

[0173] This embodiment provides a channel scheduling and narrowband extraction mechanism; specifically, the aforementioned decision-making process solves the problem of determining the necessity of target triggering, but if manual frequency selection or full-frequency fine scanning is still used after passing the decision, the resource utilization rate will be calculated to exceed the tolerance.

[0174] Therefore, in this embodiment, after the decision is passed, the center frequency and bandwidth parameters are directly extracted from the actual residual, and concurrent channelization processing resources are scheduled accordingly to achieve narrowband signal extraction.

[0175] Specifically, the parameter extraction unit locates the frequency region where the target energy block is located from the actual residual. Taking the simplified matrix as an example, if the actual residual of a certain time window is [0,0,4,4,0], then it can be considered that the target occupies the 3rd and 4th frequency units consecutively. The center frequency can be taken as the center position of these two units, and the bandwidth corresponds to the span of the two frequency units.

[0176] Specifically, when defining parameters for complex energy regions, the parameter extraction unit does not rely on subjective visual judgment or clustering algorithms that are difficult to analyze. Instead, it adopts structured parameter extraction rules: the center frequency parameter is obtained by weighting the center position of the power of the energy block in the actual residual.

[0177] Specifically, assuming that a total of [number] energy blocks were detected within the energy block... There are 1 active frequency points, and the index of each active frequency point is set as follows: Its corresponding amplitude in the actual residual data is ,in The label of the active frequency point and Then the center frequency parameter The specific formula for extracting the centroid is:

[0178] ;

[0179] in, To determine the summation sign, and after obtaining the centroid peak position, continuously search for the energy decrease to both sides in the frequency domain to reach a predetermined ratio, such as the farthest boundary node where the energy decrease from the center peak reaches 3dB. Map the frequency band distance between the left and right boundary nodes to the bandwidth parameter, thereby accurately anchoring the narrowband extraction range. If the target migrates with the frequency point in multiple adjacent time windows, the parameter extraction unit can generate a set of center frequency and bandwidth parameters for each dwelling window, or extract a scheduling list covering multiple dwelling points.

[0180] Based on these parameters, the resource allocation unit dynamically allocates concurrent channelization processing resources. Concurrent channelization can be understood as splitting the original broadband receiving link into multiple narrowband focusing channels, with each channel locking onto only a smaller frequency band.

[0181] Assuming the system currently supports a maximum of 4 concurrent narrowband extractions, and 3 high-confidence targets appear simultaneously after topology decision, 3 channels can be allocated to the 3 targets respectively, with 1 channel reserved for redundancy; if 6 targets appear simultaneously, but only 4 channels are available, the resource allocation unit can sort them according to the continuity index, prioritize allocating channels to the first 4 targets, and the remaining targets enter the waiting queue or retry in the next cycle.

[0182] The data extraction unit performs channelization processing on the allocated narrowband channels and outputs narrowband digital data; this narrowband data can be directly sent to the subsequent parameter estimation and modulation identification modules; compared with repeatedly performing high-resolution processing over the entire broadband range, narrowband extraction significantly reduces the amount of computation and improves the accuracy of single-target analysis.

[0183] From the perspective of evolutionary logic, although the aforementioned judgment can suppress a large number of invalid targets, without a resource scheduling mechanism in conjunction with it, the system may still use a fixed configuration after the judgment, resulting in channel idleness or congestion. This embodiment transforms the judgment result into resource action in real time through a closed loop of judgment-parameter extraction-allocation-extraction, so that the limited channelization resources are truly used for high-reliability targets.

[0184] If the output is a suppression decision, the resource allocation unit remains in a non-triggered state and does not open a narrowband channel for the candidate target; if the difference between the extracted center frequency and the current broadband boundary is less than the preset protection bandwidth, resulting in the complete bandwidth not falling entirely into the processable range, the coverable part can be extracted first and a boundary truncation mark can be given.

[0185] If the frequency bands of multiple targets overlap, they can be merged into a wider narrowband channel, and then further separated within that channel. If the merged channel exceeds the processing capacity of a single channel, the more reliable one should be retained according to priority. If an allocated channel fails to obtain valid data in several consecutive windows, the channel should be released and reclaimed for use by a new target.

[0186] For example, during a perimeter patrol task at a border crossing, the system continuously identified two high-confidence frequency hopping candidates within a certain time period, one located near the inspection channel and the other located at the edge of the storage area;

[0187] The parameter extraction unit, based on the above quantization extraction formula, derives from the actual residual that the bandwidth parameter of the former is less than the preset narrowband monitoring tolerance, while the center frequency of the latter deviates from the current dwell frequency and its bandwidth parameter is greater than the preset broadband judgment threshold. The preset tolerance and threshold are calculated by the device's current channelization processing capability and the standard bandwidth of the target signal. The resource allocation unit issues two channelization configurations accordingly, and the data extraction unit outputs two narrowband digital data in real time for subsequent identification. For several other energy blocks that are only locally similar but have not passed the judgment, no narrowband channel is allocated.

[0188] The purpose of this mechanism is to quickly convert the structural decision results into executable narrowband extraction actions, thereby achieving the technical effects of reducing invalid full-frequency processing, improving concurrent resource utilization efficiency, and shortening target response latency.

[0189] In a preferred embodiment of the present invention, the identification module includes: a parameter estimation unit for estimating symbol rate, carrier frequency, and bandwidth of the narrowband digital data; a feature matching unit connected to the parameter estimation unit for matching the estimation results with a preset feature library; and a modulation identification unit connected to the feature matching unit for outputting modulation identification results based on the matching results; wherein the preset feature library includes at least one of a modulation scheme feature template, a symbol rate interval template, a carrier frequency offset template, and a bandwidth template.

[0190] This embodiment provides a narrowband parameter estimation and modulation identification mechanism. Specifically, after channel scheduling, the system has obtained narrowband digital data focused on the target frequency band. However, if it only stays at the level of discovering a high-confidence frequency hopping activity, it still cannot support subsequent monitoring and judgment.

[0191] Especially in port scenarios, it is necessary to further distinguish between normal operating equipment, temporary legitimate business, and abnormal networks that require special attention; therefore, this embodiment introduces a three-level identification process of parameter estimation, feature matching, and modulation recognition.

[0192] Specifically, the parameter estimation unit estimates the symbol rate, carrier frequency, and bandwidth of narrowband digital data. To illustrate with a simplified example, if a narrowband data stream exhibits approximately periodic symbol boundaries in time, with a significant zero-crossing or phase flip every 10 sampling points, then the ratio of its symbol rate to sampling rate can be estimated to be 1:10.

[0193] If the center of its main lobe deviates from the desired center frequency by 1 frequency unit, it can be considered as having a fixed carrier frequency offset; if the main lobe occupies about 2 frequency units, the current bandwidth can be estimated to approximately cover 2 units.

[0194] The feature matching unit compares these estimation results with a preset feature library; the feature library can contain typical templates under different modulation schemes. For example, a certain type of frequency shift keying signal often corresponds to a relatively stable double-peak spectrum or a specific frequency offset relationship, while a certain type of PSK signal has different phase shift statistics.

[0195] To avoid over-reliance on complex formulas in the implementation method, an interval matching approach can be adopted here: if the symbol rate of a certain data falls within the range of 40ksps to 60ksps, the bandwidth falls within the narrowband template range, and the carrier frequency offset mode conforms to the frequency shift keying template, then its matching degree with the frequency shift keying class template is relatively high; if another set of results is closer to the PSK template, then the corresponding candidate category is output.

[0196] The modulation recognition unit outputs the modulation recognition result based on the matching result; it can adopt the highest matching strategy, or output the top two candidates and their confidence levels; for example, if the matching score of a certain narrowband data with the quaternary frequency shift keying template is 0.82 and the matching score with the BPSK template is 0.45, then the recognition result output is quaternary frequency shift keying.

[0197] If the highest score is only slightly higher than the second highest score, for example, 0.58 to 0.55, then the category to be confirmed can be output and handed over to the feedback update module for conservative processing;

[0198] Compared to performing global identification directly in broadband data, this embodiment first uses topological filtering to lock in high-confidence targets, and then performs fine estimation and matching on narrowband data. This hierarchical structure of coarse screening followed by fine identification reduces invalid identification operations and improves the stability of parameter estimation.

[0199] If the duration of narrowband data is too short, causing unstable symbol rate estimation, the parameter estimation unit can output only the carrier frequency and bandwidth estimates and record the symbol rate as undetermined; if the estimation result does not match multiple templates, the modulation recognition unit outputs an unknown category and sends the result to the subsequent feedback loop as a possible new sample.

[0200] If there is a large carrier frequency drift, causing feature matching to deviate, offset compensation can be performed before matching; if it is still unstable after compensation, the weight of the recognition result in the update process should be reduced.

[0201] For example, during nighttime patrols at border ports, the system analyzes narrowband data extracted from a channel that passes through the port. The bandwidth parameter is less than the preset bandwidth, the symbol rate is higher than the preset rate threshold, and the spectrum shape is closer to the frequency shift keying template.

[0202] After feature matching, the matching degree between this data and the narrowband frequency shift keying template is significantly higher than that of the conventional template for intercom, and the modulation identification unit outputs the corresponding category accordingly; the other narrowband data, due to its short duration, only estimates the bandwidth and carrier frequency, and fails to provide a stable symbol rate, so the system marks it as pending confirmation;

[0203] The purpose of this mechanism is to extract key communication parameters that can be assessed under narrowband focusing conditions and match them with a preset system template, thereby improving the accuracy of target classification and providing a basis for subsequent environmental updates and disposal.

[0204] In a preferred embodiment of the present invention, the feedback update module is further configured to: update the steady-state time-frequency reference data when the identification result meets the preset steady-state conditions; update the simulation excitation parameters when the identification result meets the preset change conditions; and reduce the preset trigger threshold when the number of consecutive suppression decisions in the decision result reaches a preset number, and the reduced threshold is not lower than the lower limit of the noise confidence level calculated based on the current background energy data of the system.

[0205] While the decision result remains a pass decision, the currently allocated channelization processing resources and corresponding narrowband extraction parameters are maintained.

[0206] The preset steady-state condition is that the identification results are consistent within a preset number of consecutive time windows and the variance of the actual residual data is less than a preset variance threshold. The preset change condition is that the identification results are inconsistent with the current steady-state time-frequency reference data within a preset number of consecutive time windows and the coherence index is greater than a preset update threshold.

[0207] This embodiment provides a feedback update mechanism; specifically, in the aforementioned process, the system has been able to complete the closed loop from broadband observation, residual matching to narrowband identification, but if the parameters and benchmarks are fixed for a long time, the system will gradually deviate from the real environment.

[0208] For example, the day and night operation modes of ports are different, the start and stop of perimeter equipment changes frequently, and the objects of attention may change during special tasks; if there is no updating capability, the steady-state background will become outdated, and the theoretical excitation will also lose focus; therefore, this embodiment introduces a dynamic updating mechanism based on the identification results and the decision results;

[0209] Specifically, the first type of update is the update of steady-state time-frequency reference data; when the identification result meets the preset steady-state conditions, the system will gradually absorb the corresponding observations into the steady-state background; the steady-state conditions here not only require that the identification results are consistent within multiple consecutive time windows, but also require that the variance of the actual residual data is less than the preset variance threshold.

[0210] To illustrate with a simplified example, suppose that in three consecutive time windows, the identification results corresponding to a certain frequency point are all of the same type of fixed service, and the actual residual amplitudes of these three windows are 0.2, 0.1, and 0.2 respectively. Then the variance is very small, and it can be considered that the signal has changed from abnormal to normal. Therefore, it is incorporated into the steady-state time-frequency reference. This can prevent the system from mistakenly treating normal operating equipment as abnormal objects for a long time.

[0211] The second type of update is the update of simulation excitation parameters. When the identification result meets the preset change conditions, it indicates that a structural activity that is inconsistent with the current steady-state reference but has a high degree of coherence has appeared in reality. At this time, the candidate modulation parameters, symbol rate parameters or frequency change parameters can be updated accordingly.

[0212] For example, if the same type of novel narrowband frequency shift keying activity is identified in three consecutive time windows, and its consistency index is always higher than the update threshold, but there is no corresponding dwell time and step pattern in the current excitation library, then a new set of excitation parameters can be added for subsequent theoretical residual generation.

[0213] The third type of adjustment is the adaptive adjustment of the trigger threshold; if the number of consecutive suppression decisions reaches a preset number, the trigger threshold can be appropriately reduced; this is done to solve the problem of insufficient triggering in some low signal-to-noise ratio scenarios.

[0214] To illustrate with a simplified example, the current trigger threshold is 0.80. The five most recent consistency indices are 0.72, 0.74, 0.70, 0.73, and 0.71, respectively, none of which triggered the target, but the system exhibits a structural characteristic of consistently approaching the threshold. The system can lower the threshold to 0.75 after reaching a preset number of suppression attempts to improve sensitivity to weak targets. Conversely, if the decision is consistently passed, it indicates that the current target state is stable, and the system can maintain the currently allocated channelization processing resources and corresponding narrowband extraction parameters to avoid jitter caused by frequent reconfiguration.

[0215] From an evolutionary perspective, without this feedback loop, although the preceding modules can complete independent tests one after another, the entire system cannot self-correct as the environment changes.

[0216] To avoid baseline shifts or threshold distortions caused by environmental changes, this embodiment uses three actions—background update, stimulus update, and threshold adjustment—to create a continuously evolving closed loop in the system.

[0217] If the identification results are consistent, but the actual residual variance is higher than the threshold, it indicates that the energy fluctuation is still large and it is not appropriate to directly incorporate it into the steady-state benchmark. In this case, we should continue to observe without updating. If the identification results are inconsistent with the current steady-state benchmark, but the consistency index has not reached the update threshold, it indicates that the change is still unstable and the excitation parameters should not be updated.

[0218] If the trigger threshold is lowered after the number of consecutive suppressions reaches the threshold, but there are still no valid decisions for a long time afterward, a lower threshold can be set to prevent the threshold from being lowered indefinitely, which would lead to an increase in false alarms. If decisions are made continuously but the quality of narrowband data deteriorates significantly, such as a sudden signal interruption or severe distortion, the channel should be reclaimed after the release conditions are met, even if the resources are kept for a period of time for verification.

[0219] For example, during a complete day-night monitoring cycle at a border port, the warehouse control equipment commonly used during the day appears continuously on multiple frequency points, and the identification results are stable and consistent with small fluctuations in the actual residuals. The system gradually incorporates them into the steady-state time-frequency reference.

[0220] A new type of short-stay frequency hopping activity appeared in the perimeter area at night. Multiple consecutive time windows were identified as the same type of narrowband frequency shift keying, and the topological coherence was high. Therefore, the system updated its dwell time, step pattern and bandwidth characteristics into the simulation excitation parameter library.

[0221] Meanwhile, some weak targets remain in a state close to but not yet reaching the trigger threshold for a long time. After suppressing them for several consecutive times, the system slightly lowers the threshold to improve the ability to capture low-intensity activities. For targets that have been continuously passed the decision, the current narrowband channel is maintained to ensure that the tracking is not interrupted.

[0222] The purpose of this mechanism is to enable the system to adaptively correct the background model, candidate templates, and decision thresholds based on long-term observation results, thereby achieving the technical effects of enhancing environmental adaptability, reducing long-term false alarms and false negatives, and stabilizing resource scheduling behavior.

[0223] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks, characterized in that, The system includes: The data acquisition module includes a broadband radio frequency front-end and a global clock unit, used to acquire broadband digital sampling data within a preset monitoring frequency band, and generate a time-frequency matrix based on the broadband digital sampling data; the time-frequency matrix is ​​a two-dimensional energy distribution matrix containing time series and frequency units; The reference reconstruction module is communicatively connected to the data acquisition module and is used to construct steady-state time-frequency reference data based on the broadband digital sampling data and the time-frequency matrix. The parameter generation module is used to generate simulation excitation parameters based on preset modulation type parameters, symbol rate parameters, and frequency change parameters. The theoretical residual generation module is communicatively connected to the parameter generation module and is used to generate theoretical residual data relative to the steady-state time-frequency reference data based on the steady-state time-frequency reference data and the simulation excitation parameters. The real residual generation module is used to generate real residual data based on the difference relationship between the real-time time-frequency data contained in the time-frequency matrix and the steady-state time-frequency reference data; The time-frequency topology matching module is used to calculate the time-frequency structural similarity between the theoretical residual data and the actual residual data, and generate a judgment result containing a matching degree index based on the calculation result. The channel scheduling module is used to allocate channelization processing resources to generate narrowband data when the matching degree index is greater than or equal to a preset trigger threshold, and to keep the channelization processing resources from being triggered when the matching degree index is less than the preset trigger threshold. The identification module is used to perform parameter estimation and modulation identification on the narrowband data and output the identification results; The feedback update module is used to update the steady-state time-frequency reference data and / or the simulation excitation parameters according to the decision result and the identification result and a preset update rule.

2. The multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring according to claim 1, characterized in that, The data acquisition module includes: A broadband receiving unit is used to acquire analog radio frequency signals within a preset monitoring frequency band; An analog-to-digital converter unit, connected to the broadband receiving unit, is used to convert the analog radio frequency signal into the broadband digital sampling data; The time-frequency conversion unit, connected to the analog-to-digital conversion unit, is used to perform time-frequency conversion on the broadband digital sampling data and generate the time-frequency matrix.

3. A multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring according to claim 2, characterized in that, The benchmark reconstruction module includes: Envelope tracking unit is used to extract the envelope data of the broadband digital sampling data after smoothing by a preset time window; An energy cancellation unit, connected to the envelope tracking unit, is used to subtract the moving average value of the envelope data to eliminate transient energy components and generate background energy data. A reference modeling unit, connected to the energy cancellation unit, is used to generate the steady-state time-frequency reference data based on the background energy data and the time-frequency matrix.

4. A multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring according to claim 3, characterized in that, The parameter generation module includes: The modulation parameter storage unit is used to store preset modulation type parameters and symbol rate parameters; The frequency variation parameter storage unit is used to store preset dwell time parameters, frequency step parameters, and bandwidth parameters; An excitation construction unit, connected to the modulation parameter storage unit and the frequency variation parameter storage unit, is used to generate the simulation excitation parameters based on the modulation type parameter, the symbol rate parameter, the dwell time parameter, the frequency step parameter, and the bandwidth parameter. The preset parameters are determined by at least one of the following: statistical results of historical monitoring samples, standard signal system parameter library, or manual configuration.

5. A multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring according to claim 4, characterized in that, The theoretical residual generation module is also used for: Based on the simulation excitation parameters, the steady-state time-frequency reference data is superimposed or mapped to generate simulated time-frequency data. The simulated time-frequency data and the steady-state time-frequency reference data are differentially processed to generate the theoretical residual data; The theoretical residual data is used to characterize the time-frequency structure features corresponding to the frequency variation pattern defined by the residence time parameter, frequency step parameter, and bandwidth parameter.

6. A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks according to claim 5, characterized in that, The real residual generation module is also used for: The real-time time-frequency data contained in the time-frequency matrix is ​​differentially processed with the steady-state time-frequency reference data to generate the actual residual data; The real residual data is used to characterize the transient energy distribution features in a real-time wireless communication environment.

7. A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks according to claim 6, characterized in that, The time-frequency topology matching module includes: The relevant calculation unit is used to generate a relevant score by calculating the cross-correlation coefficient between the theoretical residual data and the actual residual data; A coherence judgment unit, connected to the relevant calculation unit, is used to perform a moving average processing on the relevant scores within multiple consecutive time windows to generate a coherence index as the matching degree index. The decision unit, connected to the continuity judgment unit, is used to generate a pass decision when the continuity index is greater than or equal to a preset trigger threshold, and to generate a suppress decision when the continuity index is less than the preset trigger threshold.

8. A multi-band adaptive frequency hopping reconnaissance signal processing system for wireless communication network monitoring according to claim 7, characterized in that, The channel scheduling module includes: A parameter extraction unit is used to extract the center frequency parameter and bandwidth parameter from the actual residual data during the decision generation process. A resource allocation unit, connected to the parameter extraction unit, is used to allocate concurrent channelization processing resources based on the center frequency parameter and the bandwidth parameter; A data extraction unit, connected to the resource allocation unit, is used to output narrowband digital data obtained through channelization processing; The resource allocation unit is also used to prevent the channelization processing resources from being triggered when the suppression decision is generated.

9. A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks according to claim 8, characterized in that, The identification module includes: The parameter estimation unit is used to perform symbol rate estimation, carrier frequency estimation, and bandwidth estimation on the narrowband digital data. A feature matching unit, connected to the parameter estimation unit, is used to match the estimation results with a preset feature library; A modulation recognition unit, connected to the feature matching unit, is used to output a modulation recognition result based on the matching result; The preset feature library includes at least one of modulation scheme feature template, symbol rate range template, carrier frequency offset template, and bandwidth template.

10. A multi-band adaptive frequency hopping reconnaissance signal processing system for monitoring wireless communication networks according to claim 9, characterized in that, The feedback update module is also used for: When the identification result meets the preset steady-state conditions, the steady-state time-frequency reference data is updated; When the recognition result meets the preset change conditions, the simulation excitation parameters are updated; When the number of consecutive suppression decisions reaches a preset number, the preset trigger threshold is reduced, and the reduced threshold is not lower than the lower limit of the noise confidence level calculated based on the current background energy data of the system. While the decision result remains a pass decision, the currently allocated channelization processing resources and corresponding narrowband extraction parameters are maintained. The preset steady-state condition is that the identification results are consistent within a preset number of consecutive time windows and the variance of the actual residual data is less than a preset variance threshold. The preset change condition is that the identification results are inconsistent with the current steady-state time-frequency reference data within a preset number of consecutive time windows and the coherence index is greater than a preset update threshold.

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