A time-frequency domain joint continuous and burst signal detection method and system

Abstract

The application provides a time-frequency domain combined continuous and burst signal detection method. For a piece of signal sampling data generated through collection and preprocessing, frequency domain detection and time domain detection are respectively performed on the signal sampling data in the frequency domain and the time domain, the detection of continuous and burst signals is completed, and time-frequency parameters of the signal are obtained. In the frequency domain, spectrum analysis is performed to determine the existence of signals in the bandwidth. If the signals exist, the actual bandwidth of the signals is determined after the time domain detection is completed. In the time domain, the existing signals are detected as burst signals, and the starting time and signal duration parameters of the burst signals are obtained. The application automatically detects the existing signals and time-frequency parameters by using the time-frequency domain combined detection method, without manual observation and burst signal frame selection to determine the signal parameters. The design of the monitoring equipment is simplified, the manual operation is reduced, the reliability of the engineering implementation is improved, the burst signal detection cost is low, the speed is fast, the detection precision is high, and the stability is high.

Description

Technical Field

[0001] This invention relates to the field of digital signal processing, and in particular to a time-frequency domain joint method and system for detecting continuous and burst signals. Background Technology

[0002] The broadband electromagnetic spectrum monitoring equipment uses an antenna to capture spatial electromagnetic radiation signals and convert them into electrical signals. The signals are then analog-converted, filtered, and amplified via a radio frequency channel before being output to the acquisition module. The acquisition module performs AD sampling and digital down-conversion on the single-channel analog signal output from the receiving channel to generate signal sampling data. The composition diagram of the broadband electromagnetic spectrum monitoring equipment is shown below. Figure 1 As shown. In complex electromagnetic environments, electromagnetic signal monitoring equipment captures electromagnetic signals, which need to be filtered out one by one based on the time and frequency occupancy ranges of various specific signals for subsequent analysis and processing. In recent years, the continuous development of wireless communication technology (power control, access methods, etc.) and the large-scale construction of wireless communication equipment have made the electromagnetic environment increasingly complex, which has increased the difficulty of electromagnetic signal identification and reduced the accuracy of electromagnetic signal detection.

[0003] Traditional signal detection typically uses a fixed threshold to determine the signal duration or directly employs single or double sliding windows to analyze signal abrupt changes and identify sudden signals. This method is susceptible to noise and cannot distinguish between continuous signals and continuous background noise, leading to false alarms from sudden signals. These issues reduce the capability and efficiency of signal detection. Summary of the Invention

[0004] To address the shortcomings of broadband electromagnetic spectrum monitoring equipment, such as low detection probability of sudden electromagnetic radiation signals and low accuracy of time parameter extraction, a time-frequency domain joint continuous and sudden signal detection method and system is provided. This method can be applied to broadband electromagnetic spectrum monitoring equipment to achieve high-precision and high-stability detection of sudden signals.

[0005] The technical solution adopted in this invention is as follows: a time-frequency domain joint method for detecting continuous and burst signals. For a segment of signal sampling data generated after acquisition and preprocessing, frequency domain detection and time domain detection are performed on the signal sampling data in the frequency domain and time domain respectively to complete the detection and discovery of continuous and burst signals and obtain the time-frequency parameters of the signal. In the frequency domain, spectrum analysis is performed to determine the existence of the signal within the bandwidth. If the signal exists, the actual bandwidth of the signal is further determined after the time domain detection is completed. In the time domain, burst signal detection is performed on the existing signal, and the start time and duration parameters of the burst signal are obtained.

[0006] Furthermore, the frequency domain detection includes spectral energy detection and signal bandwidth detection; spectral energy detection analyzes the signal-to-noise ratio (SNR) of in-band and out-of-band energy in the signal spectrum, and determines whether a signal exists in the input signal sampling data based on the SNR; signal bandwidth detection analyzes the trend of in-band spectral changes to accurately estimate the signal bandwidth.

[0007] Furthermore, the time-domain detection includes dual sliding window detection, signal energy detection, and signal duration detection. Dual sliding window detection accumulates time-domain signal energy through sliding windows and preliminarily estimates the start and end time series of burst signals by comparing the ratio of the two sliding windows with a preset signal-to-noise ratio threshold. Signal energy detection is used to judge the signal-to-noise ratio of burst signal segments and filter out false alarm signals. Signal duration detection is used to filter out burst signals with excessively short durations by using a preset minimum burst signal length as a threshold, thereby obtaining the duration of the remaining burst signals after filtering.

[0008] Furthermore, the specific method for detecting the spectral energy is as follows:

[0009] The discrete Fourier transform of the input signal sample data x(n) yields the entire signal spectrum X. s Based on the input sampling signal bandwidth Bs, calculate and determine the spectrum center X. s The average energy over the bandwidth is obtained. Average energy With preset energy threshold Th e In comparison, if the average energy Greater than or equal to the preset energy threshold Th e If the signal is positive, it indicates that there is a signal in the sampled data of that segment, and time-domain detection is performed on it.

[0010] Furthermore, the specific method for detecting the signal bandwidth is as follows:

[0011] After time-domain detection is completed, the discrete Fourier transform of each signal segment is performed separately to obtain the spectrum X. n The smoothed spectrum X′ is obtained after processing with a smoothing filter. n Search spectrum X′ n All peak values ​​within the bandwidth range Bs {Peak i} and the corresponding position {P i}, for P k P k ∈{P i}, search for the spectral energy value X′ to the left and right. l 、X′ r First time below C×Peak k Points Pl and Pr are stored in the sequence {Pl}. j} and {Pr jIn the above, C is the set threshold; if no energy value is lower than C×Peak during the search process... k The point, or the point where energy is higher than Peak. k If a point is not found, skip the current search; after all peak searches are completed, the corresponding signal bandwidth B = max{Pr} is obtained. j}-min{Pl j}

[0012] Furthermore, the specific method for the dual sliding window detection is as follows:

[0013] Input signal sampling data that shows a signal after spectral energy detection, and calculate the energy within the sliding windows on both the left and right sides point by point. And calculate the ratio r between the two. n And using a preset signal-to-noise ratio threshold Th, r is filtered out n The signal passing through the ±Th threshold region is divided into ratios r within each positive / negative threshold region. n The points corresponding to the maximum / minimum values, in their alternating order of appearance in the time domain, are stored as the start / end sequence {P} of the burst signal. s1 P e1 P s2 P e2 , ..., P si P ei}, where P si P ei These represent the start and end points of the i-th burst signal segment, respectively.

[0014] Furthermore, the specific method for detecting the signal energy is as follows:

[0015] Calculate the start / end sequence of the burst signal {P} s1 P e1 P s2 P e2 , ..., P si P ei The average energy of each burst signal segment and the residual noise segment L in} n =L-∑ n (P en -P sn The signal-to-noise ratio of the average energy of ()-n is calculated, and it is determined whether it exceeds the preset signal-to-noise ratio threshold Th. If it does not exceed the threshold, it indicates that the burst signal segment is a false alarm signal caused by noise, and filtering is performed. The signal duration of the burst signal sequence after filtering is detected, where L is the length of the signal sampling data.

[0016] Furthermore, the specific method for detecting the signal duration is as follows:

[0017] According to the preset signal length threshold Th t, with an interval smaller than Th t The burst signal segments are spliced ​​together to form a single burst signal. The duration of all the resulting burst signals is calculated, and the output duration is greater than or equal to Th. t The sudden signal and its corresponding duration parameter, if there is no burst signal with a duration greater than or equal to Th t If a sudden signal is detected, the signal segment is considered as a continuous signal, and time-domain detection is completed.

[0018] This invention also proposes a time-frequency domain joint continuous and burst signal detection system, comprising:

[0019] A receiving antenna is used to capture electromagnetic radiation signals in space and convert them into electrical signals;

[0020] The radio frequency channel is used to perform analog frequency conversion, filtering, and amplification of electrical signals, and then output them to the acquisition module.

[0021] The acquisition module performs AD sampling and digital down-conversion on the received analog signal, generates one-end signal sampling data, and outputs it to the detection module;

[0022] The detection module includes a frequency domain detection module and a time domain detection module, which are used to detect the signal sampling data in the frequency domain and time domain respectively, to complete the detection and discovery of continuous and burst signals and to acquire the time and frequency parameters of the signal;

[0023] The frequency domain detection module performs spectrum analysis in the frequency domain to determine the existence of a signal within the bandwidth. If the signal exists, the actual bandwidth of the signal will be further determined after the time domain detection is completed. The time domain detection module performs burst signal detection on the existing signal in the time domain and obtains the start time and duration parameters of the burst signal.

[0024] Compared with existing technologies, the beneficial effects of adopting the above technical solution are as follows:

[0025] (1) Simplify the operation of monitoring equipment.

[0026] This invention addresses the automated detection of sampled signal data for burst electromagnetic radiation signals in broadband electromagnetic spectrum monitoring equipment. It utilizes a time-frequency domain joint detection method to automatically detect existing signals and time-frequency parameters, eliminating the need for manual observation and selection of burst signals to determine signal parameters. This simplifies the design of monitoring equipment, reduces manual operation, and improves the reliability of engineering implementation.

[0027] (2) The detection of sudden signals is low-cost and fast.

[0028] The detection method proposed in this invention requires only CPU computation, eliminating the need for high-performance GPUs or dedicated AI processing chips for training or model loading. The algorithm optimizes the number of detection iterations, enabling rapid processing and computation of signal sampling data, and outputting parameters such as the start time and duration of burst signals, significantly improving the detection speed of broadband spectrum monitoring equipment for burst electromagnetic radiation signals.

[0029] (3) High detection accuracy and high stability.

[0030] This invention addresses the automated detection of sudden electromagnetic radiation signals by broadband electromagnetic spectrum monitoring equipment. It employs a joint time-frequency domain detection method, which determines the presence of a signal through frequency domain detection and confirms the presence of a signal in the data sent for time domain detection, thereby improving detection stability. The time domain detection part determines the signal through sliding window energy ratio, sudden signal energy average, and sudden signal duration, and detects the sudden signal bandwidth in the frequency domain, achieving high-precision detection of sudden signals. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of a broadband electromagnetic spectrum detection device.

[0032] Figure 2 This is a schematic diagram illustrating the application of the time-frequency domain joint continuous and burst signal detection method proposed in this invention in a broadband electromagnetic spectrum detection device.

[0033] Figure 3 This is a time-domain diagram of signal detection in one embodiment of the present invention.

[0034] Figure 4 This is a flowchart of frequency domain detection in one embodiment of the present invention.

[0035] Figure 5 This is a flowchart of time-domain detection in one embodiment of the present invention. Detailed Implementation

[0036] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0037] Example 1

[0038] In existing broadband electromagnetic spectrum monitoring equipment, the duration of the sampled signal is typically determined by a fixed threshold, or single or double sliding window methods are used to analyze signal abrupt changes and find burst signals. However, this method is susceptible to noise and cannot distinguish between continuous signals and continuous noise floor, leading to false alarms for burst signals and reducing signal detection capability and efficiency. Therefore, this embodiment proposes a time-frequency domain joint method for detecting continuous and burst signals, applicable to broadband electromagnetic spectrum monitoring equipment as well as other devices. This embodiment uses a broadband electromagnetic spectrum monitoring equipment as an example for illustration, and the specific scheme is as follows:

[0039] like Figure 2 , Figure 3 As shown, a time-frequency domain joint method for detecting continuous and burst signals involves using a broadband electromagnetic spectrum monitoring device to capture spatial electromagnetic radiation signals with an antenna and converting them into electrical signals. The signals are then subjected to analog frequency conversion, filtering, and amplification via a radio frequency channel before being output to a data acquisition module. The acquisition module performs AD sampling and digital down-conversion on the single-channel analog signal output from the receiving channel to generate a signal sampling data segment. Frequency domain detection and time domain detection are performed on the signal sampling data in both the frequency and time domains to detect continuous and burst signals and acquire their time-frequency parameters. Specifically, in the frequency domain, spectrum analysis is performed to determine the existence of signals within the bandwidth; if present, the actual bandwidth of the signal is further determined after time domain detection. In the time domain, burst signal detection is performed on the existing signals to acquire the start time and duration parameters of the burst signals.

[0040] In this embodiment, as Figure 4 As shown, frequency domain detection includes spectral energy detection and signal bandwidth detection. Spectral energy detection analyzes the signal-to-noise ratio (SNR) of the signal spectrum inside and outside the band, and determines whether a signal exists in the input signal sampling data based on the SNR. Signal bandwidth detection analyzes the trend of in-band spectrum changes to accurately estimate the signal bandwidth.

[0041] Specifically, the specific method for spectral energy detection is as follows:

[0042] The discrete Fourier transform of the input signal sample data x(n) with a sampling rate of Fs and a sampling bandwidth of Bs yields the entire signal spectrum X. s Based on the input sampling signal bandwidth Bs, determine the spectrum center X. s The average energy over the bandwidth is obtained. Average energy With preset energy threshold Th e In comparison, if the average energy Greater than or equal to the preset energy threshold Th eIf the signal is detected, it indicates that a signal exists in the sampled data. After confirming the presence of a signal in the sampled data, time-domain detection is performed, followed by signal bandwidth detection.

[0043] The specific method for signal bandwidth detection is as follows:

[0044] After time-domain detection is completed, perform discrete Fourier transform on each segment of the signal separately. Obtain the spectrum X of the entire signal n The smoothed spectrum X′ is obtained after processing with a smoothing filter. n Search spectrum X′ n All peak values ​​within the bandwidth range Bs {Peak i} and the corresponding position {P i}, for P k P k ∈{P i}, search for the spectral energy value X′ to the left and right. l 、X′ r First time below C×Peak k Points Pl and Pr are stored in the sequence {Pl}. j} and {Pr j In the above, C is the set threshold; if no energy value is lower than C×Peak during the search process... k The point, or the point where energy is higher than Peak. k If the point is not found, skip the current search; after all peak searches are completed, the corresponding signal bandwidth B = max{Pr} is obtained. j}-min{Pl j}, that is, the sequence {Pr} j The maximum value in} minus the sequence {Pl j Minimum value in}

[0045] In this embodiment, as Figure 5 As shown, the time-domain detection includes dual sliding window detection, signal energy detection, and signal duration detection. Dual sliding window detection accumulates time-domain signal energy through sliding windows and preliminarily estimates the start and end time series of burst signals by using the ratio of the dual sliding windows and a preset signal-to-noise ratio threshold. Signal energy detection is used to judge the signal-to-noise ratio of burst signal segments and filter out false alarm signals. Signal duration detection is used to filter out burst signals with excessively short durations by using a preset minimum burst signal length as a threshold, thus obtaining the duration of the remaining burst signals after filtering.

[0046] Specifically, the method for dual sliding window detection is as follows:

[0047] Input signal sample data x(n) that shows a signal after spectral energy detection, with a sampling rate of Fs and a data length of L. Set the sliding window length to L. wCalculate the energy within the sliding windows on both the left and right sides point by point. And calculate the ratio of the two. Using a preset signal-to-noise ratio threshold Th, r is filtered out n The signal passing through the ±Th threshold is divided into r values ​​within each positive / negative threshold region. n The points corresponding to the maximum / minimum values, in their alternating order of appearance in the time domain, are stored as the start / end sequence {P} of the burst signal. s1 P e1 P s2 P e2 , ..., P si P ei}, where P si P ein These represent the start and end points of the i-th burst signal segment, respectively; if multiple consecutive positive threshold regions appear in the time domain, the r value within the last positive threshold region is taken. n The maximum value corresponds to point P. si Store the sequence; if multiple consecutive regions exceeding the negative threshold appear in the time domain, take r from the first negative threshold region. n The minimum value corresponds to point P. ei Store the sequence.

[0048] After determining the burst signal sequence, it is necessary to remove false alarms caused by noise. Therefore, signal energy detection is performed on the burst signal sequence. The specific method is as follows:

[0049] Calculate the average energy of each burst signal segment in the sequence. With the remaining noise segment L n =L-∑ n (P en -P sn The average energy E of )-n noise =(∑|x(n)|-∑E i ) / L n Signal-to-noise ratio (SNR) i =20lg(E i / E noise The system checks whether the signal-to-noise ratio (SNR) exceeds a preset threshold Th. If it does not, the burst signal segment is considered a false alarm signal caused by noise, and filtering is performed. The filtered burst signal sequence {P′} is then processed. s1 , P′ e1 , P′ s2 , P′ e2 , ..., P′ sj , P′ ej}, to perform signal duration detection.

[0050] Specifically, the method for signal duration detection is as follows:

[0051] According to the preset signal length threshold Th t , with an interval smaller than Th t The burst signal segments are spliced ​​together to form a single burst signal, Th t The minimum resolution for subsequent signal processing is determined. If the interval is less than this value, the subsequent processing cannot distinguish between the two signals, so they can be directly spliced.

[0052] Calculate the duration t of all burst signals obtained after splicing. j =(P′) ej -P′ sj +1) / Fs, output duration t j ≥Th t The sudden signal and its corresponding duration parameter. If there is no signal satisfying t j ≥Th t If a sudden signal is detected, the signal segment is considered as a continuous signal, thus completing the time-domain detection.

[0053] Example 2

[0054] This embodiment also proposes a system for a time-frequency domain joint continuous and burst signal detection method based on Example 1, including:

[0055] A receiving antenna is used to capture electromagnetic radiation signals in space and convert them into electrical signals;

[0056] The radio frequency channel is used to perform analog frequency conversion, filtering, and amplification of electrical signals, and then output them to the acquisition module.

[0057] The acquisition module performs AD sampling and digital down-conversion on the received analog signal, generates one-end signal sampling data, and outputs it to the detection module;

[0058] The detection module includes a frequency domain detection module and a time domain detection module, which are used to detect the signal sampling data in the frequency domain and time domain respectively, to complete the detection and discovery of continuous and burst signals and to acquire the time and frequency parameters of the signal;

[0059] The frequency domain detection module performs spectrum analysis in the frequency domain to determine the existence of a signal within the bandwidth. If the signal exists, the actual bandwidth of the signal will be further determined after the time domain detection is completed. The time domain detection module performs burst signal detection on the existing signal in the time domain and obtains the start time and duration parameters of the burst signal.

[0060] In this embodiment, the frequency domain detection module includes a spectrum energy detection module and a signal bandwidth detection module. The spectrum energy detection module analyzes the signal-to-noise ratio (SNR) of the in-band and out-of-band energy in the signal spectrum and determines whether a signal exists in the input signal sampling data based on the SNR. The signal bandwidth detection module analyzes the in-band spectrum variation trend and accurately estimates the signal bandwidth.

[0061] The time-domain detection module includes a dual sliding window detection module, a signal energy detection module, and a signal duration detection module. The dual sliding window detection module accumulates time-domain signal energy through sliding windows and preliminarily estimates the start and end time series of burst signals by comparing the ratio of the two sliding windows with a preset signal-to-noise ratio threshold. The signal energy detection module is used to judge the signal-to-noise ratio of burst signal segments and filter out false alarm signals. The signal duration detection module is used to filter out burst signals with excessively short durations by using a preset minimum burst signal length as a threshold, thus obtaining the duration of the remaining burst signals after filtering.

[0062] It should be noted that, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances. The accompanying drawings in the embodiments are used to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0063] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A time-frequency domain joint continuous and burst signal detection method, characterized in that, For a segment of signal sampling data generated after acquisition and preprocessing, frequency domain detection and time domain detection are performed on the signal sampling data in the frequency domain and time domain respectively to complete the detection and discovery of continuous and burst signals and to obtain the time and frequency parameters of the signal. Specifically, in the frequency domain, spectrum analysis is performed to determine the existence of the signal within the bandwidth. If the signal exists, the actual bandwidth of the signal is further determined after the time domain detection is completed. In the time domain, burst signal detection is performed on the existing signal, and the start time and duration parameters of the burst signal are obtained. The frequency domain detection includes spectrum energy detection and signal bandwidth detection. Spectrum energy detection analyzes the signal-to-noise ratio (SNR) of the in-band and out-of-band energy in the signal spectrum and determines whether a signal exists in the input signal sampling data based on the SNR. Signal bandwidth detection analyzes the in-band spectrum variation trend to accurately estimate the signal bandwidth. The time-domain detection includes dual sliding window detection, signal energy detection, and signal duration detection. Dual sliding window detection accumulates time-domain signal energy through sliding windows and preliminarily estimates the start and end time series of burst signals by comparing the ratio of the two sliding windows with a preset signal-to-noise ratio threshold. Signal energy detection is used to judge the signal-to-noise ratio of burst signal segments and filter out false alarm signals. Signal duration detection is used to filter out burst signals with excessively short durations by using a preset minimum burst signal length as a threshold, thus obtaining the duration of the remaining burst signals.

2. The time-frequency domain joint continuous and burst signal detection method of claim 1, wherein, The specific method for detecting the spectral energy is as follows: Sample data of the input signal Perform a Discrete Fourier Transform to obtain the spectrum of the entire signal. Based on the bandwidth of the input sampling signal Determine the spectrum center The average energy over the bandwidth is obtained. , average energy With preset energy threshold In comparison, if the average energy Greater than or equal to the preset energy threshold If the signal is positive, it indicates that there is a signal in the sampled data of that segment, and time-domain detection is performed on it.

3. The time-frequency domain joint continuous and burst signal detection method of claim 2, wherein, The specific method for detecting the signal bandwidth is as follows: After time-domain detection is completed, the discrete Fourier transform of each signal segment is performed separately to obtain the spectrum. The smoothed spectrum is obtained after processing with a smoothing filter. Search spectrum bandwidth range All peak values and corresponding position ,for , The spectral energy for searching to the left and right is lower than [a certain value] for the first time. The points are stored in the sequence respectively. and Among them, The set threshold; If there is no point with energy value lower than or a point with energy value higher than in the search process, the current search is skipped; after all the peak searches are completed, the corresponding signal bandwidth is obtained.

4. The time-frequency domain joint continuous and burst signal detection method of claim 1, wherein, The specific method for the dual sliding window detection is as follows: Input signal sampling data that shows a signal after spectral energy detection, and calculate the energy within the sliding windows on both the left and right sides point by point. , And calculate the ratio of the two. And adopt a preset signal-to-noise ratio threshold Filter out Pass The threshold signal will be the ratio of each positive / negative threshold region. The points corresponding to the maximum / minimum values ​​are stored as the start / end sequence of the burst signal, consisting of the start and end points of the burst signal segment, in the order of their alternating appearance in the time domain.

5. The time-frequency domain joint continuous and burst signal detection method of claim 4, wherein, The specific method for detecting the signal energy is as follows: Calculate the signal-to-noise ratio (SNR) of the average energy of each burst signal segment and the average energy of the remaining noise segment in the burst signal start / end sequence, and determine whether it exceeds a preset SNR threshold. If the value does not exceed the limit, it indicates that the burst signal segment is a false alarm signal caused by noise, and it is filtered out. The duration of the burst signal sequence after filtering is then detected.

6. The time-frequency domain joint continuous and burst signal detection method of claim 5, wherein, The specific method for detecting the signal duration is as follows: Based on the preset signal length threshold , with intervals smaller than The burst signal segments are spliced ​​together to form a single burst signal. The duration of all the resulting burst signals is calculated, and the output signal duration is greater than or equal to a threshold. The burst signal and its corresponding duration parameter, if there is no signal duration greater than or equal to the threshold. If a sudden signal is detected, the signal segment is considered as a continuous signal, and time-domain detection is completed.

7. A time-frequency domain joint continuous and burst signal detection system, characterized in that, include: A receiving antenna is used to capture electromagnetic radiation signals in space and convert them into electrical signals; The radio frequency channel is used to perform analog frequency conversion, filtering, and amplification of electrical signals, and then output them to the acquisition module. The acquisition module performs AD sampling and digital down-conversion on the received analog signal, generates one-end signal sampling data, and outputs it to the detection module; The detection module includes a frequency domain detection module and a time domain detection module, which are used to detect the signal sampled data in the frequency domain and time domain respectively, to complete the detection and discovery of continuous and burst signals and to acquire the time and frequency parameters of the signal; The frequency domain detection module performs spectrum analysis in the frequency domain to determine the existence of a signal within the bandwidth. If the signal exists, the actual bandwidth of the signal will be further determined after the time domain detection is completed. The time domain detection module performs burst signal detection on the existing signal in the time domain and obtains the start time and duration parameters of the burst signal.

Images