Signal detection method, device and equipment for to-be-detected target and storage medium

Abstract

The application relates to the technical field of signal processing, and discloses a signal detection method, device and equipment for a to-be-detected target and a storage medium, the method comprising the following steps: obtaining a baseband signal, decomposing the baseband signal, generating a plurality of sub-channel corresponding signals, respectively performing correlation detection on the sub-channel corresponding signals, determining a plurality of signal segments, recording time sequences, channel information and phase information corresponding to the signal segments, clustering the signal segments according to the time sequences, the channel information and the phase information, obtaining at least one signal segment set, determining a feature vector corresponding to the signal segment set according to the time sequences, the channel information and the phase information, determining a target signal segment set from the at least one signal segment set according to a preset rule, and collecting the baseband signal according to the feature vector corresponding to the target signal segment set to determine a signal corresponding to the to-be-detected target. Through the above method, the calculation amount is reduced, and the accuracy of target signal extraction is ensured through time and phase double verification.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, specifically to a signal detection method for a target to be detected, a signal detection device for the target to be detected, a signal detection equipment, and a computer-readable storage medium. Background Technology

[0002] In applications such as low-altitude target monitoring, radio security protection, and communication link testing, signal detection equipment typically needs to acquire baseband signals over a wide bandwidth. These baseband signals may exhibit characteristics such as suddenness, abrupt changes, variable bandwidth occupancy, and adjacent or partially overlapping signals corresponding to multiple targets in the frequency domain. Current detection schemes usually employ methods such as energy detection, spectrum peak search, frequency sweep detection, or single-channel correlation detection to determine the signal corresponding to the target to be detected.

[0003] However, while energy detection is simple to implement, it is easily affected by noise floor fluctuations, strong leading signals, and non-target radiation sources; frequency sweep detection requires cyclic searching over a large frequency range and has poor real-time performance; single-channel detection is prone to missed detections, duplicate detections, or incorrect target attribution in broadband multi-target scenarios. Therefore, there is an urgent need for a real-time processing method for broadband complex signal reception, enabling signal detection equipment to quickly complete signal detection of the target and accurately detect the signal corresponding to the target. Summary of the Invention

[0004] In view of the above problems, this application provides a signal detection method for a target to be detected, a signal detection device for a target to be detected, a signal detection equipment, and a computer-readable storage medium to solve the problems of poor real-time performance and inaccurate signal detection in the prior art.

[0005] According to a first aspect of the embodiments of this application, a signal detection method for a target to be detected is provided. The method includes: acquiring a baseband signal in a region to be detected and decomposing the baseband signal to generate signals corresponding to multiple sub-channels; performing correlation detection on the signals corresponding to each sub-channel to determine multiple signal segments and recording the time series, channel information, and phase information corresponding to each signal segment; clustering the multiple signal segments according to the time series, channel information, and phase information to obtain at least one signal segment set, wherein multiple signal segments in the signal segment set all originate from the same target; determining the feature vector corresponding to the signal segment set according to the time series, channel information, and phase information corresponding to each signal segment in the signal segment set; determining the target signal segment set corresponding to the target to be detected from the at least one signal segment set according to a preset rule; and collecting the baseband signal in the region to be detected according to the feature vector corresponding to the target signal segment set to determine the signal corresponding to the target to be detected.

[0006] In one alternative approach, baseband signals within the detection area are acquired based on feature vectors corresponding to the target signal segment set to determine the signal corresponding to the target to be detected. Specifically, this includes: decomposing the baseband signals within the detection area to generate multiple sub-channel signals to be detected; traversing the signals to be detected corresponding to each sub-channel and performing correlation detection on the signals to be detected to determine the target feature vectors corresponding to the signals to be detected; matching the target feature vectors with the feature vectors corresponding to the target signal segment set, and determining the signal corresponding to the target to be detected from the baseband signals based on the matching results.

[0007] In one alternative approach, correlation detection is performed on the signal to be detected to determine the target feature vector corresponding to the signal to be detected. Specifically, this includes: determining a detection window based on the time series in the feature vector corresponding to the target signal segment set; performing correlation detection on the signal to be detected according to the detection window to obtain peak information; and determining the time series and phase information corresponding to the signal to be detected based on the peak information to obtain the target feature vector corresponding to the signal to be detected.

[0008] In one alternative approach, after acquiring baseband signals within the detection area based on feature vectors corresponding to the target signal segment set to determine the signal corresponding to the target to be detected, the method further includes: analyzing the signal corresponding to the target to be detected to obtain time information and channel data; generating an interference signal based on feature vectors corresponding to the target signal segment set, and outputting the interference signal based on the time information and channel data.

[0009] In one optional approach, correlation detection is performed on the signals corresponding to each sub-channel to determine multiple signal segments, and the time series, channel information, and phase information corresponding to each signal segment are recorded. Specifically, this includes performing the following steps on the signals corresponding to each sub-channel: performing correlation detection on the signals according to a preset sliding window to obtain a correlation value sequence; determining multiple signal segments and sub-correlation value sequences corresponding to each signal segment based on the correlation value sequence; and analyzing the sub-correlation value sequences to determine the time series and phase information corresponding to the signal segments.

[0010] In one alternative approach, the sub-correlation value sequence is analyzed to determine the time series and phase information corresponding to the signal segment. Specifically, this includes: determining the peak value in the sub-correlation value sequence and recording the position corresponding to the peak value to obtain the time series corresponding to the signal segment; calculating the amplitude based on the sub-correlation value sequence to generate the phase information corresponding to the signal segment.

[0011] In one alternative approach, multiple signal segments are clustered based on time series, channel information, and phase information to obtain at least one set of signal segments. Specifically, this includes: if the time series and channel information corresponding to a signal segment meet a first preset condition, then the signal segment is identified as a signal segment to be clustered; and a preset clustering algorithm is used to cluster the signal segments to be clustered to generate at least one set of signal segments.

[0012] According to a second aspect of the embodiments of this application, a signal detection device for a target to be detected is provided. The device includes: a signal decomposition module, used to acquire baseband signals in a region to be detected and decompose the baseband signals to generate signals corresponding to multiple sub-channels; a correlation detection module, used to perform correlation detection on the signals corresponding to each sub-channel, determine multiple signal segments, and record the time series, channel information, and phase information corresponding to each signal segment; a clustering module, used to cluster the multiple signal segments according to the time series, channel information, and phase information to obtain at least one signal segment set, wherein the multiple signal segments in the signal segment set all originate from the same target; a feature vector determination module, used to determine the feature vector corresponding to the signal segment set according to the time series, channel information, and phase information corresponding to each signal segment in the signal segment set; a target determination module, used to determine the target signal segment set corresponding to the target to be detected from the at least one signal segment set according to a preset rule; and a signal detection module, used to collect baseband signals in the region to be detected according to the feature vector corresponding to the target signal segment set to determine the signal corresponding to the target to be detected.

[0013] According to a third aspect of the embodiments of this application, a signal detection device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the signal detection method for the target to be detected as described in any of the preceding claims.

[0014] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the signal detection method for the target to be detected as described in any of the preceding claims.

[0015] This application embodiment achieves parallel processing of broadband signals through channel decomposition, reducing computational load; then, through multi-dimensional temporal feature extraction and cluster fusion, it solves the problems of incorrect attribution and duplicate reporting of cross-channel targets, improving the stability of the target list; finally, it acquires baseband signals based on feature vectors and ensures the accuracy of target signal extraction through dual temporal verification, laying a precise time-frequency synchronization foundation for the synchronous output of subsequent modulated test signals.

[0016] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of the signal detection method for a target to be detected provided in an embodiment of this application is shown; Figure 2 The time-frequency diagram of the baseband signal provided in the embodiment of this application is shown; Figure 3 This application provides an embodiment of a 32-channel time-frequency diagram after baseband signal channelization. Figure 4 This paper illustrates a schematic diagram of the structure of the relevant value sequence provided in an embodiment of this application; Figure 5 A flowchart illustrating a signal detection method for a target to be detected according to another embodiment of this application is shown; Figure 6 This illustration shows another structural schematic diagram of the baseband signal provided in an embodiment of this application; Figure 7 A schematic flowchart of a signal detection method for a target to be detected according to another embodiment of this application is shown; Figure 8 This paper shows a schematic diagram of the structure of the signal detection device for the target to be detected provided in an embodiment of this application; Figure 9 A schematic diagram of the structure of the signal detection device provided in an embodiment of this application is shown. Detailed Implementation

[0018] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0019] Signal detection equipment can simultaneously detect baseband signals of multiple unknown or semi-unknown targets within a wide instantaneous bandwidth. From these baseband signals, it can determine the presence of target signals, extract parameters (such as frequency, amplitude, phase, and modulation scheme), or reconstruct the information they carry. Currently, signal detection equipment mainly employs methods such as energy detection, spectrum peak search, frequency sweep detection, or single-channel correlation detection to detect baseband signals. However, in these methods, energy detection is easily affected by noise floor fluctuations, strong leading signals, and non-target radiation sources; frequency sweep detection requires cyclic searching within a large frequency range, resulting in poor real-time performance; and single-channel detection is prone to missed detections, duplicate detections, or incorrect target attribution in broadband multi-target scenarios.

[0020] Based on this, this application implements a signal detection method for a target to be detected. This method can decompose the baseband signal to generate signals corresponding to multiple sub-channels and process the multiple sub-channels in parallel, enabling the signal detection device to complete fast detection under a multi-channel parallel structure, effectively reducing computational complexity and realizing near real-time processing in broadband complex signal scenarios.

[0021] Furthermore, this method identifies multiple signal segments in the baseband signal through correlation detection, and simultaneously extracts the corresponding time series, signal information, and phase information of the signal segments as signal features. Then, it clusters multiple signal segments based on these signal features. This not only extracts multi-dimensional signal features through correlation detection, effectively improving detection robustness and information richness, but also aggregates signal segments originating from the same target into a single signal segment set, effectively solving the problems of repeated reporting by multiple targets and cross-channel target splitting, thus improving the stability of the target list.

[0022] Finally, based on preset rules, a target segment set corresponding to the target to be detected is determined from multiple signal segment sets. Then, based on the feature vectors corresponding to the target signal segment set, the baseband signal in the detection area is collected to determine the signal corresponding to the target to be detected. This allows for accurate tracking and extraction of the signal corresponding to the target to be detected using the target features such as the time and phase information of the baseband signal, and ensures that the subsequently output modulated test signal is precisely synchronized in time and phase with the signal corresponding to the target to be detected.

[0023] According to a first aspect of the embodiments of this application, a signal detection method for a target to be detected is provided, such as... Figure 1 As shown, the signal detection method for the target to be detected is performed by a signal detection device. The signal detection device is an electronic instrument used to capture, measure, and analyze electromagnetic / radio signals, electrical signals, or physical signals. Specifically, the signal detection device can be a spectrum analyzer, signal analyzer, etc. Figure 1 As shown, the method includes the following steps: Step S110: Obtain the baseband signal in the area to be detected, and decompose the baseband signal to generate signals corresponding to multiple sub-channels.

[0024] The baseband signal refers to the broadband complex baseband acquisition signal output by the radio frequency front-end or data acquisition equipment, which includes in-phase component I and quadrature component Q, and retains the amplitude, frequency, and phase information of the radio frequency signal. Specifically, the signal detection equipment can receive the digital sampling stream transmitted by the radio frequency front-end and store it in a double buffer or ring buffer structure to provide a continuous data stream for subsequent processing.

[0025] Sub-channels refer to the narrower signal components obtained after channelization of a broadband signal. Specifically, they can be divided into 16, 32, 64, or 128 sub-channels. Signal detection equipment can employ polyphase filter banks to channelize the broadband input signal. Specifically, the prototype low-pass filter is decomposed into K polyphase branches. The input baseband signal is filtered, decimated, and subjected to a K-point Fast Fourier Transform (FFT) for frequency domain synthesis, thus dividing the broadband baseband signal into K sub-channels in one operation. Each sub-channel carries a sub-channel number and a center frequency mapping relationship.

[0026] Furthermore, the multiphase filter bank can be replaced with an FFT filter bank, a digital downconverter bank, a mixer-low-pass-decimation structure, or other equivalent channelized structures. The prototype filter can be designed using Blackman, Hamming, Kaiser, or other ripple or window functions, and the filter order and transition band can be adjusted according to hardware resources.

[0027] by Figure 2 and Figure 3 Taking the structure shown as an example, Figure 2 The time-frequency diagram of the baseband signal is shown. Figure 3 The following is a time-frequency diagram of 32 channels after baseband signal channelization. Figure 2 It contains multiple baseband signals (i.e., the yellow area in the diagram, specifically including signals a, b, c, d, and e), and the frequency ranges and frequency intervals spanned by the different baseband signals are all different. The signal detection equipment can use a polyphase filter bank to... Figure 2 After decomposing the baseband signal shown, it can generate signals such as... Figure 3 The signals corresponding to the 32 sub-channels are shown, and the center frequency of each sub-channel is recorded. For example, the center frequency of the first sub-channel is 5681.0MHz, the center frequency of the second sub-channel is 5688.6MHz, the center frequency of the third sub-channel is 5696.3MHz, and so on.

[0028] Step S120: Perform correlation detection on the signals corresponding to each sub-channel to determine multiple signal segments, and record the time series, channel information and phase information corresponding to each signal segment.

[0029] In baseband signal processing and digital communication, signals typically carry prefixes and suffixes, also known as preambles and end-of-frame markers, which are additional fields in the data link layer / physical layer frame structure. The prefix (preamble) is usually located at the very beginning of each data frame and is a known, fixed-pattern bit sequence. The suffix (end-of-frame marker) is located at the end of the frame, commonly in the form of an end-of-frame delimiter or an FCS checksum field. Signal detection equipment can use the known preamble to perform sliding correlation / matched filtering to find the location of correlation peaks, thereby pinpointing the start of the frame.

[0030] Taking Orthogonal Frequency Division Multiplexing (OFDM) signals as an example, the signals typically carry a cyclic prefix (CP). The cyclic prefix is ​​a guard interval added to the beginning of a symbol by copying the tail of the valid symbol. This guard interval is used to resist multipath propagation and maintain subcarrier orthogonality. Signal detection equipment can use methods such as cyclic shift CP correlation, fixed CP correlation, parallel correlation with multiple candidate CP lengths, normalized delay correlation, short training sequence correlation, preamble correlation, or joint detection of CP and pilot signals to perform correlation detection on the signals corresponding to each subchannel.

[0031] The cyclic shift (CP) correlation detection utilizes the repetition relationship between the cyclic prefix and the tail samples of the valid symbols to perform complex correlation calculations, thereby obtaining the symbol boundary, correlation amplitude, and correlation phase. Specifically, the signal detection device can independently perform CP correlation operations for each sub-channel using a sliding window approach, calculating the complex correlation value between the CP segment and the tail segment of the valid symbols for the candidate valid symbol length N and the cyclic prefix length. Due to the uncertainty of the symbol start position in the signal, the signal detection device performs a cyclic shift search within the candidate shift set, calculates the normalized correlation, and takes the maximum value as the detection result for that position.

[0032] Signal detection equipment can perform threshold decision and local maximum search on the normalized correlation value sequence to extract candidate CP peaks and their corresponding time, amplitude, and phase, and combine consecutive peaks into signal segments. For each signal segment, time series (e.g., peak time, time interval between adjacent peaks), channel information (e.g., sub-channel number), and phase information (e.g., correlation amplitude, correlation phase, phase difference) are extracted to construct the feature vector corresponding to the signal segment. Of course, the feature vector corresponding to the signal segment can also include data such as normalized correlation and detection confidence.

[0033] Specifically, in order to provide accurate data support for subsequent steps, step S120 may include the following steps (steps S121 to S123): For each sub-channel corresponding signal, the following steps are performed: Step S121: Perform correlation detection on the signal according to the preset sliding window to obtain the correlation value sequence.

[0034] The signal detection device can employ a parallel processing architecture, allocating independent hardware processing threads or software computing cores to the multiple sub-channels. This ensures that each sub-channel performs subsequent sliding window correlation detection, segment extraction, and feature analysis operations without differentiation. For example, in a scenario with 32-channel multiphase filter output, the signal detection device simultaneously starts 32 independent detection threads to synchronously expand and calculate the low-speed complex sequence for each sub-channel.

[0035] A preset sliding window refers to a data truncation interval that slides along the time axis with a fixed step size. Its length typically covers the sum of the length of a candidate valid symbol and the length of the cyclic prefix, and is used to search for CP repeating structures point by point in the sub-channel data stream. The correlation value sequence is a sequence of complex correlation values ​​calculated at each sampling position by the sliding window, arranged in chronological order, and includes normalized correlation amplitude and correlation phase information.

[0036] Specifically, the signal detection device moves a sliding window across the complex sequence of each sub-channel. For the data within the sliding window, it extracts candidate cyclic prefix segments and effective symbol tail segments, and calculates their complex correlation values. Specifically, the conjugate of the CP segment is multiplied by the tail segment and summed, then divided by the sum of the energies at both ends for normalization, yielding a normalized complex correlation value. If the starting position of the target symbol is uncertain, the signal detection device performs a cyclic shift search within the candidate shift set, selecting the shift with the highest normalized correlation as the detection result for the current window. As the window slides point by point, the signal detection device outputs a series of complex correlation values, thus forming a correlation value sequence.

[0037] As an example, suppose the signal output by the kth sub-channel is The effective symbol length of the signal is N, and the cyclic prefix length is... Let Q be the set of cyclic shifts. For a sliding position n and a cyclic shift q, the cross-correlation of CP obtained from the correlation detection can be represented as:

[0038] In addition, signal detection equipment can also calculate energy terms. (CP part energy) and energy terms The correlation value is obtained by normalizing the cross-correlation amplitude of the corresponding data segment energy, as shown in the following formula: ; ; ; in, To prevent extremely small positive numbers with a denominator of zero, for the same sliding position n, select from the candidate shift set Q that makes the shift result... The largest q is taken as the optimal cyclic shift detection result for that position.

[0039] Step S122: Determine multiple signal segments and the corresponding sub-correlation value sequences for each signal segment based on the correlation value sequence.

[0040] Here, a signal segment refers to a range of candidate target waveforms that appear consecutively within a sub-channel and meet the detection threshold; that is, the time period covered by the effective symbols and cyclic prefixes corresponding to consecutive peaks within the same sub-channel. A sub-correlation value sequence refers to a subset of data containing only correlation values ​​within the corresponding signal segment's time range, which centrally reflects the details of the amplitude and phase changes of the signal within that signal segment.

[0041] Signal detection equipment can perform threshold detection and local maximum search on correlation value sequences. First, sampling points with normalized correlation amplitudes below a preset threshold are set to zero to eliminate the noise floor. Then, local maxima points satisfying peak interval and persistence constraints are searched in the remaining region as candidate peaks. After determining the peak location, the signal detection equipment can delineate the start and end boundaries of the signal segment by truncating the CP length forward and the effective symbol length backward, centered on the peak time. Data within the corresponding time interval is then extracted from the correlation value sequence to form a sub-correlation value sequence.

[0042] For example, if a signal detection device finds five consecutive correlation peaks in the correlation value sequence of subchannel 5, and the time interval between the peaks is stable at about 4 microseconds, the signal detection device will use these five peak time points as a reference to extract the corresponding baseband waveform as a signal segment, and extract the correlation values ​​near these five peaks to form a sub-correlation value sequence.

[0043] Step S123: Analyze the sub-correlation value sequence to determine the time series and phase information corresponding to the signal segment.

[0044] The time series is a set of time-dimensional parameters, including the precise location, peak interval, and duration of relevant peaks within a signal segment, recorded in chronological order. The phase information consists of the amplitude of the complex correlation value (CP) within the signal segment and its time-varying sequence, reflecting the frequency offset, phase drift, and synchronization status of the signal segment.

[0045] The signal detection device can extract the precise sampling time when the normalized correlation amplitude reaches a local maximum for each candidate peak in the sub-correlation value sequence as the peak time, and combine the consecutive peak times to obtain the time series. Simultaneously, it extracts the real and imaginary parts of the complex correlation value at the peak, calculates its argument using the arctangent function, obtains the correlation phase of the current peak, and then calculates the time interval and phase difference between adjacent peaks. Aligning the phase and phase difference with the time series constitutes complete phase information. Specifically, step S123 may include the following steps (steps S123a to S123b): Step S123a: Determine the peak value in the sub-correlation value sequence and record the position corresponding to the peak value to obtain the time series corresponding to the signal segment.

[0046] Step S123b: Calculate the amplitude based on the sub-correlation value sequence to generate phase information corresponding to the signal segment.

[0047] The sub-correlation value sequence is a subset of normalized complex correlation data output by sliding window cyclic shift complex correlation operation within a specific signal segment time range. It centrally reflects the correlation strength and phase details of the CP repetition structure within that signal segment. The peak value is the sampling point in the sub-correlation value sequence where the normalized correlation amplitude exhibits a local maximum, indicating that the repetition matching degree between the CP and the tail of the effective symbol reaches its highest at that position.

[0048] The position corresponding to the peak is the absolute time coordinate or discrete sampling time index of the peak in the sub-channel data stream. The time series is a set of multiple peak positions arranged in chronological order, including information such as the time of peak occurrence, peak interval, and duration.

[0049] Specifically, the signal detection device first applies an amplitude threshold decision to the subcorrelation value sequence to filter out noise baseline data below the threshold; then it performs a local maximum search algorithm in the remaining data, that is, it compares the amplitude of the current sampling point with the amplitudes of the adjacent sampling points before and after it. If the current amplitude is the largest and meets the preset peak interval constraint and persistence constraint, then the point is determined to be a valid peak.

[0050] by Figure 2 and Figure 4 Taking the structure shown as an example, the signal detection device processes the signals corresponding to each sub-channel in parallel to determine the signal segments, i.e. Figure 2 Each signal within a red box represents a signal segment. Specifically, the signal segment is determined by obtaining a correlation value sequence through relevant detection, such as... Figure 2 After performing correlation detection on the signal d in the signal, the following can be obtained: Figure 4The correlation value sequence is shown. The correlation value is higher at position d and lower at other positions. The signal detection device can determine each signal segment in the baseband signal based on the magnitude of the correlation value, and based on... Figure 4 The magnitude of each correlation value in the correlation value sequence shown is used to determine the effective peak value, for example, by... Figure 4 Peak values ​​exceeding the red dashed line are defined as valid peak values.

[0051] For adjacent plateau-shaped peaks caused by multipath effects or oversampling, signal detection equipment can use a peak centroid algorithm or select the position of maximum amplitude as the true peak point. After locking the peak, the signal detection equipment can read the absolute timestamp or sampling index corresponding to the peak in the baseband signal as position information, and combine the positions of multiple consecutive peaks within the same signal segment in chronological order, thus recording and generating the time series corresponding to the signal segment.

[0052] The argument angle refers to the phase angle of the complex correlation value in the subcorrelation value sequence, that is, the angle between the complex number and the positive direction of the real axis in the complex plane, which is calculated from the real and imaginary parts using the arctangent function. The phase information consists of the argument angle corresponding to each peak in the signal segment and its phase sequence changing over time, which can reflect the carrier frequency offset, phase drift, and synchronization status of the signal segment.

[0053] For each identified peak in the sub-correlation value sequence, the signal detection equipment extracts the real and imaginary parts of the normalized original complex correlation value. It then calculates the argument of this complex value using a four-quadrant arctangent function to obtain the correlation phase of the current peak. To form a continuous phase change trajectory, the signal detection equipment can further calculate the phase difference between adjacent peaks and estimate the frequency offset trend by combining this with the peak time interval. If the phase difference between adjacent peaks exceeds the positive and negative values... To detect the transitions, the signal detection device performs a phase unrolling operation to maintain phase continuity. Finally, the amplitude, phase difference, and frequency offset trend parameters corresponding to each peak are arranged in chronological order to generate the phase information corresponding to that signal segment.

[0054] As an example, phase information can be calculated using the following formula:

[0055] Phase information It can be used as an input feature for determining signal phase continuity, estimating frequency offset trend, and subsequent synchronous tracking. Of course, phase continuity determination can also be replaced by phase difference, phase slope, frequency offset residual, coherent integral result, or linear prediction residual after phase expansion.

[0056] Through steps S123a to S123b, the peak position is accurately established and the time series is recorded using local maximum search, providing a solid basis for the calibration of the target symbol boundary and subsequent steps. By calculating the complex correlation value argument and generating the phase sequence, the detection dimension is expanded from a single amplitude to a dual temporal and phase feature, enabling the signal detection device to use phase fingerprints for target identification and cross-channel fusion. At the same time, the high-precision phase information supports the phase state matching of subsequent modulated test signals, ensuring the synchronization and accuracy of signal tracking and interference output.

[0057] Through steps S121 to S123, sliding correlation is performed independently for each sub-channel, fully utilizing the speed reduction of multi-channel parallel operation to achieve real-time processing of broadband signals. Correlation value sequences are obtained through normalized correlation detection, effectively resisting noise and adjacent channel interference, ensuring detection robustness. Furthermore, signal segments and sub-correlation value sequences are accurately separated from the correlation value sequences, and time series and phase information are extracted accordingly. This elevates the detection dimension from simple existence judgment to temporal feature extraction, providing a basis for comparing time and phase consistency for subsequent signal segment convergence, and laying a data foundation for accurate synchronous output of subsequent modulated test signals through time-frequency parameter matching.

[0058] After step S120, step S130 is executed: multiple signal segments are clustered according to time series, channel information and phase information to obtain at least one signal segment set, wherein multiple signal segments in the signal segment set all originate from the same target.

[0059] In this context, a signal segment set refers to a collection of multiple signal segments that have been clustered and merged into the same target category. All signal segments within the set are considered by the signal detection equipment to originate from the same physical target. Specifically, based on the characteristics of each signal segment, judgments can be made regarding time consistency, phase continuity, channel adjacency, and periodic stability to determine whether the signal segments originate from the same target. If they do, they are grouped into the same signal segment set.

[0060] Signal detection equipment can classify and cluster signal segments using methods such as rule-based thresholds, clustering algorithms, Kalman filter tracking, Hungarian matching, or machine learning classifiers to generate a signal segment set. Specifically, the signal detection equipment can use preset rules to match and cluster signal segments; these preset rules can be one or more. For example, it can determine whether the time interval between two candidate signal segments is less than a preset time threshold or conforms to the same symbol period model; whether the phase difference after phase expansion or frequency offset compensation is less than a preset phase threshold; whether candidate signal segments are located in the same sub-channel or several adjacent sub-channels with synchronized peak occurrence times; and whether the variance of the consecutive peak time interval is less than a set threshold. Signal segments that meet the preset rules can be grouped into the same target category and stored in the same signal segment set.

[0061] Of course, to improve the accuracy of clustering, signal detection equipment can also use a combination of multiple clustering methods to cluster signal segments. For example, the signal detection equipment can first use a rule threshold to detect signal segments and remove obvious noise peaks, and then perform temporal feature clustering on the remaining candidate signal segments to reduce the probability of misclassification. Specifically, step S130 may include the following steps (steps S131 to S132): Step S131: If the time series and channel information corresponding to the signal segment meet the first preset condition, then the signal segment is determined as the signal segment to be clustered.

[0062] Step S132: Use a preset clustering algorithm to cluster the signal segments to be clustered, and generate at least one set of signal segments.

[0063] The first preset condition is a set of multi-dimensional constraint rules pre-set by the signal detection device to initially determine whether different signal segments have the potential to be from the same source. Its core includes channel adjacency and periodic stability. The signal segments to be clustered are the set of signal segments that have passed the screening of the first preset condition and are considered by the signal detection device to be likely to belong to the same target and are thus allowed to enter the subsequent deep clustering calculation.

[0064] Specifically, the signal detection device first extracts the time series and channel information corresponding to each signal segment, and then compares and verifies the signal segments based on the time series and channel information. For example, in the channel adjacency scheme, the signal detection device can determine the frequency range of the target signal based on the signal type corresponding to the target to be detected, and then determine the signal segments to be clustered by comparing the channel information with the frequency range of the target signal.

[0065] As an example, assuming the target to be detected primarily communicates using 2.4GHz Wi-Fi or Bluetooth signals, the frequency of the corresponding signal should be within the range of 2.4000GHz to 2.4835GHz, with a total bandwidth of 83.5MHz. The signal detection device can determine whether a signal segment falls within this range based on channel information. Furthermore, the frequency range of 5.8GHz image transmission or remote control signals should be 5.725 to 5.865GHz. When the signal detection device is specifically used for tracking image transmission signals, it can determine whether a signal segment falls within this range based on channel information.

[0066] Regarding periodic stability, a complete OFDM signal typically contains multiple symbols, each corresponding to a valid symbol segment and a cyclic prefix. Therefore, after correlation detection of the OFDM signal, the signal segment should correspond to a certain number of peaks, such as... Figure 4 As shown in the figure, a red peak represents the location of the cyclic prefix of a symbol in the signal. The signal detection device can determine the number of peaks corresponding to a signal segment through time series analysis, and then compare the number of peaks with a preset threshold.

[0067] If a signal segment falls within the frequency range corresponding to the target signal and the number of peaks is greater than or equal to a preset threshold, then the signal segment can be identified as a signal segment to be clustered; otherwise, it can be identified as a noise segment that needs to be removed. This pre-filtering, constrained by both time and channel, quickly removes isolated noise segments or irrelevant radiation source segments that are far apart in the frequency domain and clearly cannot belong to the same target. This reduces the search space and computational scale of subsequent deep clustering, effectively lowering the probability of misclassification.

[0068] The preset clustering algorithm is a mathematical algorithm that groups data points into the same category based on multi-dimensional feature distance metrics. Specifically, it can be a rule-based threshold clustering, DBSCAN, K-means, hierarchical clustering, Kalman filtering, particle filtering, Hungarian matching, multi-hypothesis tracking, or neural network classification algorithm that performs multi-dimensional association calculations based on time consistency, phase continuity, and confidence constraints.

[0069] The signal detection device can perform in-depth temporal feature correlation calculations on the signal segments to be clustered after preliminary screening. Specifically, the signal detection device can calculate the time difference of the occurrence time of CP-related peaks in the time series of different signal segments to be clustered, determine whether the time difference is less than a preset time threshold, or determine whether the interval between CP-related peaks in the time series of different signal segments to be clustered conforms to the same symbol period or frame period model.

[0070] Meanwhile, the signal detection equipment can also introduce phase information for connectivity verification, expand or compensate for the frequency offset trend of the phase sequence of the signal segments to be clustered, calculate the phase difference between adjacent peaks of the signal segments to be clustered, and determine whether the phase difference is less than the preset phase threshold, so as to ensure the consistency between the frequency offset trend and phase drift of the signal segments to be clustered.

[0071] If the signal segments to be clustered not only meet the prerequisite channel and periodic conditions, but also exhibit high consistency in phase variation patterns, and meet the time consistency and confidence standards, the clustering algorithm will merge the numbering, sub-channel set, time series, and phase series of these signal segments into parameter fusion to generate a signal segment set representing a single target entity. If there are significant breaks or mismatches in the phase characteristics among the signal segments to be clustered, the clustering algorithm will assign them to different clusters, generating multiple independent signal segment sets.

[0072] Steps S131 to S132 first use multi-dimensional feature thresholds to filter out signal segments with correlation potential to be clustered. Then, a clustering algorithm is used to fuse fragmented segments into a unified target entity based on temporal and channel consistency. In this way, the signal detection device can deeply mine the target fingerprint features formed by time series and phase information in multiple dimensions, so that broadband target signals distributed across channels can also be accurately identified and fused into a single target. This avoids the defect that the same target is misjudged as multiple independent targets because it crosses multiple adjacent sub-channels, effectively improving the stability of the target list and the classification accuracy in multi-target scenarios, and providing a reliable data foundation for the subsequent accurate determination of the signal corresponding to the target to be detected.

[0073] After step S130, step S140 is executed: the feature vector corresponding to the signal segment set is determined based on the time series, channel information and phase information corresponding to each signal segment in the signal segment set.

[0074] The feature vector is a multi-dimensional data structure used to characterize and distinguish target categories. Specifically, signal detection equipment can fuse and estimate parameters from the temporal features of multiple signal segments belonging to the same target after clustering. For example, it can estimate the symbol period or frame period of the target by combining the peak time series of each signal segment, estimate the frequency offset trend by combining the phase series, determine the target bandwidth and sub-channel set by summarizing the channel adjacency relationship, and calculate the comprehensive confidence score by combining the normalized correlation peak and duration. Finally, these parameters are packaged to form the feature vector of the target category. In addition to time series, phase information, and channel information, the feature vector can also incorporate features such as signal-to-noise ratio, peak width, correlation peak shape, frequency offset estimation, bandwidth estimation, and sub-channel energy patching.

[0075] Step S150: Determine the target signal segment set corresponding to the target to be detected from at least one signal segment set according to preset rules.

[0076] The preset rules are either built-in business strategies or external command logic of the signal detection equipment, while the target signal segment set is a specific set of targets selected by the signal detection equipment for tracking and testing. Specifically, the signal detection equipment can prioritize and filter the target list according to business needs. The selection logic can be to select the target with the highest confidence, highest power, closest distance, highest frequency band priority, or longest duration. A tracking status is established for the selected targets, and their most recent CP peak times and phases are recorded.

[0077] Step S160: Collect the baseband signal in the region to be detected based on the feature vector corresponding to the target signal segment set, so as to determine the signal corresponding to the target to be detected.

[0078] In this context, the signal corresponding to the target to be detected refers to a real physical signal that matches the target in terms of time, frequency, and phase. Specifically, the signal detection device can predict the time position and expected phase of the next detection window based on the estimated symbol period and phase sequence in the feature vector. Within this local window, it re-executes the local CP correlation search and determines whether the signal in the current window belongs to the target to be detected based on the CP correlation search results. Finally, the signal detection device can accurately extract the data within this time window from the baseband signal to complete the confirmation and extraction of the signal corresponding to the target to be detected.

[0079] As an example, suppose the signal detection method for the target is applied in a low-altitude target monitoring scenario, and the radio frequency front-end outputs a wideband complex baseband signal with an instantaneous bandwidth of 100MHz. The signal detection device first acquires this baseband signal and stores it in a ring buffer; subsequently, a polyphase filter bank decomposes the baseband signal into 32 sub-channels, each with a bandwidth of approximately 3.125MHz.

[0080] At this time, the image transmission signal of a certain UAV occupies three adjacent sub-channels. The signal detection equipment performs cyclic shift CP correlation detection in parallel on 32 sub-channels and detects CP peak values ​​that meet the threshold in all three adjacent sub-channels. The signal detection equipment combines these peak values ​​to generate a signal range, and then extracts signal segments from the baseband signal according to the signal range, and records the peak time sequence, the sub-channel number, and the related phase sequence of each signal segment.

[0081] During the clustering process, the signal detection device discovered that the signal segments of these three adjacent sub-channels were highly consistent in time, exhibited continuous phase change patterns, and their channels conformed to the frequency range of the image transmission signal. Therefore, they were merged into a single signal segment set, avoiding false alarms as three independent targets. Next, the signal detection device calculated the symbol period, frequency offset trend, and bandwidth of the image transmission signal based on the time interval and phase difference of this signal segment set, constructing a feature vector.

[0082] Finally, the signal detection device selects the transmitted signal as the target to be detected according to a priority strategy, and the set of signal segments corresponding to this transmitted signal is the target signal segment set. In subsequent tracking, the signal detection device predicts the occurrence time and phase of the next symbol of the transmitted signal based on the feature vector corresponding to the target signal segment set, and performs matching verification on the acquired baseband signal. If successful, it accurately outputs the signal data corresponding to the target to be detected.

[0083] In this embodiment, parallel processing of broadband signals is achieved through channel decomposition, reducing the computational load. Then, multi-dimensional temporal feature extraction and cluster fusion are used to solve the problems of incorrect attribution and repeated reporting of cross-channel targets, improving the stability of the target list. Finally, baseband signals are acquired based on feature vectors, and the accuracy of target signal extraction is ensured through dual temporal verification, laying a precise time-frequency synchronization foundation for the synchronous output of subsequent modulated test signals.

[0084] Furthermore, in order to improve the real-time performance of signal detection methods, such as... Figure 5 As shown, Figure 5 This invention illustrates a flowchart of a signal detection method for a target to be detected according to another embodiment of the present application. Step S160 may include the following steps: Step S161: Decompose the baseband signal in the detection area to generate multiple sub-channel signals to be detected.

[0085] The baseband signal within the detection area refers to the broadband complex baseband sampled signal, which contains the target radiation information at the current moment, newly received after the signal detection equipment determines the target signal segment set corresponding to the target to be detected. It retains the amplitude, frequency, and phase information of the signal. The signal to be detected refers to the multiple low-speed complex sequences output after the broadband baseband signal has been processed by a polyphase filter bank, with each complex sequence corresponding to a specific sub-channel number and center frequency.

[0086] As an example, a signal detection device can input a newly received broadband complex signal into a K-channel polyphase filter bank. The prototype low-pass filter is decomposed into K-channel polyphase branches. The input baseband signal is frequency-domain synthesized after branch filtering, decimation, and K-point fast Fourier transform. The broadband signal is divided into K sub-channels and output as a low-speed complex sequence at one time. At the same time, each sub-channel retains the sub-channel number and center frequency mapping relationship.

[0087] Step S162: Traverse the signals to be detected corresponding to each sub-channel, perform correlation detection on the signals to be detected, and determine the target feature vector corresponding to the signals to be detected.

[0088] Here, the target feature vector refers to a multi-dimensional feature set extracted from the newly input signal to be detected through correlation detection, including parameters such as candidate peak time, correlation phase, amplitude, and sub-channel number. Specifically, the signal detection device can traverse and read each sub-channel separately, and perform cyclic shift CP complex correlation operation in parallel on the read signal to be detected to calculate the normalized correlation between the cyclic prefix segment and the effective symbol tail segment, and search for local maxima to obtain candidate CP peaks.

[0089] Subsequently, the signal detection device extracts the precise time and the amplitude of the complex correlation value of the candidate CP peak as the correlation phase, and constructs the target feature vector corresponding to the current signal to be detected by combining the sub-channel number and the correlation amplitude.

[0090] Furthermore, in order to improve signal detection efficiency, step S162 may also include the following steps (steps S162a to S162c): Step S162a: Determine the detection window based on the time series in the feature vector corresponding to the target signal segment set.

[0091] Step S162b: Perform relevant detection on the signal to be detected according to the detection window to obtain peak information.

[0092] Step S162c: Determine the time series and phase information corresponding to the signal to be detected based on the peak information, and obtain the target feature vector corresponding to the signal to be detected.

[0093] The detection window refers to a local time interval defined on the time axis of the newly input signal to be detected, used for performing related operations. Its length and position are predicted from historical time series. Specifically, the signal detection device can predict the next peak position based on the historical time series and generate a detection window. This detection window is a small search interval around the predicted position of the next CP peak. For example, if the predicted position of the next peak is t, then [tW, t+W] can be set as the detection window, where W is used to cover clock error, sampling deviation, target drift, and detection error. If the target is lost for a short time (i.e., the CP peak cannot be detected in the detection window), the signal detection device can gradually expand W to perform fast reacquisition.

[0094] Next, local CP correlation search and phase matching are performed within the detection window. That is, the signal detection device reads the corresponding data segment from the buffer of the signal to be detected only within the defined detection window time interval, and performs cyclic shift CP complex correlation operation on the data segment to obtain peak information.

[0095] Then, the signal detection device can calculate the absolute time corresponding to the peak value based on the time offset corresponding to the peak value and the start time of the detection window, which serves as the time series of the signal to be detected. Simultaneously, it determines the phase information by calculating the amplitude. Finally, the extracted peak time, relevant phase, and relevant amplitude parameters are combined according to a preset data structure to generate the target feature vector corresponding to the current signal to be detected.

[0096] Steps S162a to S162c utilize historical time series to precisely constrain the detection interval, thereby performing cyclic prefix complex correlation operations within a smaller detection window. This extracts real-time features for matching verification while ensuring computational efficiency, effectively improving the accuracy and anti-interference capability of feature extraction.

[0097] After step S162, step S163 is executed: the target feature vector is matched with the feature vector corresponding to the target signal segment set, and the signal corresponding to the target to be detected is determined from the baseband signal based on the matching result.

[0098] The matching result refers to the degree of agreement between the newly extracted target feature vector and the feature vector of the historically established target signal segment set in terms of dimensions such as time error and phase residual. The signal corresponding to the target to be detected is the baseband physical signal segment that has been confirmed as the real tracking target in both time-frequency position and phase state.

[0099] Signal detection equipment can compare and verify the target feature vector extracted from the current signal to be detected with the feature vector of the target signal segment set. If the time series and phase information of the target feature vector match the time series and phase information of the feature vector of the target signal segment set, it can be determined that the signal to be detected and the signal segments in the target signal segment set both originate from the same target. Figure 6 As shown, the signal detection device can mark the signal corresponding to the target being detected in the acquired baseband signal in order to track the target. Figure 6 This shows another schematic diagram of the baseband signal structure.

[0100] by Figure 2 Taking the baseband signal shown as an example, the signal detection device acquires... Figure 2 After the baseband signal shown, the baseband signal is decomposed to generate the following: Figure 3 The signals corresponding to the 32 sub-channels are shown. Then, the signal detection device can perform correlation detection on the signals corresponding to the 32 sub-channels respectively, generate the correlation value sequence corresponding to the signal, and determine the signal segment based on the magnitude of the correlation value in the correlation value sequence, and record the time series, channel information and phase information corresponding to each signal segment.

[0101] Then, as Figure 2As shown, the signals transmitted to the same target usually have a consistent appearance, for example... Figure 2 Each signal d in the sequence has a basically the same external shape, and its time-frequency information also has a high degree of similarity. Therefore, the signal detection device can cluster signal segments based on time series and phase information. When the time series and phase information of two signal segments are relatively similar, it can be determined that the two signal segments originate from the same target. These two signal segments are then clustered into the same signal segment set, allowing the signal detection device to extract more detailed feature information from the signal segment set to generate a feature vector corresponding to each signal segment set.

[0102] Finally, the signal detection device acquires the baseband signal of the region to be detected based on the feature vectors corresponding to the signal segment set, thus accurately obtaining the signal corresponding to the target. For example, if the signal detection device determines the signal segment set corresponding to signal d as the target signal segment set, then when the signal detection device acquires the baseband signal based on the feature vectors of the target signal segment set, it can do so as follows: Figure 6 The diagram shows how to accurately find the signal that is the same as signal d (i.e. the signal corresponding to the target) from all baseband signals in the detection area, and mark signal d with the blue dot in the figure to achieve tracking of signal d. At the same time, it can also update the time series, phase information and confidence in the feature vector corresponding to signal d.

[0103] Of course, signal detection equipment can also generate interference signals based on the feature vectors of the target signal segment set, and based on... Figure 6 The location of the detected signal d outputs an interference signal, which can be highly synchronized with signal d and can be concentrated in the channel where signal d is located, rather than outputting the interference signal in the entire channel. This ensures the concentrated output of the interference signal and avoids wasting resources.

[0104] In this embodiment, by performing channel decomposition on the baseband signal, the signal detection device can achieve real-time parallel processing of broadband signals with low computational overhead. Simultaneously, by extracting temporal features through correlation detection and employing a dual time and phase matching mechanism, the signal detection device maintains high robustness in target acquisition within complex electromagnetic environments, avoiding adjacent channel interference and false alarms from noise, thus ensuring the accuracy of target signal extraction. Furthermore, the signal detection device can perform parallel detection across multiple channels, accurately capturing the signal corresponding to the target even if it spans multiple sub-channels.

[0105] Of course, in addition to generating a detection window based on the feature vector corresponding to the target signal segment set and tracking the signal according to the detection window, the signal detection device can also use tracking strategies such as adaptive window, variable step size window, confidence-based window expansion, lock-loss re-acquisition, or multi-target parallel tracking to track the signal of the target to be detected.

[0106] Furthermore, in addition to accurately tracking the signal corresponding to the target being detected, the signal detection equipment can also generate a more accurate interference signal based on the feature vectors corresponding to the target signal segment set. This ensures that the interference signal is strictly synchronized with the signal corresponding to the target being detected in terms of time window, frequency band position, and phase state. Specifically, such as... Figure 7 As shown, Figure 7 The following is a flowchart illustrating a signal detection method for a target to be detected according to another embodiment of this application. After step S160, the method may further include the following steps: Step S170: Analyze the signal corresponding to the target to be detected to obtain time information and channel data.

[0107] Among them, time information refers to time-dimensional parameters such as precise symbol boundaries, frame start time, symbol period, and predicted start time of the next frame output, which are parsed from the currently tracked target. Channel data consists of frequency domain and channel state parameters such as the set of sub-channel numbers where the signal corresponding to the target to be detected is currently located, the center frequency mapping relationship, and the real-time energy or signal-to-noise ratio estimate of the current sub-channel.

[0108] If the signal corresponding to the target to be detected exists in only a single sub-channel, then the current sub-channel number set only contains the sub-channel number where the signal corresponding to the target to be detected is located; if the signal corresponding to the target to be detected has a wide bandwidth or undergoes frequency drift, causing it to appear in multiple sub-channels, then the current sub-channel number set contains all the sub-channel numbers that the signal corresponding to the target to be detected spans.

[0109] Signal detection equipment can perform real-time analysis of signals corresponding to confirmed targets. On one hand, based on continuously updated peak CP time series, it further refines the symbol boundaries at the current moment using interpolation or fitting algorithms, and calculates the predicted output start time series within a preset duration based on period estimation, forming high-precision time information. On the other hand, the signal detection equipment can calculate the target bandwidth based on the set of sub-channel numbers corresponding to the signal of the target, combined with the energy distribution of the signal in the sub-channels. Simultaneously, it extracts the low-speed complex sequence output from the current sub-channel to evaluate the channel state, thus summarizing the channel data.

[0110] Step S180: Generate an interference signal based on the feature vector corresponding to the target signal segment set, and output the interference signal based on time information and channel data.

[0111] In a legally authorized testing environment, the interference signal refers to a modulated test signal that shares the same time-frequency structure, CP repetition characteristics, and phase change pattern as the target signal. The signal detection equipment can construct a symbol structure based on the CP length and effective symbol length in the feature vector, copy the CP and add it to the front of the effective symbol, and simultaneously perform phase pre-compensation or phase following on the generated symbols based on the phase state and frequency offset trend in the feature vector, ensuring that the phase change pattern of the interference signal is consistent with the signal corresponding to the target signal. The interference signal can be of OFDM type structure, partial frequency band structure, pilot / CP matching structure, noise-type structure, swept frequency structure, or other test signals that match the time-frequency characteristics of the target.

[0112] Furthermore, the signal detection equipment can accurately shift the generated baseband signal spectrum to the target's frequency band location based on the target bandwidth and sub-channel number set in the channel data, thus completing the digital domain generation of the interference signal. Finally, based on the predicted output start time and preset duration in the time information, it initiates digital-to-analog conversion or RF transmission within a specified time window to transmit the generated interference signal. Simultaneously, the signal detection equipment can monitor the status of the signal corresponding to the target in real time; if the tracking status is temporarily lost or unlocked, it immediately stops or degrades the output.

[0113] As an example, suppose a signal detection device, operating in a complex low-altitude monitoring environment, has stably tracked an unknown OFDM target signal spanning sub-channels 8 and 9 through polyphase filtering channelization and cyclic shift CP correlation detection, and successfully extracted the signal corresponding to the identified target from the baseband buffer. At this point, the signal detection device can perform real-time analysis of the signal corresponding to the target, determining the current symbol boundary as 5000 microseconds based on the CP correlation peak value, predicting the subsequent detection window to be 5000 to 6000 microseconds, and confirming that the signal corresponding to the target occupies sub-channels 8 and 9 with a bandwidth of 5 MHz, summarizing these findings into time information and channel data.

[0114] Next, the modulated test signal generation module on the signal detection device reads the feature vector of the target signal segment set. Based on the CP length of 128 sampling points, the effective symbol length of 1024 sampling points, and the phase slope caused by the frequency offset recorded therein, it quickly reconstructs an OFDM modulated test signal with the same CP structure and phase drift characteristics in the digital domain.

[0115] Finally, the output scheduling control module of the signal detection equipment waits until the prediction time window opens at 5000 microseconds, then precisely shifts the generated OFDM-like modulation test signal to the corresponding RF bands of sub-channels 8 and 9 for transmission. If the signal detection equipment detects a temporary loss of the signal corresponding to the target during OFDM-like modulation test signal output, it immediately stops transmitting the OFDM-like modulation test signal.

[0116] In the above embodiments, by analyzing and obtaining precise time information and channel data, a synchronization reference is provided for the generation and output of interference signals, avoiding time-domain misalignment between the interference signal and the signal corresponding to the target to be detected. Furthermore, by generating a modulated test signal with dual matching of time-frequency structure and phase state based on the feature vector of the target signal segment set, the interference signal is no longer blind noise or wideband sweep, but a targeted simulated signal, improving the efficiency of spectrum security protection verification. Simultaneously, by outputting the interference signal based on time information and channel data, precise truncation of the output window and strict alignment of the frequency band are achieved, avoiding resource waste and additional electromagnetic pollution caused by continuous output across the entire frequency band, ensuring that the signal detection equipment can still efficiently and stably complete testing and evaluation tasks in complex burst link environments.

[0117] It is important to note that the generation of the modulated-like test signal does not require waiting for the complete reception of the entire target signal. Once the signal detection equipment detects several consecutive CP-related peaks that satisfy time consistency and phase continuity, and the target confidence exceeds a preset threshold, it can generate an interference signal based on the feature vector corresponding to the target signal segment set, and output the interference signal based on time information and channel data. For scenarios requiring higher reliability, the generation of the modulated-like test signal can also be configured to wait until the complete burst signal or complete frame structure is confirmed before proceeding.

[0118] It should be noted that the signal detection equipment can be based on platforms such as FPGA, RFSoC, DSP, GPU, CPU, and embedded SoC, or a combination thereof. Each module can be implemented using hardware pipelines, software threads, or a hardware-software co-processing approach. In addition to low-altitude target monitoring, the signal detection method for the target can also be used in applications such as licensed spectrum testing, radio security protection, communication link robustness verification, laboratory protocol analysis, and target signal management in complex electromagnetic environments.

[0119] According to a second aspect of the embodiments of this application, a signal detection device for a target to be detected is provided, such as... Figure 8 As shown, Figure 8 A schematic diagram of the structure of the signal detection device for the target to be detected provided in an embodiment of this application is shown. Figure 8As shown, the signal detection device 1 includes: a signal decomposition module 11, a correlation detection module 12, a clustering module 13, a feature vector determination module 14, a target determination module 15, and a signal detection module 16.

[0120] The signal decomposition module 11 acquires the baseband signal within the detection area and decomposes it to generate signals corresponding to multiple sub-channels. The correlation detection module 12 performs correlation detection on the signals corresponding to each sub-channel, identifies multiple signal segments, and records the time series, channel information, and phase information corresponding to each signal segment. The clustering module 13 clusters the multiple signal segments based on the time series, channel information, and phase information to obtain at least one signal segment set, wherein multiple signal segments in the signal segment set originate from the same target. The feature vector determination module 14 determines the feature vector corresponding to the signal segment set based on the time series, channel information, and phase information corresponding to each signal segment in the signal segment set. The target determination module 15 determines the target signal segment set corresponding to the target to be detected from at least one signal segment set according to preset rules. The signal detection module 16 acquires the baseband signal within the detection area based on the feature vector corresponding to the target signal segment set to determine the signal corresponding to the target to be detected.

[0121] In this embodiment, parallel processing of broadband signals is achieved through channel decomposition, reducing the computational load. Then, multi-dimensional temporal feature extraction and cluster fusion are used to solve the problems of incorrect attribution and repeated reporting of cross-channel targets, improving the stability of the target list. Finally, baseband signals are acquired based on feature vectors, and the accuracy of target signal extraction is ensured through dual temporal verification, laying a precise time-frequency synchronization foundation for the synchronous output of subsequent modulated test signals.

[0122] According to a third aspect of the embodiments of this application, a signal detection device is provided, such as... Figure 9 As shown, Figure 9 The diagram shows a schematic of the signal detection device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the signal detection device.

[0123] like Figure 9 As shown, the signal detection device 2 may include a processor 21 and a memory 22.

[0124] The memory 22 is used to store the computer program 23. The memory 22 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device. The computer program 23 may include computer-executable instructions.

[0125] The processor 21 is used to execute the computer program 23 to implement the above-described embodiment of the signal detection method for the target to be detected.

[0126] The processor 21 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The signal detection device 2 may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0127] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described signal detection method embodiment for the target to be detected.

[0128] This application provides a computer program that can be executed by a processor to implement the above-described signal detection method for the target to be detected.

[0129] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described signal detection method embodiment for the target to be detected.

[0130] In the several embodiments provided in this application, any function, if implemented as a software functional module / unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of this application can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or other electronic device) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0132] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In claims enumerating several means, several units or modules of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

[0133] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting a signal of a target, characterized in that, The method includes: The baseband signal within the detection area is acquired, and the baseband signal is decomposed to generate signals corresponding to multiple sub-channels; Correlation detection is performed on the signals corresponding to each sub-channel to determine multiple signal segments, and the time series, channel information and phase information corresponding to each signal segment are recorded. Based on the time series, the channel information, and the phase information, multiple signal segments are clustered to obtain at least one signal segment set, wherein multiple signal segments in the signal segment set originate from the same target; The feature vector corresponding to the signal segment set is determined based on the time series, channel information, and phase information corresponding to each signal segment in the signal segment set. According to preset rules, a target signal segment set corresponding to the target to be detected is determined from at least one of the signal segment sets; Based on the feature vector corresponding to the target signal segment set, the baseband signal within the detection area is collected to determine the signal corresponding to the target to be detected.

2. The signal detection method for the target to be detected according to claim 1, characterized in that, The step of acquiring the baseband signal within the detection region based on the feature vector corresponding to the target signal segment set to determine the signal corresponding to the target to be detected specifically includes: The baseband signal within the detection area is decomposed to generate multiple sub-channel corresponding signals to be detected; The signals to be detected corresponding to each sub-channel are traversed one by one, and correlation detection is performed on the signals to be detected to determine the target feature vector corresponding to the signals to be detected. The target feature vector is matched with the feature vector corresponding to the target signal segment set, and the signal corresponding to the target to be detected is determined from the baseband signal based on the matching result.

3. The signal detection method for the target to be detected according to claim 2, characterized in that, The step of performing correlation detection on the signal to be detected to determine the target feature vector corresponding to the signal to be detected specifically includes: The detection window is determined based on the time sequence in the feature vector corresponding to the target signal segment set; The signal to be detected is subjected to relevant detection according to the detection window to obtain peak information; Based on the peak information, the time series and phase information corresponding to the signal to be detected are determined, and the target feature vector corresponding to the signal to be detected is obtained.

4. The signal detection method for the target to be detected according to claim 1, characterized in that, After acquiring the baseband signal within the detection region based on the feature vector corresponding to the target signal segment set to determine the signal corresponding to the target to be detected, the method further includes: The signal corresponding to the target to be detected is analyzed to obtain time information and channel data; An interference signal is generated based on the feature vector corresponding to the target signal fragment set, and the interference signal is output based on the time information and the channel data.

5. The signal detection method for the target to be detected according to claim 1, characterized in that, The process of performing correlation detection on the signals corresponding to each sub-channel to determine multiple signal segments and recording the time series, channel information, and phase information corresponding to each signal segment specifically includes: For each sub-channel corresponding signal, the following steps are performed: The signal is subjected to correlation detection according to a preset sliding window to obtain a correlation value sequence; Based on the correlation value sequence, multiple signal segments and sub-correlation value sequences corresponding to each signal segment are determined; The sub-correlation value sequence is analyzed to determine the time series and phase information corresponding to the signal segment.

6. The signal detection method for the target to be detected according to claim 5, characterized in that, The step of analyzing the sub-correlation value sequence to determine the time series and phase information corresponding to the signal segment specifically includes: Determine the peak value in the sub-correlation value sequence and record the position corresponding to the peak value to obtain the time series corresponding to the signal segment; The phase angle is calculated based on the sub-correlation value sequence to generate the phase information corresponding to the signal segment.

7. The signal detection method for the target to be detected according to claim 1, characterized in that, The step of clustering multiple signal segments based on the time series, the channel information, and the phase information to obtain at least one set of signal segments specifically includes: If the time series corresponding to the signal segment and the channel information satisfy the first preset condition, then the signal segment is determined as a signal segment to be clustered; The signal segments to be clustered are clustered using a preset clustering algorithm to generate at least one set of signal segments.

8. A signal detection device for a target to be detected, characterized in that, The device includes: The signal decomposition module is used to acquire the baseband signal in the area to be detected, and decompose the baseband signal to generate signals corresponding to multiple sub-channels; The correlation detection module is used to perform correlation detection on the signals corresponding to each sub-channel, determine multiple signal segments, and record the time series, channel information and phase information corresponding to each signal segment; A clustering module is used to cluster multiple signal segments according to the time series, the channel information and the phase information to obtain at least one signal segment set, wherein multiple signal segments in the signal segment set all originate from the same target; The feature vector determination module is used to determine the feature vector corresponding to the signal segment set based on the time series, the channel information, and the phase information corresponding to each signal segment in the signal segment set. The target determination module is used to determine the target signal segment set corresponding to the target to be detected from at least one of the signal segment sets according to preset rules; The signal detection module is used to collect the baseband signal in the region to be detected based on the feature vector corresponding to the target signal segment set, so as to determine the signal corresponding to the target to be detected.

9. A signal detection device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the signal detection method for the target to be detected according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the signal detection method for the target to be detected as described in any one of claims 1 to 7.

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