A signal detection identification method and system

Abstract

The application discloses a signal detection and identification method, comprising the following steps: S1, determining the specific specification of the signal to be detected and identified; S2, extracting the signal characteristics of the signal with the specific specification; S3, distinguishing the signal bandwidth and signal type on the frequency point based on the signal characteristics; S4, locating the accurate burst starting position based on the instantaneous phase curve of the signal or the unique code information of the signal; accurately determining the end position of each burst based on signal demodulation or the frame structure of the signal; S5, counting the repetitive characteristics of the burst starting position, and calculating the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive characteristics. The corresponding system, electronic equipment and computer readable storage medium are also disclosed, which can detect the target digital signal under the condition of noise packets and other interference, and the accuracy reaches within one symbol error of 98% or above, and the modulation, symbol rate and related information characteristics of the target signal are extracted.

Description

Technical Field

[0001] This invention relates to the fields of signal detection and recognition and noise suppression technology, and in particular to a signal detection and recognition method and system. Background Technology

[0002] The target signal contains numerous noise packets, including stationary and non-stationary noise. Stationary noise is characterized by its statistical properties remaining constant over time; its mean and variance are constant at any given moment. Non-stationary noise, on the other hand, exhibits changing statistical properties over time; its mean, variance, or autocorrelation function may vary. Stationary noise, remaining stable in the time domain, is easier to handle, while non-stationary noise requires more complex modeling and processing methods. Traditional signal detection and identification methods typically involve spectral transformation, performing accumulation operations in the frequency domain to calculate the start and end positions of bursts, then identifying modulation information through constellation diagrams of valid information, and calculating the symbol rate by accumulating energy at each symbol moment. However, these methods suffer from significant computational costs and the accuracy of calculated burst start and end positions often falls short of operational requirements. This can lead to misidentification of interference as a signal, resulting in false detections and subsequent errors in information extraction.

[0003] Furthermore, commonly used frequency offset estimation methods suffer from problems such as limited signal phase values, estimation failure when the phase value exceeds π, and a small estimation range when the frequency synchronization symbol length is short. These problems limit system performance. For signals with fixed frame structures and few discontinuous frequency synchronization symbols, using the traditional method of multiplying the conjugates of frequency synchronization symbols to estimate frequency offset leads to an excessively small estimation range. Once this range is exceeded, the frequency offset estimation method fails, thus limiting system performance. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention develops a signal detection and recognition method and system. This system detects target digital signals even in the presence of noise packets and other interference, achieving an accuracy of over 98% within one symbol error. It extracts features such as modulation, symbol rate, and related information of the target signal. Since the specifications of the signal to be detected are known, this invention specifically extracts particular signal features. First, the signal bandwidth and signal type at a given frequency point are distinguished based on the signal's obvious frequency domain characteristics. Then, the precise starting position of the signal is determined using the instantaneous phase curve or the signal's unique code information. The ending position of each burst is precisely determined through signal demodulation or based on the signal's frame structure. For signals with a frame structure, the frame header and the position of each burst within the radio frame are determined, and related information is extracted based on the frame structure indicated by special fields of the signal. For signals without a frame structure, related information is calculated by demodulating the information at the corresponding bit positions. For signals with high repetition of starting positions, the frequency offset and signal-to-noise ratio are calculated based on the signal's frequency domain spectrum. For the remaining types, the frequency offset is directly calculated by determining the point of maximum frequency domain correlation using correlation characteristics.

[0005] This invention provides a signal detection and recognition method, comprising:

[0006] S1, determine the specific specifications of the signal to be detected and identified;

[0007] S2, extract the signal features of the signal with the specific specifications;

[0008] S3, based on the signal characteristics, distinguish the signal bandwidth and signal type at the frequency point;

[0009] S4. Locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal.

[0010] S5, statistically analyze the repetitive characteristics of the burst start position, and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive characteristics.

[0011] In a preferred embodiment, the signal characteristics are the frequency domain characteristics of the signal.

[0012] In a preferred embodiment, the frame structure of the signal includes signals that partially have a frame structure and signals that partially do not have a frame structure.

[0013] In a preferred embodiment, locating the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal includes:

[0014] For partially continuous phase modulated signals, the phase information changes over time, with very small instantaneous phase changes, while noise has no phase information at all. By calculating the instantaneous phase information of the input data, continuous phase modulated signals and noise can be well distinguished. However, for discontinuous phase signals, using instantaneous phase information is not effective, so unique code information is used. By demodulating multiple collected signals, their common fields are identified, or unique codes for signals acquired using standard protocols are found. Then, based on the found unique codes, the corresponding signals are generated, and correlation processing is performed with the received signal data to obtain the precise location of the unique code. Finally, based on the position of the unique code in the signal structure, the starting position of the signal is deduced.

[0015] In a preferred embodiment, the signal-based frame structure accurately determines the end position of each burst, including:

[0016] For signals with frame structures, the frame header of the signal and the position of each burst in the radio frame are first determined; then, relevant information for accurately determining the end position of each burst is extracted based on the frame structure indicated by the signal's special fields.

[0017] For the frameless signal, the information of the corresponding bit position is first obtained by demodulation; then, the relevant information for accurately determining the end position of each burst is calculated based on the information of the corresponding bit position.

[0018] As a preferred embodiment, for the signal portion that has a frame structure, the precise determination of the end position of each burst based on the signal's frame structure can also be as follows:

[0019] First, the symbol at the corresponding bit position is obtained through demodulation;

[0020] Assuming the burst start position is accurate, the relevant information for the end position of each burst is precisely determined based on the symbol of the corresponding bit position.

[0021] In a preferred embodiment, S5 includes:

[0022] S51, For signals whose burst start position repeatability exceeds a predetermined threshold, calculate the frequency offset and signal-to-noise ratio information of the signal based on the frequency domain spectrum of the signal;

[0023] S52, for the remaining signals, calculate the maximum frequency domain correlation value using correlation characteristics, and calculate the frequency offset of the signal based on the maximum frequency domain correlation value.

[0024] A second aspect of the present invention is to provide a signal detection and recognition system for implementing the method of the first aspect, comprising:

[0025] A signal specification determination module (101) is used to determine the specific specifications of the signal to be detected and identified;

[0026] The signal feature extraction module (102) is used to extract the signal features of the signal having the specific specifications;

[0027] A bandwidth and type determination module (103) is used to distinguish the signal bandwidth and signal type at a frequency point based on the signal characteristics.

[0028] The burst start and end position determination module (104) is used to locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; and to accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal.

[0029] The signal feature recognition module (105) is used to statistically analyze the repetitive features of the burst start position and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive features.

[0030] A third aspect of the present invention provides an electronic device including a processor and a memory, the memory storing a plurality of instructions, the processor being configured to read the instructions and execute the method as described in the first aspect.

[0031] A fourth aspect of the present invention provides a computer-readable storage medium storing a plurality of instructions which can be read by a processor and executed as described in the first aspect.

[0032] The method, system, and electronic device provided by this invention have the following beneficial effects:

[0033] This invention specifically extracts features from particular signals. First, it distinguishes the signal bandwidth and signal type at a given frequency point based on the signal's obvious frequency domain characteristics. Then, it accurately determines the signal's starting point position using the instantaneous phase curve or unique code information. Next, it precisely determines the ending position of each burst through signal demodulation or based on the signal's frame structure. For signals with frame structures, it extracts relevant information by determining the frame header and the position of each burst within the radio frame, and then based on the frame structure indicated by special fields in the signal. For signals without frame structures, it calculates relevant information by demodulating the information at the corresponding bit positions. For signals with high repetition of starting positions, it calculates the signal's frequency offset and signal-to-noise ratio based on the signal's frequency domain spectrum. For the remaining types, it directly calculates the frequency offset by determining the point of maximum frequency domain correlation using correlation characteristics. Even in the presence of noise packets and other interference, it detects the target digital signal with an accuracy of over 98% within one symbol error, extracting features such as modulation, symbol rate, and related information of the target signal. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the signal detection and recognition method described in this invention.

[0035] Figure 2 This is a schematic diagram of the system architecture of the signal detection and recognition method described in this invention.

[0036] Figure 3 (a)- Figure 3 (c) is a time-frequency diagram of a signal in the time-division multiplexing described in this invention.

[0037] Figure 4 (a)- Figure 4 (b) is a schematic diagram of the starting position detection result using unique code information as described in this invention.

[0038] Figure 5 This is a schematic diagram of the electronic device structure described in this invention. Detailed Implementation

[0039] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0040] Example 1

[0041] like Figure 1 As shown, this embodiment provides a signal detection and recognition method, including:

[0042] S1, determine the specific specifications of the signal to be detected and identified;

[0043] S2, extract the signal features of the signal with the specific specifications;

[0044] S3, based on the signal characteristics, distinguish the signal bandwidth and signal type at the frequency point;

[0045] Signal bandwidth is determined by accumulating spectral characteristics, which is used for filter bandwidth selection in subsequent signal phase characteristic detection and unique code correlation detection, thus facilitating the removal of signal interference. The modulation type of the signal is distinguished by the Nth power squared spectral characteristics and the differential high-power characteristics of the delay conjugate. Some signals can be identified by prior signal data, allowing for retrieval of known signal sample libraries. By distinguishing the specific signal type, a better detection method can be selected. Signals with known modulation types can be directly detected through unique code correlation, yielding the most accurate results. Continuous-phase signals can be detected using phase characteristics, which is faster. The remaining signals are detected and classified by recording specific spectral characteristics.

[0046] S4. Locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal.

[0047] In this embodiment, step S4 includes: demodulating multiple collected signals to extract their common fields or finding unique codes for signals acquired by some standard protocols; then generating corresponding signals based on the found unique codes and performing related processing with the received signal data.

[0048] S5, statistically analyze the repetitive characteristics of the burst start position, and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive characteristics.

[0049] In a preferred embodiment, the signal characteristics are the frequency domain characteristics of the signal.

[0050] In a preferred embodiment, the frame structure of the signal includes signals that partially have a frame structure and signals that partially do not have a frame structure.

[0051] In a preferred embodiment, locating the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal includes:

[0052] For partially continuous phase modulated signals, the phase information changes over time, with very small instantaneous phase changes, while noise has no phase information at all. By calculating the instantaneous phase information of the input data, continuous phase modulated signals and noise can be well distinguished. However, for discontinuous phase signals, using instantaneous phase information is not effective, so unique code information is used. By demodulating multiple collected signals, their common fields are identified, or unique codes for signals acquired using standard protocols are found. Then, based on the found unique codes, the corresponding signals are generated, and correlation processing is performed with the received signal data to obtain the precise location of the unique code. Finally, based on the position of the unique code in the signal structure, the starting position of the signal is deduced.

[0053] In a preferred embodiment, the signal-based frame structure accurately determines the end position of each burst, including:

[0054] For signals with frame structures, the frame header of the signal and the position of each burst in the radio frame are first determined; then, relevant information for accurately determining the end position of each burst is extracted based on the frame structure indicated by the signal's special fields.

[0055] For the frameless signal, the information of the corresponding bit position is first obtained by demodulation; then, the relevant information for accurately determining the end position of each burst is calculated based on the information of the corresponding bit position.

[0056] As a preferred embodiment, for the signal portion that has a frame structure, the precise determination of the end position of each burst based on the signal's frame structure can also be as follows:

[0057] First, the symbol at the corresponding bit position is obtained through demodulation;

[0058] Assuming the burst start position is accurate, the relevant information for the end position of each burst is precisely determined based on the symbol of the corresponding bit position.

[0059] In a preferred embodiment, S5 includes:

[0060] S51, For signals whose burst start position repeatability exceeds a predetermined threshold, calculate the frequency offset and signal-to-noise ratio information of the signal based on the frequency domain spectrum of the signal;

[0061] S52, for the remaining signals, calculate the maximum frequency domain correlation value using correlation characteristics, and calculate the frequency offset of the signal based on the maximum frequency domain correlation value.

[0062] Example 2

[0063] like Figure 2 As shown, this embodiment provides a signal detection and recognition system for implementing the method of Embodiment 1, including:

[0064] The signal specification determination module 101 is used to determine the specific specifications of the signal to be detected and identified;

[0065] Signal feature extraction module 102 is used to extract signal features of a signal having the specific specifications;

[0066] The bandwidth and type determination module 103 is used to distinguish the signal bandwidth and signal type at a frequency point based on the signal characteristics.

[0067] The burst start and end position determination module 104 is used to locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; and to accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal.

[0068] The signal feature recognition module 105 is used to statistically analyze the repetitive features of the burst start position and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive features.

[0069] Application Examples

[0070] Detecting a certain time-division multiplexed signal:

[0071] Time-division multiplexed signals are regularly divided into fixed-length fields, categorized into two types: frame header fields and user fields. A single radio frame contains multiple fields; the frame header appears at the beginning of the frame, followed by user fields. The frame header is always present, while the user fields can be filled or left empty depending on the user's requirements for transmitting the signal. For example... Figure 3 (a)- Figure 3 (c) shows the time-frequency diagram of a signal in time-division multiplexing.

[0072] There are two unique code types: frame header field and user field. Signal correlation is then performed using these two unique codes for subsequent signal detection. The resulting start position detection result is as follows: Figure 4 (a)- Figure 4 As shown in (b).

[0073] The present invention also provides a memory that stores multiple instructions for implementing the method as described in Embodiment 1.

[0074] like Figure 5 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores a plurality of instructions, which can be loaded and executed by the processor to enable the processor to perform the method as described in Embodiment 1.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A signal detection and recognition method, characterized in that, include: S1, determine the specific specifications of the signal to be detected and identified; S2, extract the signal features of the signal with the specific specifications; S3, based on the signal characteristics, distinguish the signal bandwidth and signal type at the frequency point; S4. Locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal. S5, statistically analyze the repetitive characteristics of the burst start position, and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive characteristics.

2. The signal detection and recognition method according to claim 1, characterized in that, The signal characteristics are the frequency domain characteristics of the signal; the signal includes signals with some frame structure and signals without frame structure.

3. The signal detection and recognition method according to claim 2, characterized in that, S3 includes: The signal bandwidth is distinguished by the cumulative spectral characteristics of the signal, which is used for the selection of filter bandwidth in subsequent signal phase characteristic detection and unique code correlation detection. The modulation type of a signal can be distinguished by the spectral characteristics of the Nth power square and the differential high power characteristics of the delay conjugate. In some cases, the specific signal format can be identified by a priori signal segment, and a known signal sample library can be searched. By distinguishing the specific types of signals, a better detection method is selected based on the type, including: signals with known formats are obtained directly through unique code correlation; signals with continuous phase are detected through phase features; and the remaining signals are detected and classified by recording specific spectral features.

4. The signal detection and recognition method according to claim 3, characterized in that, S4 includes: For partially continuous phase modulated signals, the phase information changes over time, with very small instantaneous phase changes, while the noise component has no phase information at all. The continuous phase modulated signal and noise are distinguished by calculating the instantaneous phase information of the input data. For discontinuous phase signals, unique code information is used. By demodulating multiple collected signals, their common fields are sorted out or the unique code of the signal obtained from some standard protocols is found. Then, the corresponding signal is generated based on the obtained unique code, and correlation processing is performed with the received signal data to obtain the precise position of the unique code. Based on the position of the unique code in the signal structure, the burst start position of the signal is deduced.

5. The signal detection and recognition method according to claim 4, characterized in that, The signal-based frame structure precisely determines the end position of each burst, including: For signals with frame structures, the frame header of the signal and the position of each burst in the radio frame are first determined; then, relevant information for accurately determining the end position of each burst is extracted based on the frame structure indicated by the signal's special fields. For the frameless signal, the information of the corresponding bit position is first obtained by demodulation; then, the relevant information for accurately determining the end position of each burst is calculated based on the information of the corresponding bit position.

6. The signal detection and recognition method according to claim 5, characterized in that, For signals with frame structures, the frame structure based on the signal precisely determines the end position of each burst as follows: First, the symbol at the corresponding bit position is obtained through demodulation; Assuming the burst start position is accurate, the relevant information for the end position of each burst is precisely determined based on the symbol of the corresponding bit position.

7. The signal detection and recognition method according to claim 6, characterized in that, S5 includes: S51, For signals whose burst start position repeatability exceeds a predetermined threshold, calculate the frequency offset and signal-to-noise ratio information of the signal based on the frequency domain spectrum of the signal; S52, for the remaining signals, calculate the maximum frequency domain correlation value using correlation characteristics, and calculate the frequency offset of the signal based on the maximum frequency domain correlation value.

8. A signal detection and recognition system, used to implement the method according to any one of claims 1-7, characterized in that, include: The signal specification determination module (101) is used to determine the specific specifications of the signal to be detected and identified; The signal feature extraction module (102) is used to extract the signal features of the signal having the specific specifications; A bandwidth and type determination module (103) is used to distinguish the signal bandwidth and signal type at a frequency point based on the signal characteristics. The burst start and end position determination module (104) is used to locate the precise burst start position based on the instantaneous phase curve of the signal or the unique code information of the signal; and to accurately determine the end position of each burst based on signal demodulation or the frame structure of the signal. The signal feature recognition module (105) is used to statistically analyze the repetitive features of the burst start position and calculate the frequency offset and / or signal-to-noise ratio information of the signal based on the repetitive features.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing multiple instructions, and the processor being used to read the instructions and execute the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions, which can be read by a processor and executed as described in any one of claims 1-7.

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