A signal identification method, device and storage medium

Abstract

The embodiment of the application relates to the technical field of signal identification, and discloses a signal identification method, equipment and storage medium, the method comprises the following steps: obtaining a to-be-detected sampling sequence of a target signal, finding a first starting position of a CP sequence in the to-be-detected sampling sequence; determining a first starting range of the CP sequence, determining a PSS sequence according to a second starting position, and performing frequency deviation correction on the PSS sequence to obtain a PSS correction sequence and a first SSS correction sequence; for the first SSS correction sequence, determining a search value corresponding to each preset root in a plurality of preset roots under the second starting position, obtaining a plurality of search values, taking a preset root corresponding to a maximum value of all search values under the first starting range as a root of an SSS sequence, and taking the root of the SSS sequence as identification information for target identification. Through the above manner, the embodiment of the application improves the accuracy of signal identification and effectively distinguishes a plurality of similar target signals.

Description

Technical Field

[0001] This application relates to the field of signal recognition technology, specifically to a signal recognition method, device, and storage medium. Background Technology

[0002] In many fields, including military, security, transportation, and environmental monitoring, the identification and tracking of targets is a critical requirement. For example, in the field of drone detection, receiving radio signals transmitted by drones to detect, identify, determine the direction of, and locate drone targets is currently the main technical means in drone detection.

[0003] Existing technologies mainly rely on spectrum analysis-based methods to identify target objects that transmit target signals. Spectrum analysis-based methods extract the time-domain and frequency-domain patterns of the target signal on the time-frequency diagram to analyze the time-domain and frequency-domain characteristics of the target signal on the time-frequency diagram in order to identify the target object corresponding to the target signal.

[0004] However, since the target object may use complex signal modulation methods such as frequency hopping communication, the spectrum analysis method has difficulty in identifying frequency hopping signals, and its signal identification process has low accuracy, especially in distinguishing multiple similar target signals.

[0005] Therefore, how to provide a signal recognition method with high accuracy and the ability to effectively distinguish multiple similar target signals has become an urgent technical problem to be solved. Summary of the Invention

[0006] In view of the above problems, embodiments of this application provide a signal recognition method, device and storage medium to solve the problems of low signal recognition accuracy and difficulty in distinguishing multiple similar target signals in the prior art.

[0007] According to one aspect of the embodiments of this application, a signal recognition method is provided. The method includes: acquiring a target signal sample sequence to be detected, wherein the target signal sample sequence includes a cyclic prefix (CP) sequence, a primary synchronization signal (PSS) sequence, and a secondary synchronization signal (SSS) sequence. The PSS sequence is used to characterize whether the target signal is a communication signal of a target object, the SSS sequence is used to characterize the individual characteristics of the target object corresponding to the target signal, the CP sequence is a header sequence segment of the PSS sequence used to eliminate interference between signals, and the starting positions of the PSS sequence and the SSS sequence in the target signal sample sequence are spaced at a fixed length; finding a first starting position of the CP sequence in the target signal sample sequence; determining a first starting range of the CP sequence based on the first starting position, wherein the first starting range is a position range of a preset length including the first starting position; and determining the starting range from the first starting range. From the multiple starting positions included, an unselected starting position is selected as the second starting position; based on the second starting position, a PSS sequence is determined from the sampled sequence to be detected, and the PSS sequence is corrected for frequency deviation to obtain a PSS corrected sequence; based on the PSS corrected sequence, a first SSS corrected sequence is determined for the SSS sequence after frequency deviation correction; for the first SSS corrected sequence, a search value corresponding to each preset root among multiple preset roots is determined at the second starting position, resulting in multiple search values; based on the multiple starting positions included in the first starting range, a search value corresponding to each preset root at each starting position included in the first starting range is obtained; the preset root corresponding to the maximum value among all search values ​​obtained in the first starting range is taken as the root of the SSS sequence, and the root of the SSS sequence is used as identification information for target recognition.

[0008] In one alternative approach, for the first SSS correction sequence, a search value corresponding to each of the multiple preset roots at the second starting position is determined to obtain multiple search values, including: performing frequency deviation correction on the first SSS correction sequence to obtain a second SSS correction sequence; for the second SSS correction sequence, a search value corresponding to each of the multiple preset roots at the second starting position is determined to obtain multiple search values.

[0009] In one alternative approach, before determining the PSS sequence from the sampled sequence to be detected based on the second starting position, and performing frequency offset correction on the PSS sequence to obtain a PSS-corrected sequence, and before determining the first SSS-corrected sequence of the frequency offset-corrected SSS sequence based on the PSS-corrected sequence, the signal recognition method further includes: acquiring the sampling rate SR, the original time-domain convolutional sequence OCP, and the time-domain symbolic convolutional sequence ZCP of the target signal; determining the first frequency offset of the sampled sequence to be detected based on the sampled sequence to be detected, the first starting position, and SR; determining the PSS sequence from the sampled sequence to be detected based on the second starting position, and performing frequency offset correction on the PSS sequence to obtain a PSS-corrected sequence. The PSS correction sequence, which determines the first SSS correction sequence after frequency deviation correction, includes: determining the second frequency deviation of the sampled sequence to be detected based on the sampled sequence to be detected, the second starting position, OCP, and SR; determining the PSS sequence from the sampled sequence to be detected based on the second starting position; correcting the frequency deviation of the PSS sequence based on the first frequency deviation, the second frequency deviation, and SR to obtain the PSS correction sequence; determining the starting position of the SSS sequence in the sampled sequence to be detected based on the PSS correction sequence and ZCP; and determining the first SSS correction sequence from the sampled sequence to be detected based on the starting position of the SSS sequence in the sampled sequence to be detected.

[0010] In one optional approach, for the first SSS correction sequence, a search value corresponding to each of the multiple preset roots at the second starting position is determined to obtain multiple search values. The approach further includes: performing frequency deviation correction on the first SSS correction sequence based on the first frequency offset, the second frequency offset, and SR to obtain a second SSS correction sequence; for the second SSS correction sequence, a search value corresponding to each of the multiple preset roots at the second starting position is determined to obtain multiple search values.

[0011] In one alternative approach, acquiring the target signal's sampled sequence to be detected includes: determining the bandwidth of the target signal; determining the SR, the original temporal convolutional sequence OCP, and the ZCP based on the bandwidth; and acquiring the target signal's sampled sequence to be detected under the SR.

[0012] In one alternative approach, finding the first starting position of the CP sequence in the sampled sequence to be detected includes: performing PSS detection on the sampled sequence to be detected to obtain the initial starting position of the PSS sequence in the sampled sequence to be detected; determining the second starting range of the PSS sequence based on the initial starting position, wherein the second starting range is a position range of a preset length including the initial starting position; and finding the first starting position of the CP sequence within the second starting range.

[0013] In one alternative approach, performing PSS detection on the sampled sequence to be detected to obtain the initial starting position of the PSS sequence in the sequence to be detected includes: performing a symbolization operation on the sampled sequence to be detected to obtain a symbolized sequence to be detected; performing a convolution operation on the symbolized sequence to be detected with ZCP to obtain a symbolized convolution output sequence; and determining the position of the maximum value in the symbolized convolution output sequence as the initial starting position.

[0014] In one alternative approach, finding the first starting position of the CP sequence within the second starting range includes: performing a convolution operation between the CP tail sequence and the CP head sequence whose starting position is within the second starting range to obtain a CP convolution output sequence, wherein the starting position of the CP tail sequence is spaced at a fixed length from the starting position of the CP head sequence, and the sequence lengths of the CP head sequence, CP tail sequence, and CP sequence are consistent; and determining the position of the maximum value in the CP convolution output sequence as the first starting position.

[0015] According to another aspect of the embodiments of this application, a signal recognition device is provided, including: a processor and a memory, wherein the memory stores executable instructions, and the processor can execute the executable instructions to implement the signal recognition method as described in any of the above.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and when the executable instruction is executed, it can implement the signal recognition method as described in any of the above.

[0017] This application embodiment uses the CP sequence to coarsely locate the starting position of the PSS sequence to obtain a first starting range. Frequency deviation correction is applied to the PSS sequence corresponding to each starting position within the first starting range, and the SSS sequence at that starting position is determined based on the corrected PSS sequence. This yields a more accurate SSS sequence, thereby reducing the impact of frequency deviation on the rooting solution of the SSS sequence. Furthermore, by exhaustively enumerating each starting position within the first starting range, the true starting position of the CP sequence can be determined with greater accuracy within a high confidence range. This makes the estimated starting position of the SSS sequence more accurate, enabling the final rooting solution to be obtained. The obtained rooting can then be used to identify the target signal, thereby improving the accuracy of signal recognition and effectively distinguishing multiple similar target signals.

[0018] 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

[0019] 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:

[0020] Figure 1 A flowchart illustrating the signal recognition method provided in an embodiment of this application is shown;

[0021] Figure 2 A schematic diagram showing the detection results of PSS detection provided in the embodiments of this application is illustrated;

[0022] Figure 3 This diagram illustrates the search results for the first starting position of the CP sequence provided in an embodiment of this application.

[0023] Figure 4 This illustration shows a schematic diagram of preset embedded search results provided in an embodiment of this application;

[0024] Figure 5 A schematic diagram of the structure of the signal recognition device provided in an embodiment of this application is shown;

[0025] Figure 6 A schematic diagram of the structure of the signal recognition device provided in an embodiment of this application is shown. Detailed Implementation

[0026] 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.

[0027] Frequency hopping (FH) is a communication technique in which the carrier frequency of a signal is rapidly switched in a predetermined frequency sequence according to a specific pattern to reduce interference and improve communication robustness. However, this rapid change in signal frequency also presents challenges for signal identification based on spectrum analysis.

[0028] Spectrum analysis is a signal identification technique that identifies targets by analyzing the frequency components of a signal. However, frequency-hopping signals switch rapidly between different frequencies, causing the signal energy to move dynamically across the spectrum. Spectrum analysis is not adept at capturing the dynamic characteristics of signals, such as frequency-hopping patterns. Therefore, spectrum analyzers face difficulties in identifying frequency-hopping signals, resulting in low accuracy. For example, a spectrum analyzer might normally be able to identify a signal transmitted or received by a drone by analyzing its time-frequency characteristics; however, frequency-hopping signals cause discontinuous time-frequency characteristics, making it impossible for the spectrum analyzer to identify the drone from the signal alone.

[0029] Furthermore, since spectrum analysis identifies targets based on time-frequency characteristics, and targets of the same type have consistent time-frequency characteristics, spectrum analysis can identify targets of different types, but cannot further distinguish different individuals of the same type of target, and therefore cannot identify the identity of the target.

[0030] One method for identifying a target is to determine the root value of its secondary synchronization signal (SSS) sequence. The root value of the Zadoff-Chu (ZC) sequence within the SSS is a crucial parameter, uniquely identifying the SSS sequence itself and thus the transmitting device of the target signal. Since frequency hopping does not affect the root value of the SSS sequence, the individual target can be accurately identified using the SSS root value, provided the target signal has no frequency deviation and the starting position of the SSS sequence is accurate. However, in practice, meeting these conditions is quite difficult because frequency deviations often occur during signal transmission, and the starting position of the SSS sequence may also be inaccurate for various reasons.

[0031] Based on this, the inventors of this application discovered that the cyclic prefix (CP) sequence at the head of the Primary Synchronization Signal (PSS) sequence and the cyclic suffix (CS) sequence at the tail of the PSS sequence have good correlation in the time domain. Therefore, the high correlation between the head and tail of the PSS sequence can be used to determine the initial start position of the CP sequence in the sampled sequence to be tested, and then the start position of the SSS sequence can be found through the initial start position of the CP sequence and determined from the sampled sequence to be tested. However, the initial start position of the CP sequence in the sampled sequence to be tested is affected by noise signals, causing a large deviation between the true start position and the initial start position of the CP sequence, resulting in an inaccurate determined start position of the SSS sequence, and thus an inaccurate determined SSS sequence. Therefore, a high-confidence range of starting positions, including the initial starting position, can be determined. The frequency deviation of the PSS sequence corresponding to each starting position within this range is corrected. Then, the SSS sequence is determined based on the corrected PSS sequence. A root search is then performed on the SSS sequence to obtain the search value corresponding to the preset root. All the search values ​​obtained are after frequency deviation correction. The preset root corresponding to the search value with the largest value among the obtained search values ​​is then used as the root of the SSS sequence, ensuring a high probability that the root of the determined SSS sequence corresponds to the true starting position of the CP sequence, and making the result reliable. Furthermore, using the root of the SSS sequence as identification information for target recognition can improve the accuracy of target recognition and effectively distinguish multiple similar target signals.

[0032] This application is applicable to the identification of signals from target objects. It can identify the type of target object and also perform individual identification of multiple similar target objects. For example, when multiple drone signals are received and it is necessary to distinguish between multiple drones, this application can differentiate between different individual drones based on the received signals. The target object can be a drone, vehicle, satellite, ship, or other object emitting identifiable signals.

[0033] Figure 1 A flowchart illustrating a signal recognition method provided in an embodiment of this application is shown. This method is executed by a signal recognition device. The signal recognition device can be a computing device such as a server or calculator, or it can be a signal receiver. Figure 1 As shown, the method includes the following steps:

[0034] S110, acquire the target signal to be detected sampling sequence, wherein the target signal to be detected sampling sequence includes a cyclic prefix (CP) sequence, a primary synchronization signal (PSS) sequence and a secondary synchronization signal (SSS) sequence. The PSS sequence is used to characterize whether the target signal is the communication signal of the target object. The SSS sequence is used to characterize the individual characteristics of the target object corresponding to the target signal. The CP sequence is the head sequence segment of the PSS sequence and is used to eliminate interference between signals. The starting positions of the PSS sequence and the SSS sequence in the target sample sequence are spaced at a fixed length.

[0035] The Primary Synchronization Signal (PSS) sequence is one of the synchronization signals used in LTE (Long Term Evolution) wireless communication systems. The PSS helps mobile devices synchronize time and frequency when receiving signals. The Cyclic Prefix (CP) sequence in the PSS header consists of a repeating subcarrier sequence, giving the CP sequence a cyclic characteristic. This structure provides excellent autocorrelation properties in the time domain, thus improving the signal's resistance to fading. The Secondary Synchronization Signal (SSS) sequence is an auxiliary synchronization signal in LTE systems. In LTE systems, the PSS is used for coarse time and frequency synchronization, while the SSS provides finer time and frequency synchronization. The start positions of the SSS and PSS sequences in the sampled sequence to be detected are spaced at a fixed length, typically 64 sampling points.

[0036] The target signal is sampled by a signal receiver to obtain the sampled sequence to be detected. When the signal identification device is a signal receiver, it obtains the sampled sequence to be detected of the target signal through sampling; when the signal identification device is a computing device, it can communicate with the signal receiver to obtain the sampled sequence to be detected sampled by the signal receiver.

[0037] The signal sampling process of a signal receiver includes the following steps: acquiring target signal locking parameters, such as the center frequency and bandwidth of the radio signal radiated by the target object; configuring the receiving link according to the locking parameters to match the target signal, such as setting the link receiving carrier frequency according to the center frequency of the target signal and setting the sampling rate (SR) of the analog-to-digital converter in the link according to the bandwidth of the UAV signal; and sampling the target signal through the configured receiving link to obtain the target signal's sampled sequence to be detected.

[0038] The obtained center frequency is an estimated frequency, which deviates from the true center frequency of the target signal. Therefore, the sampled sequence to be detected, obtained based on the center frequency, also contains frequency deviation. Consequently, the SSS sequence and its embedding cannot be directly determined from the sampled sequence to be detected.

[0039] In an alternative approach, the signal recognition device can determine the zero-crossings of the convolutional sequence in the time domain (ZCP) used for PSS signal detection based on the bandwidth of the target signal, where ZCP = {z1, z2, ..., z...}. m}, and determine the original convolutional polynomial sequence in the time domain (OCP) used for PSS signal detection based on the bandwidth of the target signal, OCP = {c1, c2, ..., c m} where m is the sequence length of the ZC (Zadoff-Chu) sequence in the PSS sequence. The ZC sequence is a complex sequence, a discrete sequence with good properties, and a special type of linear frequency modulated pulse compression sequence. ZC sequences are commonly used in communication systems for synchronization and channel estimation. OCP refers to the ZC sequence in the PSS sequence; ZCP refers to the sequence generated after symbolic transformation of the ZC sequence in the PSS signal. ZCP is also a complex sequence, and its real and imaginary parts only have three values: 1, -1, and 0.

[0040] When the signal identification device is a signal receiver, it can determine SR, OCP, and ZCP when setting up the receiving link. When the signal identification device is a computing device, SR, OCP, and ZCP can be input as preset parameters to the signal identification device. Alternatively, the signal identification device can obtain SR, OCP, and ZCP from the signal receiver by communicating with it. Or, the signal identification device can determine SR, OCP, and ZCP based on the locking parameters.

[0041] S120, Locate the first starting position of the CP sequence in the sampled sequence to be detected.

[0042] Because CP sequences exhibit cyclic characteristics, they possess excellent autocorrelation properties in the time domain, resulting in a CP sequence at the beginning of a PSS sequence and a cyclic suffix sequence at the end. Since the CP and CS sequences have the same length, there is a high correlation between the fixed-length beginning segment (CP sequence) and the fixed-length end segment (CS sequence) of the PSS sequence.

[0043] In one alternative approach, multiple CP head segments of the same length as the CP sequence can be sequentially taken from the sampled sequence to be detected, and a CP head sequence can be formed from these multiple CP head segments. Then, CP tail segments of fixed length intervals from the CP head segments can be sequentially taken from the sampled sequence to be detected, and the length of the CP tail segments is also the same as the length of the CP sequence. A CP tail sequence can be formed from the CP tail segments that correspond one-to-one with the multiple CP head segments. A convolution operation is performed on the CP head sequence and the CP tail sequence to obtain the CP convolution output sequence. The position of the maximum value in the CP convolution output sequence is determined as the first starting position of the found CP sequence.

[0044] In this process, each head segment in the CP head sequence can be a CP sequence, and similarly, each tail segment in the CP tail sequence can be a CS sequence. The CP convolution output sequence obtained by the convolution operation contains the convolution value corresponding to the starting position of each CP head segment. These convolution values ​​can be used to characterize the correlation between the CP head segment and the CP tail segment at the corresponding starting position. The CP head segment and CP tail segment with the highest correlation can be identified as the CP sequence and the CS sequence, respectively. The characteristics of the CP sequence mean that the first starting position of the found CP sequence is less affected by the center frequency prediction deviation, resulting in high accuracy.

[0045] The starting position of the CP sequence can be represented as the sampling start time of the CP sequence in the sampling sequence to be detected. For example, the starting position of the CP sequence can be the 12th second of sampling the target signal, or it can be represented as the sequence number of the sampling point where the CP sequence starts sampling in the sampling sequence to be detected. For example, the starting position of the CP sequence can be the 12th sampling point of sampling the target signal.

[0046] Finding the first starting position of the CP sequence in the sampled sequence to be detected requires searching the entire sampled sequence, resulting in a large search range. To narrow the search range for the first starting position of the CP sequence, in one optional approach, the above S120 includes the following sub-steps:

[0047] S121, Perform PSS detection on the sampled sequence to be detected to obtain the initial starting position of the PSS sequence in the sequence to be detected.

[0048] Specifically, S121 includes the following sub-steps to complete the PSS detection of the sampled sequence to be detected:

[0049] S121a, perform a symbolization operation on the sampled sequence to be detected to obtain the symbolized sequence to be detected.

[0050] Multiple sample values ​​s in complex form from the sample sequence to be detected iThe real and imaginary parts are symbolized separately to obtain the symbolized sequence F of continuous m points at time i. i ={f i+0 f i+1 , ..., f i+m-1}, where i is the sampling time of the sampled sequence to be detected, specifically, f i Real(f) i ) = sign(real(s i )), f i The imaginary part Imag(f) i ) = sign(imag(s i )).

[0051] S121b, the symbolic sequence to be detected is convolved with ZCP to obtain the symbolic convolution output sequence.

[0052] The formula for convolution is as follows:

[0053]

[0054] Where i is the sampling time of the sampled sequence to be detected, and c i f is a component of the symbolic convolution output sequence. i The symbolic sequence F to be detected i The constituent elements, z i is a component of ZCP, and m is the sequence length of the ZC sequence in the PSS sequence.

[0055] S121c determines the position of the maximum value in the symbolic convolution output sequence as the initial starting position.

[0056] The element c corresponding to the maximum value among the multiple elements contained in the symbolic convolution output sequence. i The time indices i are determined as the initial starting position. In an alternative approach, it is further determined whether the maximum value in the symbolic convolution output sequence is greater than the convolution detection threshold T. c If the value is not greater than 0, it is considered that there is no SSS sequence in the sampled sequence to be detected, and the acquisition of the sampled sequence to be detected of the target signal is restarted.

[0057] Figure 2 A schematic diagram illustrating the detection results of PSS detection provided in an embodiment of this application is shown, as follows. Figure 2 As shown, the maximum value in the symbolic convolution output sequence is 2300, and the position of the maximum value 2300 is at time 4.52. Therefore, time 4.52 can be determined as the initial starting position.

[0058] In S121a to S121c, PSS detection of the sampled sequence to be detected can be completed relatively quickly and accurately, thereby improving the operating efficiency of the signal recognition equipment.

[0059] S123, determine the second starting range of the PSS sequence based on the initial starting position, wherein the second starting range is a position range of a preset length including the initial starting position.

[0060] In one alternative approach, if i is determined as the initial starting position, then the second starting range [ip, i+p] can be determined based on a preset search jitter range ±p. For example, the initial starting position is as follows: Figure 2 As shown in the figure, 4.52, the search jitter range p can be set to 0.2 to obtain the second starting range [4.32, 4.72].

[0061] S125, find the first starting position of the CP sequence within the second starting range.

[0062] The formula for finding the first starting position of the CP sequence is as follows:

[0063]

[0064] Where j is the sampling time of the sampled sequence to be detected, n is the sequence length of the CP sequence, and cp j s is a component element of the output sequence of CP convolution. i For CP header sequence S i The constituent elements, s i+m For the CP tail sequence S i+m The constituent elements, m, are the fixed length of the interval between the start position of the CP tail sequence and the start position of the CP head sequence, which is also the sequence length of the ZC sequence in the PSS sequence.

[0065] Figure 3 This illustration shows a schematic diagram of the search results for the first starting position of the CP sequence provided in an embodiment of this application, as shown below. Figure 3 As shown, the maximum value in the CP convolution output sequence is 2.8, and the position of the maximum value 2.8 is at time 4.4646. Therefore, time 4.4646 can be determined as the first starting position.

[0066] To find the first starting position of the CP sequence within the second starting range, multiple CP head segments with the same length as the CP sequence are sequentially taken from the sampled sequence to be detected. The starting position of the CP head segments is limited to the second starting range. Therefore, by pre-determining the second starting range through PSS detection and then finding the first starting position of the CP sequence within the second starting range, the search range for the first starting position of the CP sequence can be narrowed.

[0067] The initial start position determined by PSS detection is affected by frequency deviation. Therefore, a second start range is determined based on the initial start position to obtain a high-confidence range for the start position of the PSS sequence. Since the CP sequence is the head sequence of the PSS sequence, the start position of the PSS sequence is actually the start position of the CP sequence. By searching for the start position of the CP sequence within the second start range of the PSS sequence, the CP sequence can be quickly located. Furthermore, using the second start range of the PSS sequence can eliminate interference from noise signals that affect the search for the first start position of the CP sequence. These noise signals are located within sampling positions outside the second start range of the sampled sequence to be detected.

[0068] S130, determine the first starting range of the CP sequence according to the first starting position, wherein the first starting range is a position range of a preset length including the first starting position.

[0069] In one alternative approach, if u is determined as the first starting position, then the first starting range [uq, u+q] can be determined based on a preset search jitter range ±q. Here, q can be set according to the signal-to-noise ratio of the signal receiver.

[0070] In a preferred embodiment, by pre-setting the lengths of the first and second starting ranges according to the signal receiver, such that the second starting range encompasses the first starting range, the computational load of the signal identification device in S140–S180 is reduced, thereby improving the operating efficiency of the signal identification device. For example, the initial starting position of the PSS sequence is as follows: Figure 2 As shown in 4.52, the search jitter range p for PSS detection can be set to 0.2, thus obtaining the second starting range [4.32, 4.72], the length of which is 0.4. The first starting position of the CP sequence is then found within [4.32, 4.72], and the obtained first starting position is as follows: Figure 3 The value shown is 4.4646. It is necessary to ensure that the range value of the search jitter range q of the CP sequence is less than the range value of p, so that the second starting range includes the first starting range. For example, q can be set to 0.1 to obtain the first starting range [4.3646, 4.5646].

[0071] S140, select an unselected starting position from the plurality of starting positions included in the first starting range as the second starting position.

[0072] S150, determine the PSS sequence from the sampled sequence to be detected according to the second starting position, and perform frequency deviation correction on the PSS sequence to obtain the PSS correction sequence, and determine the first SSS correction sequence of the SSS sequence after frequency deviation correction according to the PSS correction sequence.

[0073] The true starting position of the CP sequence is most likely located within the first starting range. However, after determining the true position of the CP sequence and the SSS sequence based on it, the SSS sequence will still be inaccurate due to the influence of frequency deviation. Therefore, frequency deviation correction is applied to the estimated SSS sequence corresponding to each starting position within the first starting range.

[0074] To correct for frequency bias in the estimated SSS sequence, in a preferred embodiment, prior to S150, the target identification method includes:

[0075] S129, determine the first frequency offset of the sampled sequence to be detected based on the sampled sequence to be detected, the first starting position, and SR.

[0076] The first frequency offset is the fine frequency offset frq1, which can be determined by the following formula:

[0077]

[0078] Where u is the first starting position, n is the sequence length of the CP sequence, and s u Let u be the sampling point located at position u in the sampling sequence to be detected, m be the sequence length of the ZC sequence of the PSS sequence, and SR be the sampling rate.

[0079] S150 includes the following sub-steps:

[0080] S152, determine the second frequency offset of the sampled sequence to be detected based on the sampled sequence to be detected, the second starting position, OCP and SR.

[0081] The second frequency offset is the coarse frequency compensation frq2, which can be determined by the following formula:

[0082] SQ1={s u+n+0 ,s u+n+1 ,…,s u+n+m-1}

[0083] SQ2 = fftshift(fft(SQ1))

[0084] FZC = fftshift(fft(OCP))

[0085] SQ3=conv(FZC,conj(SQ2(end:-1:1)))

[0086] [~,SQ4] = max(SQ3)

[0087] frq2=(SQ4-m)*(SR / m)

[0088] Where u is the second starting position, n is the sequence length of the CP sequence, m is the sequence length of the ZC sequence of the PSS sequence, and s u Here, u represents the sampling point located at position u in the sampled sequence to be detected, OCP represents the original convolutional sequence in the time domain, and SR represents the sampling rate. fft() refers to performing a fast Fourier transform on the sequence within the brackets, fftshift() refers to adjusting the frequency index of the sequence after the fft operation, changing the arrangement of the sequence from 0 to 2pi to the arrangement from -pi to pi, conj() refers to performing a conjugate operation on the sequence within the brackets, conv() refers to performing a convolution operation on the two sequences within the brackets, and max() refers to performing a maximum value operation on the sequence within the brackets.

[0089] S154, determine the PSS sequence from the sampled sequence to be detected based on the second starting position, and correct the frequency deviation of the PSS sequence based on the first frequency offset, the second frequency offset and SR to obtain the PSS corrected sequence.

[0090] Specifically, the PSS sequence and the PSS correction sequence are determined using the following formula:

[0091] CK={s u+n-v ,s u+n-v+1 ,…,s u+n+v+m-1}

[0092]

[0093] Where CK is the PSS sequence, XZ1 is the PSS correction sequence, and s q Let q be the sampling point located at position q in the sampling sequence to be detected, u be the second starting position, n be the sequence length of the CP sequence, v be the single-sided extension length of the PSS sequence, m be the sequence length of the ZC sequence of the PSS sequence, SR be the sampling rate, frq1 be the fine frequency offset, and frq2 be the coarse frequency compensation.

[0094] S156, Determine the starting position of the SSS sequence in the sampled sequence to be detected based on the PSS correction sequence and ZCP.

[0095] Specifically, the starting position of the SSS sequence in the sampled sequence to be detected is determined by the following formula:

[0096] XZ2=sign(real(XZ1))+1i*sign(imag(XZ1))

[0097] XZ3=conv(XZ2,conj(ZCP(end:-1:1)))

[0098] XZ3 = XZ3(m:end);

[0099] [~, XZ4] = max(XZ3)

[0100] Loc1=u+n-v+XZ4-1

[0101] Loc2 = Loc1 + jnum

[0102] Where Loc1 is the starting position of the PSS sequence, Loc2 is the starting position of the SSS sequence, XZ1 is the PSS correction sequence, ZCP is the temporal symbolic convolution sequence, u is the second starting position, n is the sequence length of the CP sequence, v is the single-sided extension length of the PSS sequence, m is the sequence length of the ZC sequence of the PSS sequence, and jnum is the interval between the PSS and SSS sequences; real() refers to taking the real part of the sequence within the parentheses, imag() refers to taking the imaginary part of the sequence within the parentheses, and sign() refers to performing a symbolic operation on the sequence within the parentheses, where if an element is greater than 0, it is equal to 1, if it is less than 0, it is equal to -1, and if it is equal to 0, it remains 0.

[0103] S158, determine the first SSS correction sequence from the sampled sequence to be detected based on the starting position of the SSS sequence in the sampled sequence to be detected.

[0104] Specifically, the first SSS correction sequence is determined by the following formula:

[0105] SQX1={s Loc2 s Loc2+1 , ..., s Loc2+m-1}

[0106] Where SQX1 is the first SSS correction sequence, Loc2 is the starting position of the SSS sequence, and s q The sampling point located at position q in the sampling sequence to be detected.

[0107] In S152 to S158, the PSS sequence can be effectively corrected by the first frequency offset and the second frequency offset, thereby eliminating the influence of frequency deviation on the SSS sequence and improving the accuracy of signal recognition.

[0108] S160, for the first SSS correction sequence, determine the search value corresponding to each preset root in the multiple preset roots at the second starting position, and obtain multiple search values.

[0109] To further eliminate the influence of frequency deviation in the first SSS correction sequence, frequency deviation correction can also be performed on the first SSS correction sequence. In a preferred embodiment, S160 includes the following sub-steps:

[0110] S162, the first SSS correction sequence is corrected for frequency deviation based on the first frequency offset, the second frequency offset, and SR to obtain the second SSS correction sequence.

[0111] The second SSS correction sequence can be determined using the following formula:

[0112]

[0113] Wherein, SQX2 is the second SSS correction sequence, SQX1 is the first SSS correction sequence, SR is the sampling rate, frq1 is the fine frequency offset, and frq2 is the coarse frequency compensation.

[0114] S164, for the second SSS correction sequence, determine the search value corresponding to each preset root in the multiple preset roots at the second starting position, and obtain multiple search values.

[0115] Specifically, the search value can be determined using the following formula:

[0116] R(u,i)=SQX2*conj(G(i))

[0117] Where R(u, i) is the search value corresponding to the preset root i at the second starting position u, SQX2 is the second SSS correction sequence, and G(i) is the ZC sequence of the SSS sequence rooted at the preset root i.

[0118] By iterating through the range of preset root values ​​i, we obtain the search value R(u, i) corresponding to each preset root i among the multiple preset roots at the second starting position u, thus obtaining multiple search values. In a preferred embodiment, the range of preset root values ​​can be set to [0, 1400].

[0119] In S162 to S164, the corrected SSS sequence can be corrected again by the first frequency offset and the second frequency offset to further eliminate the influence of frequency deviation on the SSS sequence and improve the accuracy of signal recognition.

[0120] S170, based on the multiple starting positions included in the first starting range, obtain the search value corresponding to each preset root at each starting position included in the first starting range.

[0121] In one alternative approach, S140 to S160 are performed on each of the multiple starting positions included in the first starting range to obtain a search value corresponding to each preset root at each starting position included in the first starting range.

[0122] When the search value R(u, i) iterates through the values ​​of multiple starting positions included in the first starting range, and i iterates through the preset root values, the search value matrix R is obtained. (u，i) .

[0123] S180, the preset root corresponding to the maximum value among all search values ​​obtained in the first starting range is taken as the root of the SSS sequence, and the root of the SSS sequence is used as the identification information for target recognition.

[0124] The search value matrix R (u，i) The index i corresponding to the maximum search value is used as the root of the ZC sequence of the SSS sequence, and the i value is used as the identification information for target recognition.

[0125] Figure 4 This illustration shows a schematic diagram of the preset embedded search results provided in an embodiment of this application, such as... Figure 4 As shown, the maximum value in the search is 390, and the preset root corresponding to the maximum value of 390 is 1200. Therefore, 1200 can be determined as the root of the ZC sequence of the SSS sequence.

[0126] This application embodiment uses the CP sequence to coarsely locate the starting position of the PSS sequence to obtain a first starting range. Frequency deviation correction is applied to the PSS sequence corresponding to each starting position within the first starting range, and the SSS sequence at that starting position is determined based on the corrected PSS sequence. This yields a more accurate SSS sequence, thereby reducing the impact of frequency deviation on the rooting solution of the SSS sequence. To further reduce the impact of frequency deviation on the rooting solution of the SSS sequence, after determining the SSS sequence, frequency deviation correction is applied to the determined SSS sequence, and then the root is searched based on the further corrected SSS sequence. By exhaustively enumerating each starting position within the first starting range, the true starting position of the CP sequence can be determined with greater accuracy within a high confidence range, making the estimated starting position of the SSS sequence more accurate. This allows for the final rooting solution, and the target signal can be identified using the solved root, thereby improving the accuracy of signal recognition and effectively distinguishing multiple similar target signals.

[0127] Figure 5 A schematic diagram of the structure of the signal recognition device provided in an embodiment of this application is shown. Figure 5 As shown, the device 200 includes: an acquisition module 210, a search module 220, a first determination module 230, a selection module 240, a first processing module 250, a second determination module 260, a jump module 270, and a second processing module 280.

[0128] The acquisition module 210 is used to acquire the sampled sequence to be detected of the target signal, wherein the sampled sequence to be detected includes a cyclic prefix (CP) sequence, a main synchronization signal (PSS) sequence, and a secondary synchronization signal (SSS) sequence. The CP sequence is the head sequence segment of the PSS sequence, and the starting positions of the PSS sequence and the SSS sequence in the sampled sequence to be detected are spaced at a fixed length.

[0129] The lookup module 220 is used to find the first starting position of the CP sequence in the sampled sequence to be detected;

[0130] The first determining module 230 is used to determine the first starting range of the CP sequence according to the first starting position, wherein the first starting range is a position range of a preset length including the first starting position;

[0131] The selection module 240 is used to select an unselected starting position from a plurality of starting positions included in the first starting range as the second starting position;

[0132] The first processing module 250 is used to determine the PSS sequence from the sampled sequence to be detected according to the second starting position, and to perform frequency deviation correction on the PSS sequence to obtain the PSS corrected sequence, and to determine the first SSS corrected sequence of the SSS sequence after frequency deviation correction according to the PSS corrected sequence.

[0133] The second determining module 260 is used to determine, for the first SSS correction sequence, the search value corresponding to each preset root in the multiple preset roots at the second starting position, and obtain multiple search values;

[0134] The jump module 270 is used to obtain the search value corresponding to each preset root at each starting position included in the first starting range, based on the multiple starting positions included in the first starting range.

[0135] The second processing module 280 is used to take the preset root corresponding to the maximum value among all search values ​​obtained in the first starting range as the root of the SSS sequence, and use the root of the SSS sequence as identification information for target recognition.

[0136] The signal recognition device 200 of this application embodiment also includes other modules for performing the steps of the above-described signal recognition method embodiments, which will not be described in detail here.

[0137] Figure 6 The diagram shows a schematic of the structure of a signal recognition device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the signal recognition device.

[0138] like Figure 6 As shown, the signal recognition device may include: a processor 302, a communications interface 304, a memory 306, and a communications bus 308.

[0139] The processor 302, communication interface 304, and memory 306 communicate with each other via communication bus 308. Communication interface 304 is used to communicate with other network elements, such as clients or other servers. The processor 302 executes program 310, specifically performing the relevant steps described above in the signal recognition method embodiment.

[0140] Specifically, program 310 may include program code, which includes computer-executable instructions.

[0141] Processor 302 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 recognition device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0142] Memory 306 is used to store program 310. Memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0143] This application provides a computer program that can be called by a processor to cause a signal recognition device to execute the signal recognition method in any of the above method embodiments.

[0144] This application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed on a computer, cause the computer to perform the signal recognition method in any of the above method embodiments.

[0145] 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.

[0146] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0147] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various aspects of the invention, features of the embodiments of this application are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of this application. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim.

[0148] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0149] 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 the unit claims enumerating several means, several 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.

Claims

1. A signal recognition method, characterized in that, The method includes: A sampled sequence to be detected of a target signal is obtained, wherein the sampled sequence to be detected includes a cyclic prefix (CP) sequence, a primary synchronization signal (PSS) sequence, and a secondary synchronization signal (SSS) sequence. The PSS sequence is used to characterize whether the target signal is a communication signal of a target object. The SSS sequence is used to characterize the individual characteristics of the target object corresponding to the target signal. The CP sequence is the header sequence segment of the PSS sequence and is used to eliminate interference between signals. The PSS sequence and the SSS sequence are spaced at a fixed length from their starting positions in the sampled sequence to be detected. Find the first starting position of the CP sequence in the sampled sequence to be detected; The first starting range of the CP sequence is determined based on the first starting position, wherein the first starting range is a position range of a preset length including the first starting position; Select an unselected starting position from among the multiple starting positions included in the first starting range as the second starting position; The PSS sequence is determined from the sampled sequence to be detected based on the second starting position, and the frequency deviation of the PSS sequence is corrected to obtain the PSS corrected sequence. The first SSS corrected sequence of the SSS sequence after the frequency deviation correction is determined based on the PSS corrected sequence. For the first SSS correction sequence, determine the search value corresponding to each of the multiple preset roots at the second starting position, and obtain multiple search values; Based on the multiple starting positions included in the first starting range, a search value corresponding to each preset root is obtained at each starting position included in the first starting range; The preset root corresponding to the maximum value among all search values ​​obtained in the first starting range is used as the root of the SSS sequence, and the root of the SSS sequence is used as identification information for target recognition.

2. The method according to claim 1, characterized in that, For the first SSS correction sequence, the search value corresponding to each of the multiple preset roots at the second starting position is determined, resulting in multiple search values, including: The first SSS correction sequence is corrected for frequency deviation to obtain the second SSS correction sequence; For the second SSS correction sequence, determine the search value corresponding to each preset root in the multiple preset roots at the second starting position, and obtain multiple search values.

3. The method according to claim 1, characterized in that, Before determining the PSS sequence from the sampled sequence to be detected based on the second starting position, performing frequency offset correction on the PSS sequence to obtain a PSS corrected sequence, and determining the first SSS corrected sequence of the SSS sequence after frequency offset correction based on the PSS corrected sequence, the method further includes: Obtain the sampling rate SR, the original temporal convolution sequence OCP, and the symbolic temporal convolution sequence ZCP of the target signal; The first frequency offset of the sampling sequence to be detected is determined based on the sampling sequence to be detected, the first starting position, and the SR; The step of determining the PSS sequence from the sampled sequence to be detected based on the second starting position, correcting the frequency deviation of the PSS sequence to obtain a PSS corrected sequence, and determining a first SSS corrected sequence of the SSS sequence after the frequency deviation correction based on the PSS corrected sequence includes: The second frequency offset of the sampling sequence to be detected is determined based on the sampling sequence to be detected, the second starting position, the OCP, and the SR. The PSS sequence is determined from the sampled sequence to be detected based on the second starting position, and the frequency deviation of the PSS sequence is corrected based on the first frequency offset, the second frequency offset, and the SR to obtain the PSS corrected sequence; The starting position of the SSS sequence in the sampled sequence to be detected is determined based on the PSS correction sequence and the ZCP. The first SSS correction sequence is determined from the sampled sequence to be detected based on the starting position of the SSS sequence in the sampled sequence to be detected.

4. The method according to claim 3, characterized in that, The step of determining, for the first SSS correction sequence, a search value corresponding to each preset root among multiple preset roots at the second starting position, and obtaining multiple search values, further includes: The first SSS correction sequence is corrected for frequency deviation based on the first frequency offset, the second frequency offset, and the SR to obtain the second SSS correction sequence. For the second SSS correction sequence, determine the search value corresponding to each preset root in the multiple preset roots at the second starting position, and obtain multiple search values.

5. The method according to claim 3, characterized in that, The sampling sequence to be detected for acquiring the target signal includes: Determine the bandwidth of the target signal; The SR, the original temporal convolutional sequence OCP, and the ZCP are determined based on the bandwidth. Obtain the target signal under the SR and sample the sequence to be detected.

6. The method according to claim 3, characterized in that, The step of finding the first starting position of the CP sequence in the sampled sequence to be detected includes: The initial start position of the PSS sequence in the sampled sequence to be detected is obtained by performing PSS detection on the sampled sequence to be detected. The second starting range of the PSS sequence is determined based on the initial starting position, wherein the second starting range is a position range of a preset length including the initial starting position; The first starting position of the CP sequence is found within the second starting range.

7. The method according to claim 6, characterized in that, The step of performing PSS detection on the sampled sequence to be detected to obtain the initial start position of the PSS sequence in the sampled sequence to be detected includes: The sampled sequence to be detected is symbolized to obtain the symbolized sequence to be detected; The symbolic sequence to be detected is convolved with the ZCP to obtain the symbolic convolution output sequence; The position of the maximum value in the symbolic convolution output sequence is determined as the initial starting position.

8. The method according to claim 6, characterized in that, The step of finding the first starting position of the CP sequence within the second starting range includes: The CP tail sequence is convolved with the CP head sequence whose starting position is within the second starting range to obtain the CP convolution output sequence. The starting position of the CP tail sequence is spaced at a fixed length from the starting position of the CP head sequence, and the sequence lengths of the CP head sequence, the CP tail sequence, and the CP sequence are the same. The position of the maximum value in the CP convolution output sequence is determined as the first starting position.

9. A signal recognition device, characterized in that, include: A processor and a memory, wherein the memory stores executable instructions, and the processor is capable of executing the executable instructions to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores executable instructions, which, when executed on the signal recognition device, cause the signal recognition device to perform the operation of the signal recognition method as described in any one of claims 1 to 8.

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