LTE-OFDM unmanned aerial vehicle signal feature analysis method and device, and storage medium

Abstract

The invention relates to the technical field of signal analysis, and discloses a signal feature analysis method for an LTE-OFDM unmanned aerial vehicle, and the method comprises the steps: obtaining a to-be-detected sampling sequence of an OFDM signal of the LTE-OFDM unmanned aerial vehicle, the OFDM signal having the features of a ZC sequence; searching the initial position of the CP sequence in the to-be-detected sampling sequence; determining a symbol sequence from the to-be-detected sampling sequence according to the initial position of the CP sequence; performing Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence; determining a length range and a root value range of the ZC sequence according to the number of the effective subcarriers; determining a plurality of ZC search sequences according to each length in the length range and each root value in the root value range; and determining the length and the root value corresponding to the ZC search sequence with the maximum correlation value with the to-be-detected sampling sequence in the length range of the sub-sequence in the plurality of ZC search sequences as the characteristic value of the OFDM signal. In this way, the detection performance of the target of the unmanned aerial vehicle which hovers or flies at a low speed is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of signal analysis technology, and specifically to a signal feature analysis method, device, and storage medium for an LTE-OFDM unmanned aerial vehicle. Background Art

[0002] With the development of miniaturization, low altitude, simplification, low technology and low cost, drones have become more and more widely used in civilian applications due to their advantages of standing high, seeing far, flying fast, moving without obstacles, strong adaptability and easy modification, and have fully penetrated into many different fields. With the rapid development and popularization of drones, the development of drone detection technology and countermeasure equipment has become a hot trend at the forefront of science and technology.

[0003] At present, drone detection technology mainly includes four categories: radio spectrum detection, sound signal detection, optoelectronic signal detection and radar detection. Among them, radio spectrum detection identifies targets by analyzing the communication and control signals of drones; sound signal detection relies on the unique soundprints produced by drone engines and rotors; optoelectronic signal detection uses the thermal imaging or visible light characteristics of drones for identification; and radar detection detects the position and speed of drones through reflected radio waves.

[0004] However, when the drone is in a hovering or low-speed state, the above drone detection technology is difficult to effectively detect the hovering or low-speed flying drone target. Summary of the invention

[0005] In view of the above problems, the embodiments of the present application provide a signal feature analysis method, device and storage medium for an LTE-OFDM UAV, which are used to solve the problem in the prior art that it is difficult to effectively detect hovering or low-speed flying UAV targets.

[0006] According to one aspect of an embodiment of the present application, a signal feature analysis method for an LTE-OFDM unmanned aerial vehicle is provided, the method comprising: obtaining a sampling sequence to be detected of an OFDM signal of an LTE-OFDM unmanned aerial vehicle, the OFDM signal having the characteristics of a ZC sequence, the sampling sequence to be detected comprising a symbol sequence, and the symbol sequence comprising a CP sequence; searching for a starting position of the CP sequence in the sampling sequence to be detected; determining the symbol sequence from the sampling sequence to be detected according to the starting position of the CP sequence; performing Fourier transform on the symbol sequence to obtain the number of valid subcarriers of the symbol sequence; determining a length range and a root value range of the ZC sequence according to the number of valid subcarriers; determining multiple ZC search sequences according to each length within the length range and each root value within the root value range; determining the length and root value corresponding to the ZC search sequence having the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among the multiple ZC search sequences as the feature value of the OFDM signal, wherein the sampling sequence to be detected is divided into multiple subsequences according to the length of the symbol sequence.

[0007] In an optional manner, the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among multiple ZC search sequences are determined as the characteristic value of the OFDM signal, further including: starting from the first sampling point of the sampling sequence to be detected, sampling points of the length of the continuous symbol sequence are selected in sequence as the sequence to be searched, to obtain multiple sequences to be searched, wherein, in two adjacent sequences to be searched, the first sampling point of one sequence to be searched is adjacent to the first sampling point of the other sequence to be searched; for each ZC search sequence, the correlation value between the ZC search sequence and each sequence to be searched is calculated in sequence to obtain multiple correlation values; within the length range of each subsequence, the ZC search sequence corresponding to the maximum value among the multiple correlation values ​​is determined as the target ZC search sequence; and the length and root value corresponding to the target ZC search sequence are determined as the characteristic value of the OFDM signal.

[0008] In an optional manner, within the length range of each subsequence, a ZC search sequence corresponding to a maximum value among multiple correlation values ​​is determined as a target ZC search sequence, further comprising: within the length range of each subsequence, determining a target correlation value greater than a preset correlation threshold from multiple correlation values; and determining a ZC search sequence corresponding to a maximum value among the target correlation values ​​as the target ZC search sequence.

[0009] In an optional manner, obtaining a sampling sequence to be detected of an OFDM signal of an LTE-OFDM drone further includes: obtaining the OFDM signal of the LTE-OFDM drone; obtaining the signal bandwidth, subcarrier spacing and sampling time of the OFDM signal; determining the sampling rate of the OFDM signal according to the signal bandwidth and subcarrier spacing; sampling the OFDM signal according to the sampling rate and sampling time to obtain a sampling sequence to be detected, wherein the length of the sampling sequence to be detected is equal to the sampling rate multiplied by the sampling time.

[0010] In an optional manner, searching for the starting position of a CP sequence in a sampling sequence to be detected, and determining a symbol sequence from the sampling sequence to be detected according to the starting position of the CP sequence, further includes: determining the length of the symbol sequence and the length of the CP sequence according to the subcarrier spacing; determining a first sliding window range and a second sliding window range from the sampling sequence to be detected, respectively, wherein the starting position of the first sliding window range is the first sampling point of the sampling sequence to be detected, the length of the interval between the starting position of the first sliding window range and the starting position of the second sliding window range is the length of the symbol sequence, and the length of the first sliding window range and the second sliding window range is the length of the CP sequence; the correlation value calculation step: calculating the first The method comprises the following steps: calculating the correlation values ​​between all sampling points in a sliding window range and all sampling points in a second sliding window range, obtaining the correlation value corresponding to the starting position of the first sliding window range; moving the first sliding window range and the second sliding window range sampling point by sampling point on the sampling sequence to be detected, and executing the correlation value calculation step to obtain multiple correlation values; starting from the first sampling point of the sampling sequence to be detected, determining the starting position of the first sliding window range corresponding to the maximum value of multiple correlation values ​​within the length of each symbol sequence as the starting position of the CP sequence; starting from the starting position of each CP sequence, determining a sequence in the sampling sequence to be detected whose length is the length of the symbol sequence as the symbol sequence, and obtaining multiple symbol sequences.

[0011] In an optional manner, the method also includes: for any two adjacent symbol sequences, determining the fractional frequency offset of the sampling sequence to be detected according to the CP sequence of the previous symbol sequence and the CP sequence of the next symbol sequence; performing fractional frequency offset compensation on the sampling sequence to be detected according to the fractional frequency offset to obtain the sampling sequence to be detected after the fractional frequency offset compensation; determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among multiple ZC search sequences as the characteristic value of the OFDM signal, further including: determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected after the fractional frequency offset compensation within the length range of the subsequence among multiple ZC search sequences as the characteristic value of the OFDM signal.

[0012] In an optional manner, the performing of Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence; determining the length range and root value range of the ZC sequence according to the number of effective subcarriers; determining multiple ZC search sequences according to each length in the length range and each root value in the root value range; determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among the multiple ZC search sequences as the characteristic value of the OFDM signal, further comprising: performing Fourier transform on each of the symbol sequences to obtain the length range and root value of each of the symbol sequences for the OFDM signal. The number of valid subcarriers corresponding to each symbol sequence is determined; the length range and root value range of the ZC sequence corresponding to each symbol sequence are determined according to the number of valid subcarriers corresponding to each symbol sequence; for the length range and root value range of the ZC sequence corresponding to each symbol sequence, multiple ZC search sequences corresponding to each symbol sequence are determined according to each length within the length range and each root value within the root value range; the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of each subsequence among the multiple ZC search sequences corresponding to each symbol sequence are determined as the characteristic value of the OFDM signal.

[0013] In an optional manner, performing Fourier transform on a symbol sequence to obtain the number of effective subcarriers of the symbol sequence further includes: performing Fourier transform on the symbol sequence to obtain a complex number corresponding to each sampling point, wherein the number of transformation points of the Fourier transform is equal to the length of the symbol sequence; determining the effective subcarrier region of the symbol sequence according to the amplitude of the complex number corresponding to each sampling point; and determining the number of effective subcarriers of the symbol sequence according to the sampling points corresponding to the starting position and the ending position of the effective subcarrier region, respectively.

[0014] In an optional manner, after determining the effective subcarrier region of the symbol sequence according to the amplitude of the complex number corresponding to each sampling point, the method also includes: determining the center sampling point of the effective subcarrier region according to the sampling points corresponding to the starting position and the ending position of the effective subcarrier region respectively; calculating the difference between the center sampling point of the effective subcarrier region and the center sampling point of the symbol sequence; determining the integer multiple frequency offset of the sampling sequence to be detected according to the difference and the sampling rate; performing integer multiple frequency offset compensation on the sampling sequence to be detected according to the integer multiple frequency offset to obtain the sampling sequence to be detected after integer multiple frequency offset compensation; determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among multiple ZC search sequences as the characteristic value of the OFDM signal, further including: determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected after integer multiple frequency offset compensation within the length range of the subsequence among multiple ZC search sequences as the characteristic value of the OFDM signal.

[0015] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the signal characteristic analysis method of the LTE-OFDM drone provided in any of the above embodiments.

[0016] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the signal characteristic analysis method of the LTE-OFDM drone provided in any of the above embodiments is implemented.

[0017] The embodiment of the present application determines the symbol sequence from the sampling sequence to be detected by determining the starting position of the CP sequence from the sampling sequence to be detected of the OFDM signal, and then obtains the number of effective subcarriers by Fourier transforming the symbol sequence, and determines the length and root value of the ZC sequence by the number of effective subcarriers. Multiple ZC search sequences can be generated according to the length and root value of the ZC sequence, and then the characteristic value of the OFDM signal can be determined by the correlation value between each ZC search sequence and the sampling sequence to be detected. By performing feature analysis on the OFDM signal to identify the characteristics of the possible ZC sequence, the detection of the drone target is achieved, and the drone target in the hovering or low-speed flight state can be effectively detected, thereby improving the detection capability of the drone. Furthermore, the effective sequence length of the ZC sequence to be searched is determined by the number of effective subcarriers, and then by traversing the root values ​​of all ZC sequences, the ZC sequence matching the OFDM signal can be accurately searched, and the strong autocorrelation feature of the ZC sequence itself is used to improve the detection capability of the drone signal.

[0018] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present application. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0020] Figure 1 A schematic diagram of the flow chart of the signal characteristic analysis method of the LTE-OFDM UAV provided in an embodiment of the present application is shown;

[0021] Figure 2 A schematic diagram showing a CP sliding window result provided in an embodiment of the present application is shown;

[0022] Figure 3A schematic diagram showing a symbol sequence provided by an embodiment of the present application after Fourier transformation is performed;

[0023] Figure 4 A schematic diagram showing a symbol sequence after Fourier transformation after integer frequency offset compensation provided by an embodiment of the present application is shown;

[0024] Figure 5 A schematic diagram showing the correlation value between the ZC search sequence and the sequence to be searched provided in an embodiment of the present application is shown;

[0025] Figure 6 A schematic diagram of the structure of a signal characteristic analysis device for an LTE-OFDM UAV provided in an embodiment of the present application is shown;

[0026] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0028] When a drone is in a hovering or low-speed state, its power output and movement speed are reduced, resulting in insufficient power of the transmitted radio signal. At the same time, the sound generated by the drone's engine and rotor may not be enough to penetrate the surrounding noise at a low speed or stationary state, resulting in the sound signal being masked by environmental noise. In addition, the optoelectronic signal may be difficult to distinguish from the background due to the lack of movement, and a stationary drone is more difficult to be captured by an optoelectronic sensor in a complex background. Radar detection may cause a low Doppler shift due to the slow movement of the drone, making it difficult for the radar system to distinguish the drone from stationary or slow-moving objects. Therefore, it is urgent to design a reliable method to improve the detection capability of drone targets in a hovering or low-speed flight state.

[0029] Orthogonal Frequency Division Multiplexing (OFDM) is a signal transmission technology widely used in modern wireless communication systems. Due to its high spectrum efficiency and strong anti-multipath interference capability, it is widely used in communication systems such as Wi-Fi (IEEE 802.11a / g / n / ac / ax), LTE and 5G. OFDM technology can effectively combat the multipath effect in wireless channels and ensure the reliability of data transmission.

[0030] The ZC sequence (Zadoff-Chu sequence) is a special complex sequence with constant envelope properties and good autocorrelation characteristics. These characteristics make the ZC sequence commonly used in key links such as channel estimation, synchronization and random access in communication systems. In complex multipath and low signal-to-noise ratio (SNR) environments, the ZC sequence can significantly improve system performance, reduce errors, and improve the accuracy of data transmission.

[0031] LTE-OFDM drones refer to drones that use LTE (Long Term Evolution) communication technology and OFDM modulation technology. In OFDM modulation, the introduction of cyclic prefix is ​​to effectively combat the inter-symbol interference caused by multipath effects and maintain the orthogonality between subcarriers, thereby maintaining good communication quality in a multipath environment.

[0032] For LTE-OFDM standard UAVs, the applicant has found that the ZC sequence is usually used as a reference signal, and its main purpose is to achieve synchronization between the UAV and the ground control station or other network elements. After the UAV is turned on, this LTE-OFDM standard UAV will send the ZC sequence as a synchronization signal to synchronize with the ground control station or other network elements to ensure that the UAV can stably access the communication network and maintain reliable data transmission.

[0033] The constant envelope of the ZC sequence, its amplitude remains unchanged during signal transmission. Therefore, when an OFDM signal is detected, the characteristics of the possible ZC sequence can be identified by performing feature analysis on the OFDM signal to determine whether a drone target is detected. This method can effectively detect drone targets in hovering or low-speed flight states and improve the detection capability of drones.

[0034] In the above process, the process of searching the OFDM signal using the generated ZC sequence is involved. In some schemes, the ZC sequence can be generated by a fixed length and a fixed root value range, and then the generated ZC sequence is used to search the OFDM signal to identify the characteristics of the ZC sequence in the OFDM signal. The autocorrelation property of the ZC sequence is highly dependent on the length of the sequence. If the length of the ZC sequence does not match, then even if all the root values ​​of the ZC sequence are traversed, a matching ZC sequence may not be found, which will reduce the accuracy of the OFDM signal feature analysis.

[0035] In the LTE system, the ZC sequence used as a reference signal is usually placed on a specific valid subcarrier in the OFDM symbol. Therefore, by determining the symbol sequence of the OFDM signal and determining the number of valid subcarriers through the symbol sequence, the length and root value of the ZC sequence can be determined by the number of valid subcarriers. Finally, the length of the ZC sequence is used to limit the searched ZC sequence, and by traversing the root values ​​of all ZC sequences, the ZC sequence matching the OFDM signal can be searched more accurately, thereby improving the reliability of the characteristic value of the OFDM signal.

[0036] Figure 1 The flowchart of the signal characteristic analysis method of the LTE-OFDM drone provided in the embodiment of the present application is shown, and the method is executed by an electronic device, such as a server, a computer, a drone detection device, and a drone countermeasure device. Figure 1 As shown, the method comprises the following steps:

[0037] Step 110: Obtain a sampling sequence to be detected of the OFDM signal of the LTE-OFDM drone, the OFDM signal has the characteristics of a ZC sequence, the sampling sequence to be detected includes a symbol sequence, and the symbol sequence includes a CP sequence.

[0038] When the LTE-OFDM drone uses the ZC sequence as a reference signal, the ZC sequence has a specific distribution in the OFDM signal, which allows the linear frequency modulation feature, that is, the feature of the ZC sequence, to be observed in the OFDM signal time-frequency diagram. In addition, in this process, it is also possible to observe that the linear frequency modulation feature repeats periodically, for example, with a period of 20ms.

[0039] The head sequence segment of the symbol sequence, namely the cyclic prefix (CP) sequence, is composed of a subcarrier sequence copied from the tail of the symbol sequence. This structure enables the CP sequence to have excellent autocorrelation properties in the time domain, thereby helping to improve the signal's anti-fading capability.

[0040] Specifically, step 110 includes the following steps:

[0041] Step 111: Obtain the OFDM signal of the LTE-OFDM drone.

[0042] Among them, the device for acquiring OFDM signals may be a signal receiver, a signal analyzer, a drone detection device, etc. For example, after the signal receiver acquires the OFDM signal, it may directly sample the OFDM signal, or send the OFDM signal to a computer, which may sample the OFDM signal.

[0043] Step 112: Obtain the signal bandwidth, subcarrier spacing and sampling time of the OFDM signal.

[0044] The signal bandwidth may be 20 MHz, the subcarrier spacing may be 15 KHz or 30 KHz, and the sampling time may be 40 ms.

[0045] Step 113: Determine the sampling rate of the OFDM signal according to the signal bandwidth and the subcarrier spacing.

[0046] The sampling rate is calculated as follows:

[0047]

[0048] Wherein, Fs represents the sampling rate, BWsize represents the signal bandwidth, D represents the subcarrier spacing, and ceil() represents rounding up.

[0049] For example, when the signal bandwidth is 20 MHz and the subcarrier spacing is 15 KHz, according to the Nyquist theorem that the sampling rate should be at least twice the signal bandwidth, the above formula can be used to calculate that the sampling rate Fs is equal to 30.72 MHz or 61.44 MHz.

[0050] Step 114: Sample the OFDM signal according to the sampling rate and the sampling time to obtain a sampling sequence to be detected, wherein the length of the sampling sequence to be detected is equal to the sampling rate multiplied by the sampling time.

[0051] After the sampling rate is determined, the sampling sequence to be detected can be obtained by configuring the sampling rate of the analog-to-digital converter of the signal receiver and configuring the signal receiver to continuously sample the OFDM signal within the next sampling time.

[0052] The length of the sampling sequence to be detected is the number of sampling points of the sampling sequence to be detected. Each sampling point of the sampling sequence to be detected represents the amplitude of the OFDM signal at a certain sampling moment. The calculation formula for the length of the sampling sequence to be detected is as follows:

[0053] The length of the sampling sequence to be detected = sampling rate / sampling time.

[0054] When the sampling rate is 61.44 MHz and the sampling time is 40 ms, the length of the sampling sequence to be detected is calculated to be equal to 2457600, that is, the number of sampling points of the sampling sequence to be detected is equal to 2457600.

[0055] After the signal receiver samples the OFDM signal to obtain the sampling sequence to be detected, the sampling sequence to be detected can be sent to the drone countermeasure device, computer or server, which will perform feature analysis on the sampling sequence to be detected.

[0056] Step 120: Searching for the starting position of the CP sequence in the sampling sequence to be detected.

[0057] In OFDM signals, a symbol is the basic unit of data transmission. A symbol is usually composed of data sent on multiple subcarriers. An OFDM symbol lasts for a fixed time interval in time, and the data on all subcarriers is sent simultaneously during this time interval.

[0058] The CP is placed at the beginning of the symbol so that the CP can cover the maximum expected delay spread caused by multipath propagation. In this case, the starting position of the CP sequence can be determined from the sample sequence to be detected by calculating the sequence correlation through the CP sequence sliding window, and then the symbol sequence can be determined from the sample sequence to be detected through the starting position of the CP sequence.

[0059] Specifically, step 120 includes the following steps:

[0060] Step 121: Determine the length of the symbol sequence and the length of the CP sequence according to the subcarrier spacing.

[0061] The length of the symbol sequence is the number of sampling points of the symbol sequence, and the length of the CP sequence is the number of sampling points of the CP sequence. The calculation formulas are as follows:

[0062] The length of the symbol sequence = sampling rate / subcarrier spacing,

[0063] The length of the CP sequence = 288 / (subcarrier spacing / 15^e3).

[0064] For example, when the sampling rate is 61.44 MHz and the subcarrier spacing is 15 KHz, the length of the symbol sequence is 4096 and the length of the CP sequence is 288.

[0065] Step 122: Determine a first sliding window range and a second sliding window range respectively from the sampling sequence to be detected, wherein the starting position of the first sliding window range is the first sampling point of the sampling sequence to be detected, the length of the interval between the starting position of the first sliding window range and the starting position of the second sliding window range is the length of the symbol sequence, and the length of the first sliding window range and the second sliding window range is the length of the CP sequence.

[0066] For example, when the number of sampling points of the sampling sequence to be detected is 2457600 and the length of the symbol sequence is 4096, the sampling points within the first sliding window range are 0 to 287, and the length of the first sliding window range, that is, the number of sampling points, is 288. The sampling points within the second sliding window range are 4096 to (4096+287), and the length of the second sliding window range, that is, the number of sampling points, is 288.

[0067] Correlation value calculation step 123: Calculate the correlation values ​​between all sampling points in the first sliding window range and all sampling points in the second sliding window range to obtain the correlation value corresponding to the starting position of the first sliding window range.

[0068] For example, sampling points 0 to 287 within the first sliding window range are multiplied one by one with sampling points 4096 to (4096+287) within the second sliding window range, and then the multiplication results are added to obtain the corresponding correlation value of sampling point 0 within the first sliding window range.

[0069] Step 124: on the sampling sequence to be detected, move the first sliding window range and the second sliding window range sampling point by sampling point, and perform the correlation value calculation step to obtain multiple correlation values.

[0070] After the first sliding window range and the second sliding window range are moved for the first time, the sampling points in the first sliding window range are 1 to 288, and the sampling points in the second sliding window range are 4097 to (4097+287). The sampling points 1 to 288 in the first sliding window range are multiplied one by one with the sampling points 4097 to (4097+287) in the second sliding window range, and the multiplication results are added to obtain the corresponding correlation value of the sampling point 1 in the first sliding window range.

[0071] After moving the first sliding window range and the second sliding window range for the second time, the sampling points within the first sliding window range are 2 to 289, and the sampling points within the second sliding window range are 4098 to (4098+287). The sampling points 2 to 289 within the first sliding window range are multiplied one by one with the sampling points 4098 to (4098+287) within the second sliding window range, and the multiplication results are added to obtain the corresponding correlation value of the sampling point 2 within the first sliding window range.

[0072] After each sampling point moves the first sliding window range and the second sliding window range, the correlation value corresponding to a sampling point is calculated, thereby sequentially calculating the correlation value corresponding to each sampling point in the sampling sequence to be detected. Figure 2 The schematic diagram of the CP sliding window result shown in FIG. 1 shows the correlation value corresponding to each sampling point.

[0073] Step 125: Starting from the first sampling point of the sampling sequence to be detected, the starting position of the first sliding window range corresponding to the maximum value of multiple correlation values ​​within the length of each symbol sequence is determined as the starting position of the CP sequence.

[0074] Specifically, first, within the length of each symbol sequence, determine the maximum value of multiple correlation values ​​corresponding to multiple sampling points one by one, and then determine the sampling point corresponding to the maximum value as the starting position of the CP sequence. For example, within sampling points 0 to 4095, determine that the maximum value of multiple correlation values ​​is 0.97, and the sampling point corresponding to the maximum value 0.97 is 1000, then the sampling point 1000 is the starting position of the CP sequence; within sampling points 4096 to (4096+287), determine that the maximum value of multiple correlation values ​​is 0.98, and the sampling point corresponding to the maximum value 0.98 is 5384, then the sampling point 5384 is the starting position of the CP sequence.

[0075] Through the above steps, the starting positions of multiple CP sequences can be determined in the sampling sequence to be detected.

[0076] Step 130: Determine a symbol sequence from the sample sequence to be detected according to the starting position of the CP sequence.

[0077] Since the starting position of the CP sequence is the starting position of the symbol sequence, after determining the starting position of the CP sequence and the length of the symbol sequence, the symbol sequence can be determined from the sampling sequence to be detected.

[0078] Specifically, after the starting position of each CP sequence is determined in step 125, starting from the starting position of each CP sequence, a sequence in the sampling sequence to be detected whose length is the length of the symbol sequence is determined as a symbol sequence to obtain multiple symbol sequences. For example, when the starting position of the first CP sequence is sampling point 1000, symbol sequence 1 includes sampling points 1000 to 5095; when the starting position of the second CP sequence is sampling point 5382, symbol sequence 2 includes sampling points 5382 to 9477.

[0079] In some embodiments, there may be a slight frequency deviation between the local oscillator of the wireless transmitter and the signal receiver on the drone, which results in a fractional frequency deviation of the OFDM signal's sampling sequence to be detected. Fractional frequency deviation means that the frequency deviation of the sampling sequence to be detected is not an integer multiple relative to the subcarrier spacing. This frequency deviation mainly affects the orthogonality between subcarriers and easily leads to inter-symbol interference (ISI) between subcarriers.

[0080] In order to compensate for the fractional frequency offset, after determining a plurality of symbol sequences from the to-be-detected sampling sequence, the following steps are also included:

[0081] Step 131: for any two adjacent symbol sequences, determine the fractional frequency offset of the sampling sequence to be detected according to the CP sequence of the previous symbol sequence and the CP sequence of the next symbol sequence.

[0082] After determining multiple symbol sequences, the fractional frequency offset of the sampling sequence to be detected is determined according to any two adjacent symbol sequences, wherein the any two adjacent symbol sequences may be symbol sequence 1 and symbol sequence 2, or symbol sequence 2 and symbol sequence 3.

[0083] For example, when the CP sequence 1 of symbol sequence 1 is 1000-1287 and the CP sequence 2 of symbol sequence 2 is 5382-5670, first, the conjugate complex numbers of CP sequence 1 and CP sequence 2 are multiplied and then added to obtain a complex number, the imaginary part of which is the phase difference, and the fractional frequency deviation of the sampling sequence to be detected can be obtained through the phase difference. Specifically, the calculation formula of the fractional frequency deviation of the sampling sequence to be detected is as follows:

[0084]

[0085] in, It represents the fractional frequency deviation of the sampling sequence to be detected, angle() represents the angle operation, cp1 and cp2 represent CP sequence 1 and CP sequence 2 respectively, and conj(cp2) represents the conjugate operation on CP sequence 2.

[0086] Step 132: Perform fractional frequency offset compensation on the sampling sequence to be detected according to the fractional frequency offset to obtain the sampling sequence to be detected after the fractional frequency offset compensation.

[0087] Calculate the fractional frequency deviation of the sampling sequence to be detected Finally, the fractional frequency offset compensation is performed on the sampling sequence to be detected using the following formula:

[0088]

[0089] Among them, f c (t) represents the sampling sequence to be detected after fractional frequency offset compensation, and f(t) represents the sampling sequence to be detected.

[0090] Step 140: Perform Fourier transform on the symbol sequence to obtain the number of effective subcarriers in the symbol sequence.

[0091] The symbol of the OFDM signal includes a cyclic prefix (CP), an effective subcarrier and a protection subcarrier, wherein the effective subcarrier is used to transmit the main part of the data. Corresponding to the sampling sequence to be detected, the symbol sequence includes a CP sequence, an effective subcarrier region and a protection subcarrier region, wherein the number of sampling points included in the effective subcarrier region is the number of effective subcarriers. After Fourier transforming the symbol sequence, the effective subcarrier region usually has higher energy, while the energy of the CP sequence and the protection subcarrier region is usually lower. Therefore, the number of effective subcarriers can be determined from the symbol sequence by energy distribution.

[0092] Specifically, step 140 includes the following steps:

[0093] Step 141: Perform Fourier transform on the symbol sequence to obtain a complex number corresponding to each sampling point, wherein the number of transformation points of the Fourier transform is equal to the length of the symbol sequence.

[0094] In this step, when the length of the symbol sequence is 4096, the number of transformation points of the Fourier transform is 4096. After the symbol sequence is Fourier transformed, the complex numbers corresponding to the 4096 sampling points are obtained. For example, after the symbol sequence 1 is Fourier transformed, the complex numbers corresponding to each sampling point in the 1000-5095 sampling points are obtained.

[0095] Step 142: Determine the effective subcarrier region of the symbol sequence according to the amplitude of the complex number corresponding to each sampling point.

[0096] In this embodiment, the energy of a complex number generally refers to the square of its amplitude. Therefore, in this step, the amplitude of the complex number corresponding to each sampling point is first calculated, and then the effective subcarrier region is determined according to the amplitude.

[0097] Step 143: Determine the number of valid subcarriers of the symbol sequence according to the sampling points corresponding to the start position and the end position of the valid subcarrier region.

[0098] The calculation formula for the number of effective subcarriers N of the symbol sequence is as follows:

[0099] N = the end position of the effective subcarrier region - the start position of the effective subcarrier region.

[0100] For example, Figure 3 The schematic diagram of the symbol sequence after Fourier transform is shown in the figure, the ordinate represents the amplitude, and the abscissa represents the length of the symbol sequence. It can be seen from the figure that the amplitudes of sampling points 0 to 1482 and 2682 to 4096 are below 10, and the amplitudes of sampling points 1482 to 2682 are above 100. Therefore, it can be determined that the starting position of the effective subcarrier area is sampling point 1482, and the ending position is sampling point 2682, so as to calculate the number of effective subcarriers N of the symbol sequence:

[0101] N=2682-1482=1200.

[0102] In some embodiments, the frequency offset of the signal receiver or the delay spread of the radio channel exceeds the CP interval, which may cause an integer multiple frequency deviation of the sampling sequence to be detected of the OFDM signal. Integer multiple frequency deviation means that the frequency deviation of the sampling sequence to be detected is an integer multiple relative to the subcarrier interval. This frequency deviation mainly affects the orthogonality between subcarriers and is prone to cause inter-carrier interference (ICI) within the subcarrier.

[0103] In order to compensate for the integer frequency offset, after determining the effective subcarrier region in step 142, the following steps are also included:

[0104] Step 142a: Determine the central sampling point of the effective subcarrier region according to the sampling points corresponding to the start position and the end position of the effective subcarrier region respectively.

[0105] Specifically, the calculation formula of the center sampling point f of the effective subcarrier area is as follows:

[0106] f=(starting position of the effective subcarrier region+ending position of the effective subcarrier region) / 2.

[0107] like Figure 3 As shown, the central sampling point f of the effective subcarrier area is calculated:

[0108] f = (2682-1482) / 2 = 2082.

[0109] Step 142b: Calculate the difference between the central sampling point of the effective subcarrier region and the central sampling point of the symbol sequence.

[0110] The length of the symbol sequence is 4096, so the central sampling point f mid = 2049 (Z2 = 4096 / 2 + 1), from which the center sampling point f of the effective subcarrier region and the center sampling point f of the symbol sequence are calculated mid The difference between mid )for:

[0111] ff mid =2082-2049=33.

[0112] Step 142c: Determine the integer multiple frequency offset of the sampling sequence to be detected according to the difference and the sampling rate.

[0113] The calculation formula of the integer frequency deviation Δf of the sampling sequence to be detected is as follows:

[0114] Δf=(ff mid )*scs / samplerate,

[0115] Among them, scs represents the subcarrier spacing, and samplerate represents the sampling rate.

[0116] Step 142d: Perform integer frequency offset compensation on the sampling sequence to be detected according to the integer frequency offset to obtain the sampling sequence to be detected after integer frequency offset compensation.

[0117] After the integer frequency offset Δf of the sampling sequence to be detected is calculated, the integer frequency offset compensation is performed on the sampling sequence to be detected using the following formula:

[0118] f C (t) = f(t)e j2πΔft ,

[0119] Among them, f C (t) represents the sampling sequence to be detected after integer frequency offset compensation, and f(t) represents the sampling sequence to be detected.

[0120] like Figure 4 The figure shows a schematic diagram of the symbol sequence after Fourier transform after integer frequency offset compensation. As shown in the figure, after integer frequency offset compensation is performed on the sampling sequence to be detected, the center sampling point of the effective subcarrier area is 2049, which is the same as the center sampling point of the symbol sequence. Therefore, the frequency offset of the sampling sequence to be detected is effectively compensated.

[0121] Step 150: Determine the length range and root value range of the ZC sequence according to the number of effective subcarriers.

[0122] In the LTE system, the reference signal generated by the ZC sequence is usually placed in the effective subcarriers in the symbols of the OFDM signal. Therefore, the length of the ZC sequence is usually the number of effective subcarriers, so that all effective subcarriers of the OFDM signal can be completely covered. Taking into account the influence of frequency offset or other system parameters, the number of effective subcarriers actually used is different. The length of the ZC sequence used in this application has a length range, that is, the length of the ZC sequence starts from the number of effective subcarriers and includes more lengths of ZC sequences.

[0123] The root values ​​of different ZC sequences are traversed to find the best ZC sequence with the best autocorrelation characteristics under a given system configuration. The traversal range is from 1 to the number of effective subcarriers because the root value of the ZC sequence is usually related to the sequence length, and in the LTE system, the root value usually does not exceed the number of effective subcarriers.

[0124] In summary, the length range of the ZC sequence is: [valid number of subcarriers: valid number of subcarriers + deta], deta>0, and the root value range of the ZC sequence is: [1: valid number of subcarriers]. For example, when the number of valid subcarriers is 1200 and deta is 3, the length range of the ZC sequence is: [1200:1203], and the root value range of the ZC sequence is: [1:1200], where the length range of the ZC sequence includes 4 lengths and the root value range of the ZC sequence includes 1200 root values.

[0125] Step 160: Determine a plurality of ZC search sequences according to each length in the length range and each root value in the root value range.

[0126] In the embodiment of the present application, the total length of each generated ZC search sequence is equal to the length of the symbol sequence, and the effective sequence length depends on each length included in the length range of the ZC sequence, that is, in each ZC search sequence, except for the effective sequence, the amplitudes corresponding to other sequences are assigned 0.

[0127] The formula for the standard ZC sequence is as follows:

[0128]

[0129] Among them, length represents the length of the ZC sequence, and root represents the root value of the ZC sequence.

[0130] Each length of the ZC sequence can be permuted and combined with each root value of the ZC sequence, and then substituted into the above formula of the ZC sequence to obtain multiple ZC search sequences.

[0131] For example, when the length range of the ZC sequence is: [1200:1203], and the root value range of the ZC sequence is: [1:1200], substituting the length of the ZC sequence and the root value (1200,1) into the formula of the ZC sequence, a ZC search sequence 1 with a total length of 4096 can be obtained, and the effective sequence length of the ZC search sequence 1 is 1200; substituting the length of the ZC sequence and the root value (1201,1) into the formula of the ZC sequence, a ZC search sequence 2 with a total length of 4096 can be obtained, and the effective sequence length of the ZC search sequence 2 is 1201; substituting the length of the ZC sequence and the root value (1200,1200) into the formula of the ZC sequence, a ZC search sequence 3 with a total length of 4096 can be obtained, and the effective sequence length of the ZC search sequence 3 is 1200.

[0132] Step 170: Determine the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among multiple ZC search sequences as the characteristic value of the OFDM signal, wherein the sampling sequence to be detected is divided into multiple subsequences according to the length of the symbol sequence.

[0133] For example, when the length of the symbol sequence is 4096, the length of each subsequence is 4096, that is, 0 to 4095 is subsequence 1, 4096 to 8191 is subsequence 2, 8182 to 12287 is subsequence 3, and so on.

[0134] When the LTE system uses one of the multiple ZC search sequences as a reference signal, the correlation value between the ZC search sequence and the sampling sequence to be detected is the largest within the length range of each subsequence. Therefore, by calculating the correlation value between the ZC search sequence and the sampling sequence to be detected, and then determining the ZC search sequence with the largest correlation value within the length range of the subsequence, it can be determined that the received OFDM signal contains the ZC search sequence, so that the length and root value corresponding to the ZC search sequence are determined as the characteristic value of the OFDM signal.

[0135] After the fractional frequency offset compensation is performed on the sampling sequence to be detected in step 132, the correlation values ​​between the multiple ZC search sequences and the sampling sequence to be detected after the fractional frequency offset compensation are calculated in step 170, and then the ZC search sequence with the largest correlation value within the length range of the subsequence is determined, and finally the length and root value corresponding to the ZC search sequence are determined as the characteristic value of the OFDM signal.

[0136] After integer frequency offset compensation is performed on the sampling sequence to be detected in step 142d, correlation values ​​between multiple ZC search sequences and the sampling sequence to be detected after integer frequency offset compensation are calculated in step 170, and then the ZC search sequence with the largest correlation value within the length range of the subsequence is determined, and finally the length and root value corresponding to the ZC search sequence are determined as the characteristic value of the OFDM signal.

[0137] When the characteristic value of the OFDM signal is determined, it indicates that the drone target has been detected. After that, the drone target can be tracked according to the characteristic value.

[0138] Specifically, step 170 includes the following steps:

[0139] Step 171: Starting from the first sampling point of the sampling sequence to be detected, sampling points of the length of the continuous symbol sequence are selected in sequence as the sequence to be searched, to obtain multiple sequences to be searched, wherein, in two adjacent sequences to be searched, the first sampling point of one sequence to be searched is adjacent to the first sampling point of the other sequence to be searched.

[0140] The length of each sequence to be searched and the total length of the ZC search sequence are both the length of the symbol sequence, that is, 4096. When the length of the sampling sequence to be detected is 2457600, multiple sequences to be searched with a length of 4096 can be selected from the sampling sequence to be detected, for example: 0-4095, 1-4096, 2-4097, 3-4098, ..., 2453505-2457600.

[0141] Step 172: For each ZC search sequence, calculate the correlation value between the ZC search sequence and each sequence to be searched in turn to obtain multiple correlation values.

[0142] For example, for ZC search sequence 1, correlation values ​​between ZC search sequence 1 and each sequence to be searched are calculated in sequence, thereby obtaining multiple correlation values ​​corresponding to ZC search sequence 1.

[0143] Step 173: within the length range of each subsequence, determine a ZC search sequence corresponding to the maximum value among multiple correlation values, and obtain a ZC search sequence with the maximum correlation value corresponding to the length range of each subsequence.

[0144] For example, within the length range of subsequence 1, the maximum correlation value is 0.95, and its corresponding ZC search sequence is ZC search sequence 50. Then, within the length range of subsequence 1, the ZC search sequence with the maximum corresponding correlation value is ZC search sequence 50.

[0145] Step 174: If the ZC search sequences with the largest correlation values ​​corresponding to the length ranges of the multiple subsequences are the same ZC search sequence, the ZC search sequence with the largest correlation value corresponding to the length range of the subsequence is determined as the target ZC search sequence.

[0146] When the LTE system uses the same ZC sequence as a reference signal, the ZC search sequence with the largest correlation value within the length range of each subsequence is the same ZC search sequence. For example, the ZC search sequence with the largest correlation value within the length range of subsequence 1 is 50, and the ZC search sequence with the largest correlation value within the length range of subsequence 2 is also ZC search sequence 50... In this case, ZC search sequence 50 is determined as the target ZC search sequence.

[0147] Step 175: Determine the length and root value corresponding to the target ZC search sequence as the characteristic value of the OFDM signal.

[0148] In this step, the characteristic value of the OFDM signal is the length corresponding to the ZC search sequence 50 and the root value (1200, 60).

[0149] For example, after calculating the correlation values ​​between all ZC search sequences and the sequence to be searched, we can get Figure 5 The correlation value diagram shown in the figure shows that the horizontal axis represents the change in the length of the ZC search sequence, the vertical axis represents the root value of the ZC search sequence, and the vertical axis represents the correlation value. As shown in the figure, the maximum correlation value is 0.869732, and the length of the ZC search sequence corresponding to the correlation value is (the number of effective subcarriers + 3), and the corresponding root value of the ZC search sequence is 37. When the number of effective subcarriers is 1200, the characteristic value of the OFDM signal is (1203, 37).

[0150] In some cases, the correlation value between the target ZC search sequence and the sequence to be searched may be less than 0.6, which indicates that the similarity between the target ZC search sequence and the sequence to be searched is low, that is, the LTE system does not use the target ZC search sequence to generate the OFDM signal. In this case, determining the ZC search sequence corresponding to the maximum value among multiple correlation values ​​as the target ZC search sequence may lead to incorrect determination of the characteristic value of the OFDM signal, reducing the accuracy of drone target detection.

[0151] In order to solve the above problem, step 173 includes the following steps:

[0152] Step 173a: Within the length range of each subsequence, determine a target correlation value greater than a preset correlation threshold from multiple correlation values.

[0153] The preset correlation threshold may be 0.6. For example, within the length range of subsequence 1, after calculating the correlation value 1 between the ZC search sequence 1 and the first sequence to be searched, it is determined whether the correlation value 1 is greater than the preset correlation threshold, if so, the correlation value 1 is determined as the target correlation value, if not, the correlation value 2 between the ZC search sequence 1 and the second sequence to be searched is calculated, and then the correlation value 2 is compared with the preset correlation threshold.

[0154] Through this step, within the length range of each subsequence, multiple target correlation values ​​can be determined from multiple correlation values, that is, within the length range of each subsequence, multiple ZC search sequences with a high degree of similarity to the sequence to be searched can be determined from multiple ZC search sequences.

[0155] Step 173b: Determine the ZC search sequence corresponding to the maximum value in the target correlation value as the ZC search sequence with the maximum corresponding correlation value within the length range of each subsequence.

[0156] After determining the target correlation value within the length range of each subsequence, the ZC search sequence corresponding to the maximum value among the target correlation values ​​within the length range of each subsequence has the highest similarity to the sequence to be searched. Therefore, the ZC search sequence corresponding to the maximum value among the target correlation values ​​within the length range of each subsequence is determined as the ZC search sequence with the largest corresponding correlation value within the length range of each subsequence.

[0157] Through step 173a and step 173b, it can be ensured that the target ZC search sequence is a ZC search sequence with a high degree of similarity to the sampling sequence to be detected, so that the characteristic value of the determined OFDM signal is more accurate, thereby improving the detection capability of the drone target.

[0158] In other embodiments, when the LET system uses different ZC sequences as reference signals in a periodic manner, the ZC search sequence with the largest correlation value corresponding to the length range of the subsequence appears in a periodic manner. In this case, after determining multiple symbol sequences from the sampling sequence to be detected according to the starting position of each CP sequence, Fourier transform is performed on each of the symbol sequences to obtain the number of valid subcarriers corresponding to each of the symbol sequences. Then, the length range and root value range of the ZC sequence corresponding to each of the symbol sequences are determined according to the number of valid subcarriers corresponding to each of the symbol sequences. After that, for the length range and root value range of the ZC sequence corresponding to each of the symbol sequences, multiple ZC search sequences corresponding to each of the symbol sequences are determined according to each length within the length range and each root value within the root value range. Finally, the correlation values ​​between the multiple ZC search sequences corresponding to each of the symbol sequences and the sequence to be searched are calculated, and the ZC search sequence with the largest correlation value corresponding to the length range of each subsequence is further determined, and finally the length and root value corresponding to each ZC search sequence with the largest correlation value are determined as the characteristic value of the OFDM signal. In this way, the characteristics of all ZC sequences existing in the OFDM signal can be identified, and the detection capability of the drone signal can be improved.

[0159] The embodiment of the present application determines the symbol sequence from the sampling sequence to be detected by determining the starting position of the CP sequence from the sampling sequence to be detected of the OFDM signal, and then obtains the number of effective subcarriers by Fourier transforming the symbol sequence, and determines the length and root value of the ZC sequence by the number of effective subcarriers. Multiple ZC search sequences can be generated according to the length and root value of the ZC sequence, and then the characteristic value of the OFDM signal can be determined by the correlation value between each ZC search sequence and the sampling sequence to be detected. By performing feature analysis on the OFDM signal to identify the characteristics of the possible ZC sequence, the detection of the drone target is achieved, and the drone target in the hovering or low-speed flight state can be effectively detected, thereby improving the detection capability of the drone. Furthermore, the effective sequence length of the ZC sequence to be searched is determined by the number of effective subcarriers, and then by traversing the root values ​​of all ZC sequences, the ZC sequence matching the OFDM signal can be accurately searched, and the strong autocorrelation feature of the ZC sequence itself is used to improve the detection capability of the drone signal.

[0160] Figure 6 The structure diagram of the signal characteristic analysis device of the LTE-OFDM UAV provided in the embodiment of the present application is shown. Figure 6As shown, the device 200 includes: an acquisition module 210, a search module 220, a first determination module 230, a Fourier transform module 240, a second determination module 250, a third determination module 260 and a fourth determination module 270. The acquisition module 210 is used to acquire the sampling sequence to be detected of the OFDM signal of the LTE-OFDM drone, the OFDM signal has the characteristics of a ZC sequence, the sampling sequence to be detected includes a symbol sequence, and the symbol sequence includes a CP sequence; the search module 220 is used to find the starting position of the CP sequence in the sampling sequence to be detected; the first determination module 230 is used to determine the symbol sequence from the sampling sequence to be detected according to the starting position of the CP sequence; the Fourier transform module 240 is used to perform Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence; the second determination module 250 is used to determine the length range and root value range of the ZC sequence according to the number of effective subcarriers; the third determination module 260 is used to determine multiple ZC search sequences according to each length in the length range and each root value in the root value range; the fourth determination module 270 is used to determine the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected among the multiple ZC search sequences as the characteristic value of the OFDM signal.

[0161] The signal characteristic analysis device 200 of the LTE-OFDM UAV in the embodiment of the present application also includes other modules for executing the various steps of the above-mentioned LTE-OFDM UAV signal characteristic analysis method embodiment, which will not be described one by one here.

[0162] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown, and the specific embodiment of the present application does not limit the specific implementation of the electronic device.

[0163] like Figure 7 As shown, the electronic device may include: a processor (processor) 302 and a memory (memory) 304.

[0164] The memory 304 is used to store a computer program 306. The memory 304 may include a high-speed RAM memory, or may also include a non-volatile memory, such as at least one disk memory. The computer program 306 may include computer executable instructions.

[0165] The processor 302 is used to execute the computer program 306 to implement the above-mentioned signal characteristic analysis method embodiment of the LTE-OFDM drone.

[0166] The processor 302 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the electronic device may be processors of the same type, such as one or more CPUs, or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0167] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the signal characteristic analysis method embodiment of the above-mentioned LTE-OFDM drone is implemented.

[0168] An embodiment of the present application provides a computer program, which can be executed by a processor to implement the above-mentioned LTE-OFDM UAV signal characteristic analysis method embodiment.

[0169] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the signal characteristic analysis method embodiment of the above-mentioned LTE-OFDM drone is implemented.

[0170] In several embodiments provided in the present application, if any function is implemented in the form of a software function module / unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, part or all of the technical solution of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or other electronic device) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (RandomAccess Memory, RAM), disk or optical disk and other media that can store computer program code.

[0171] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0172] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the claims that list several devices, several units or modules in these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

[0173] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A signal feature analysis method for LTE-OFDM UAV, characterized in that: The method comprises: Acquire a sampling sequence to be detected of an OFDM signal of an LTE-OFDM drone, wherein the OFDM signal has a feature of a ZC sequence, the sampling sequence to be detected includes a symbol sequence, and the symbol sequence includes a CP sequence; Searching for the starting position of the CP sequence in the sampling sequence to be detected; Determining the symbol sequence from the to-be-detected sampling sequence according to the starting position of the CP sequence; Performing Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence; Determine the length range and root value range of the ZC sequence according to the number of valid subcarriers; Determine a plurality of ZC search sequences according to each length within the length range and each root value within the root value range; The length and root value corresponding to the ZC search sequence having the largest correlation value with the sampling sequence to be detected within the length range of the subsequence among the multiple ZC search sequences are determined as the characteristic value of the OFDM signal, wherein the sampling sequence to be detected is divided into the multiple subsequences according to the length of the symbol sequence.

2. The method according to claim 1, characterized in that The step of determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the to-be-detected sampling sequence within the length range of the subsequence among the plurality of ZC search sequences as the characteristic value of the OFDM signal further comprises: Starting from the first sampling point of the sampling sequence to be detected, sampling points of the length of the symbol sequence are selected in sequence as sequences to be searched, to obtain multiple sequences to be searched, wherein, in two adjacent sequences to be searched, the first sampling point of one sequence to be searched is adjacent to the first sampling point of the other sequence to be searched; For each of the ZC search sequences, sequentially calculating correlation values ​​between the ZC search sequence and each of the sequences to be searched, to obtain multiple correlation values; Determine a ZC search sequence corresponding to a maximum value among a plurality of correlation values ​​within the length range of each subsequence, and obtain a ZC search sequence with a maximum correlation value corresponding to the length range of each subsequence; If the ZC search sequences with the largest correlation values ​​respectively corresponding to the length ranges of the multiple subsequences are the same ZC search sequence, then the ZC search sequence with the largest correlation value corresponding to the length range of the subsequences is determined as the target ZC search sequence; The length and root value corresponding to the target ZC search sequence are determined as characteristic values ​​of the OFDM signal.

3. The method according to claim 2, characterized in that The method further comprises: determining a ZC search sequence corresponding to a maximum value among a plurality of correlation values ​​within the length range of each subsequence, and obtaining a ZC search sequence with a maximum correlation value corresponding to the length range of each subsequence. Within the length range of each subsequence, determining a target correlation value greater than a preset correlation threshold from a plurality of correlation values; The ZC search sequence corresponding to the maximum value in the target correlation value is determined as the ZC search sequence with the maximum corresponding correlation value within the length range of each subsequence.

4. The method according to claim 1, characterized in that: The method of obtaining a sampling sequence to be detected of an OFDM signal of an LTE-OFDM drone further includes: Obtaining the OFDM signal of the LTE-OFDM UAV; Obtaining a signal bandwidth, a subcarrier spacing, and a sampling time of the OFDM signal; Determining a sampling rate of the OFDM signal according to the signal bandwidth and the subcarrier spacing; The OFDM signal is sampled according to the sampling rate and the sampling time to obtain the sampling sequence to be detected, wherein the length of the sampling sequence to be detected is equal to the sampling rate multiplied by the sampling time.

5. The method according to claim 4, characterized in that The step of searching for a starting position of the CP sequence in the sampling sequence to be detected, and determining the symbol sequence from the sampling sequence to be detected according to the starting position of the CP sequence, further comprises: Determine the length of the symbol sequence and the length of the CP sequence according to the subcarrier spacing; Determine a first sliding window range and a second sliding window range respectively from the sampling sequence to be detected, wherein the starting position of the first sliding window range is the first sampling point of the sampling sequence to be detected, the length of the interval between the starting position of the first sliding window range and the starting position of the second sliding window range is the length of the symbol sequence, and the lengths of the first sliding window range and the second sliding window range are the lengths of the CP sequence; Correlation value calculation step: calculating the correlation values ​​between all sampling points within the first sliding window range and all sampling points within the second sliding window range, and obtaining the correlation value corresponding to the starting position of the first sliding window range; On the sampling sequence to be detected, the first sliding window range and the second sliding window range are moved sampling point by sampling point, and the correlation value calculation step is performed to obtain a plurality of correlation values; Starting from the first sampling point of the sampling sequence to be detected, determining the starting position of the first sliding window range corresponding to the maximum value of multiple correlation values ​​within the length of each symbol sequence as the starting position of the CP sequence; Starting from the starting position of each of the CP sequences, a sequence in the to-be-detected sampling sequence whose length is the length of the symbol sequence is determined as the symbol sequence, to obtain a plurality of the symbol sequences.

6. The method according to claim 5, characterized in that The method further comprises: For any two adjacent symbol sequences, determining the fractional frequency offset of the sampling sequence to be detected according to the CP sequence of the previous symbol sequence and the CP sequence of the next symbol sequence; Performing fractional frequency offset compensation on the sampling sequence to be detected according to the fractional frequency offset to obtain a sampling sequence to be detected after fractional frequency offset compensation; The step of determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the to-be-detected sampling sequence within the length range of the subsequence among the plurality of ZC search sequences as the characteristic value of the OFDM signal further comprises: The length and root value corresponding to the ZC search sequence having the largest correlation value with the to-be-detected sampling sequence after fractional frequency offset compensation within the length range of the subsequence among the multiple ZC search sequences are determined as the characteristic value of the OFDM signal.

7. The method according to claim 5, characterized in that The method further comprises: performing Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence; determining the length range and root value range of the ZC sequence according to the number of effective subcarriers; determining multiple ZC search sequences according to each length in the length range and each root value in the root value range; and determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the sampling sequence to be detected within the length range of the subsequence as the characteristic value of the OFDM signal. Performing Fourier transform on each of the symbol sequences to obtain the number of effective subcarriers corresponding to each of the symbol sequences; Determine the length range and root value range of the ZC sequence corresponding to each of the symbol sequences according to the number of valid subcarriers corresponding to each of the symbol sequences; For a length range and a root value range of a ZC sequence corresponding to each of the symbol sequences, determine a plurality of ZC search sequences corresponding to each of the symbol sequences according to each length within the length range and each root value within the root value range; The length and root value corresponding to the ZC search sequence having the largest correlation value with the to-be-detected sampling sequence within the length range of each subsequence among the multiple ZC search sequences corresponding to each of the symbol sequences are determined as the characteristic value of the OFDM signal.

8. The method according to claim 1, characterized in that The performing Fourier transform on the symbol sequence to obtain the number of effective subcarriers of the symbol sequence further includes: Performing Fourier transform on the symbol sequence to obtain a complex number corresponding to each sampling point, wherein the number of transformation points of the Fourier transform is equal to the length of the symbol sequence; Determining a valid subcarrier region of the symbol sequence according to the amplitude of the complex number corresponding to each sampling point; The number of valid subcarriers of the symbol sequence is determined according to sampling points corresponding to the start position and the end position of the valid subcarrier region respectively.

9. The method according to claim 8, characterized in that After determining the effective subcarrier region of the symbol sequence according to the amplitude of the complex number corresponding to each sampling point, the method further includes: Determine the central sampling point of the effective subcarrier region according to the sampling points corresponding to the starting position and the ending position of the effective subcarrier region respectively; Calculating a difference between a central sampling point of the effective subcarrier region and a central sampling point of the symbol sequence; Determine the integer multiple frequency deviation of the sampling sequence to be detected according to the difference and the sampling rate; Performing integer frequency offset compensation on the sampling sequence to be detected according to the integer frequency offset to obtain the sampling sequence to be detected after integer frequency offset compensation; The step of determining the length and root value corresponding to the ZC search sequence with the largest correlation value with the to-be-detected sampling sequence within the length range of the subsequence among the plurality of ZC search sequences as the characteristic value of the OFDM signal further comprises: The length and root value corresponding to the ZC search sequence having the largest correlation value with the sampling sequence to be detected after integer frequency offset compensation within the length range of the subsequence among the multiple ZC search sequences are determined as the characteristic value of the OFDM signal.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the signal characteristic analysis method of the LTE-OFDM unmanned aerial vehicle according to any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the signal characteristic analysis method of the LTE-OFDM unmanned aerial vehicle described in any one of claims 1 to 9 is implemented.

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