Unmanned aerial vehicle remoteID receiving method with anti-interference function

Abstract

The application provides a remote ID receiving method of a UAV with an anti-interference function, relates to the technical field of UAV communication, and comprises the following steps: sampling a strong interference frequency band signal, performing carrier detection to obtain a signal power spectrum, starting preamble detection to identify a signal type at a power maximum frequency point, adopting a corresponding frequency domain equalization demodulation mode, performing signal reconstruction to calculate a minimum norm and a channel response matrix, removing a reconstructed signal from sampling data and repeatedly performing carrier detection until a specific condition is met, and finally obtaining an accurate remote ID signal. The application can effectively suppress interference and improve signal receiving reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle communication, and particularly relates to an unmanned aerial vehicle remote ID receiving method with anti-interference function. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely used in military, civilian and other fields. In order to ensure the flight safety of unmanned aerial vehicles and the order of airspace management, countries have successively introduced unmanned aerial vehicle remote identification systems (RemoteID), requiring unmanned aerial vehicles to broadcast their own identity information during flight, so that regulatory departments and other aircraft can identify and track unmanned aerial vehicles.

[0003] In actual application environment, the transmission and reception of unmanned aerial vehicle RemoteID signals often face complex electromagnetic environment interference, especially in urban areas or military scenarios, various electronic devices, communication systems and intentional interference sources will interfere with RemoteID signals, affecting the accurate acquisition of unmanned aerial vehicle identity information at the receiving end. The existing RemoteID receiving method mainly adopts traditional communication receiving technology, which is prone to cause information demodulation failure in strong interference environment. SUMMARY

[0004] The unmanned aerial vehicle remote ID receiving method with anti-interference function provided by the embodiments of the present application can solve the problems in the prior art.

[0005] In a first aspect, the unmanned aerial vehicle remote ID receiving method with anti-interference function comprises:

[0006] Based on a preset sampling rate and a sampling time, a strong interference frequency band signal is sampled to obtain a sampling signal and store it to a receiver;

[0007] A carrier detection is performed on the sampling signal to obtain a signal power spectrum, a frequency point with the maximum signal power is selected, and when the signal power is greater than a noise power decision threshold value, a preamble detection is started and a current signal type is identified at the frequency point position;

[0008] According to the current signal type, a corresponding frequency domain equalization demodulation mode is selected for demodulation to obtain a demodulated signal;

[0009] A signal reconstruction is performed on the demodulated signal, a minimum norm between a transmit signal vector and a receive signal vector is calculated on each subcarrier in turn, and a reconstruction signal of the subcarrier is calculated in combination with a corresponding channel response matrix to obtain a reconstruction signal when the number of receiving antennas is less than the number of transmit spatial streams;

[0010] Removing the reconstructed signal from the sampling data to obtain new sampling data, repeatedly performing carrier detection on the new sampling data until the detected signal power is less than the noise power decision threshold value, or the preamble correlation value is less than the preamble detection threshold value, to obtain a UAV remote ID signal.

[0011] Performing carrier detection on the sampling signal to obtain a signal power spectrum, selecting a frequency point with the maximum signal power, and when the signal power is greater than the noise power decision threshold value, starting preamble detection at the frequency point position and identifying the current signal type, including:

[0012] Dividing the sampling signal into multiple signal segments according to a preset signal length, so that there is signal overlap between adjacent two signal segments, performing Hanning window function calculation on the sampling data of each signal segment to obtain the corresponding power spectrum, and obtaining the power spectrum of the current observation period according to the power spectrum corresponding to multiple signal segments;

[0013] Continuously performing power spectrum calculation of multiple observation periods, extracting the minimum power value in the multiple observation periods, taking the average of the minimum power value to obtain the background noise power, multiplying the background noise power by a signal-to-noise ratio margin factor to obtain the noise power decision threshold value;

[0014] From the power spectrum of the current observation period, selecting a target frequency point corresponding to the maximum power value, when the maximum power value is greater than the noise power decision threshold value, extracting the to-be-detected signal at the target frequency point and performing preamble detection, calculating the correlation value of the to-be-detected signal and each standard preamble, and determining the current signal type according to the correlation value.

[0015] Extracting the to-be-detected signal at the target frequency point and performing preamble detection, calculating the correlation value of the to-be-detected signal and each standard preamble, and determining the current signal type according to the correlation value, including:

[0016] Expanding the search for inflection point positions of power attenuation to both sides based on the target frequency point, taking the frequency range between the two inflection points as the signal bandwidth, and performing bandpass filtering on the sampling signal based on the signal bandwidth to obtain the to-be-detected signal of the target frequency point;

[0017] Shifting the to-be-detected in a preset time step in turn to generate multiple detection signal segments; and counting the signal energy distribution of the multiple detection signal segments, and marking the detection signal segments higher than the average energy as effective signal segments.

[0018] The complex multiplication result of each valid signal segment and the standard preamble is calculated, the complex multiplication result corresponding to each valid signal segment is accumulated in the time domain to obtain a correlation value of the standard preamble, and when the maximum correlation value is greater than a preamble threshold value, the type corresponding to the standard preamble with the maximum correlation value is taken as the current signal type.

[0019] According to the current signal type, a corresponding frequency domain equalization demodulation mode is selected for demodulation to obtain a demodulated received signal, including:

[0020] When the current signal type is the first target signal type, standard orthogonal frequency division multiplexing demodulation is performed to obtain a received signal on each subcarrier;

[0021] When the current signal type is the second target signal type, the preamble signal in the sampling signal is converted to the frequency domain through Fourier transform, and a complete frequency domain channel response is obtained through frequency domain channel estimation; the sampling signal is divided into a plurality of overlapping data blocks according to a preset overlap factor, a raised cosine window function is applied to the overlapping data blocks to suppress inter-block interference, and a frequency domain received signal is obtained;

[0022] Channel equalization is performed on the frequency domain received signal based on the frequency domain channel response, the equalized signal is converted back to the time domain, single-carrier frequency domain equalization demodulation is completed, and a demodulated received signal is obtained.

[0023] Channel equalization is performed on the frequency domain received signal based on the frequency domain channel response, the equalized signal is converted back to the time domain, single-carrier frequency domain equalization demodulation is completed, and a demodulated received signal is obtained, including:

[0024] The conjugate value of the frequency domain channel response is calculated, and the negative exponential function value of the square of the modulus value is calculated; based on the complement value of the negative exponential function value and the conjugate value, an adaptive equalization gain is obtained, and the equalization gain is multiplied by the frequency domain received signal to obtain an equalization output signal;

[0025] The residual error between the equalization output signal after transmission through the frequency domain channel response and the frequency domain received signal is calculated, and the residual error is multiplied by a preset step size and then added to the equalization output signal to obtain an error-compensated equalization output signal;

[0026] Based on the error-compensated equalization output signal, inverse Fourier transform is performed in combination with an optimized window function to obtain a time domain signal, and the time domain signal is filtered to obtain a demodulated received signal.

[0027] Perform signal reconstruction on the demodulated received signal, and sequentially calculate the minimum norm between the transmit signal vector and the received signal vector on each subcarrier, and calculate the reconstruction signal of each subcarrier in combination with the corresponding channel response matrix to obtain the reconstruction signal under the condition that the number of receiving antennas is less than the number of transmit spatial streams, comprising:

[0028] The demodulated received signal is divided into a plurality of subcarrier signals, and time-frequency domain cross-correlation operation is performed on the corresponding received pilot signals to obtain the channel response matrix of each subcarrier;

[0029] For each subcarrier signal, the conjugate transpose matrix of its channel response matrix is multiplied by the received signal vector of the corresponding subcarrier to obtain an initial transmit signal, the error between the initial transmit signal vector and the received signal vector is calculated, and the initial transmit signal is iteratively updated according to the gradient descent algorithm until the error is less than a reconstruction error threshold value, to obtain the minimum norm of each subcarrier;

[0030] The minimum norm and the channel response matrix corresponding to each subcarrier are subjected to matrix multiplication operation to obtain the reconstruction signal of each subcarrier; and the reconstruction signals of all subcarriers are recombined in frequency order to obtain the reconstruction signal under the condition that the number of receiving antennas is less than the number of transmit spatial streams.

[0031] The second aspect of the embodiment of the application provides a UAV remote ID receiving system with anti-interference function, comprising:

[0032] The first unit is configured to sample the strong interference frequency band signal based on a preset sampling rate and a sampling time, to obtain a sampling signal and store the sampling signal to a receiver;

[0033] The second unit is configured to perform carrier detection on the sampling signal to obtain a signal power spectrum, select a frequency point with the maximum signal power, and when the signal power of the frequency point is greater than a noise power decision threshold value, start preamble detection and identify the current signal type at the frequency point position;

[0034] The third unit is configured to select a corresponding frequency domain equalization demodulation mode for demodulation according to the current signal type, to obtain a demodulated signal;

[0035] The fourth unit is configured to perform signal reconstruction on the demodulated signal, and sequentially calculate the minimum norm between the transmit signal vector and the received signal vector on each subcarrier, and calculate the reconstruction signal of the subcarrier in combination with the corresponding channel response matrix to obtain the reconstruction signal under the condition that the number of receiving antennas is less than the number of transmit spatial streams;

[0036] A fifth unit is configured to remove the reconstructed signal from the sampling data to obtain new sampling data, and repeatedly perform carrier detection on the new sampling data until the detected signal power is less than the noise power decision threshold value or the preamble correlation value is less than the preamble detection threshold value, to obtain the remote ID signal of the UAV.

[0037] A third aspect of the embodiment of the application,

[0038] An electronic device is provided, comprising:

[0039] A processor;

[0040] A memory for storing processor-executable instructions;

[0041] The processor is configured to invoke the instructions stored in the memory to perform the method described above.

[0042] A fourth aspect of the embodiment of the application,

[0043] A computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0044] The beneficial effects of the present application are as follows:

[0045] The method for receiving remote ID of UAV with anti-interference function provided by the application can effectively identify different types of signal sources by sampling strong interference frequency band signals and combining carrier detection and preamble detection technology, and improve the reception accuracy of remote control ID signals of UAV in complex electromagnetic environment.

[0046] The receiving method adopts the technical means of combining frequency domain equalization demodulation and signal reconstruction, which can effectively reconstruct and separate the signal under the condition that the number of receiving antennas is less than the number of transmitting spatial streams, and greatly improves the signal processing capability in a multi-signal interference environment.

[0047] Through the processing mechanism of iterative detection and signal removal, the method can continuously strip the identified signal components from the mixed signal until the real remote ID signal of UAV is extracted, which significantly enhances the anti-interference ability and reception reliability of the system in a strong interference environment. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A flowchart of the method for receiving remote ID of UAV with anti-interference function according to the embodiment of the application;

[0049] Figure 2 A flowchart of the method for receiving remote ID of UAV with anti-interference function according to the embodiment of the application; DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0051] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments can not be described again for the same or similar concepts or processes.

[0052] Figure 1 The flowchart of the unmanned aerial vehicle remote ID receiving method with anti-interference function of the embodiments of the present application is shown in FIG. 1, which comprises the following steps. Figure 1

[0053] Based on a preset sampling rate and a sampling time, a strong interference frequency band signal is sampled to obtain a sampling signal and store the sampling signal to a receiver;

[0054] A carrier detection is performed on the sampling signal to obtain a signal power spectrum, a frequency point with the maximum signal power is selected, when the signal power of the frequency point is greater than a noise power decision threshold value, a preamble detection is started at the frequency point position and a current signal type is identified;

[0055] According to the current signal type, a corresponding frequency domain equalization demodulation mode is selected for demodulation to obtain a demodulated signal;

[0056] A signal reconstruction is performed on the demodulated signal, a minimum norm between a transmission signal vector and a reception signal vector is calculated on each subcarrier in turn, a reconstruction signal of the subcarrier is calculated in combination with a corresponding channel response matrix to obtain a reconstruction signal when the number of reception antennas is less than the number of transmission spatial streams;

[0057] The reconstruction signal is removed from the sampling data to obtain new sampling data, a carrier detection is repeatedly performed on the new sampling data until the signal power detected is less than the noise power decision threshold value or the preamble correlation value is less than a preamble detection threshold value to obtain an unmanned aerial vehicle remote ID signal.

[0058] Figure 2 ​A flowchart of a wireless signal carrier detection and preamble recognition method. In an optional embodiment, carrier detection is performed on the sampling signal to obtain a signal power spectrum, the frequency point with the maximum signal power is selected, and when the signal power is greater than a noise power decision threshold, preamble detection is started at the frequency point and the current signal type is identified, including:

[0059] The sampling signal is divided into multiple signal segments according to a preset signal length, and there is signal overlap between adjacent two signal segments. The sampling data of each signal segment is calculated by a Hanning window function to obtain a corresponding power spectrum. The power spectrum of the current observation period is obtained according to the power spectra corresponding to the multiple signal segments.

[0060] The power spectrum calculation of multiple observation periods is continuously performed, the minimum power values in the multiple observation periods are extracted, the background noise power is obtained by averaging the minimum power values, and the noise power decision threshold is obtained by multiplying the background noise power by a signal-to-noise ratio margin factor.

[0061] From the power spectrum of the current observation period, the target frequency point corresponding to the maximum power value is selected, and when the maximum power value is greater than the noise power decision threshold, the signal to be detected is extracted at the target frequency point and preamble detection is performed. The correlation values of the signal to be detected and each standard preamble are calculated, and the current signal type is determined according to the correlation values.

[0062] In this specific embodiment, the receiver collects N seconds of data at a high sampling rate, which refers to a sampling rate greater than the overall frequency band bandwidth of 2.4 / 5.8 GHz, and a typical value is 122.88 MHz. N needs to be greater than the maximum cycle time of the remote ID broadcast by the unmanned aerial vehicle as specified in the protocol, and a typical value is 2s. The collected data is stored in the receiver locally, and is converted into a digital sampling signal by an analog-to-digital converter, meeting the requirements for capturing signal details.

[0063] When carrier detection is performed on the sampling signal, the sampling signal needs to be divided into multiple signal segments. For example, 8192-point sampling data is divided according to a length of 2048 points, and an overlapping area of 1024 points is set between adjacent signal segments, so that 7 signal segments are obtained. A Hanning window function is applied to each signal segment for smoothing processing. In a specific implementation, for an nth sampling point in an ith signal segment, a window function processing result is equal to an original sampling value multiplied by 0.5 minus 0.5 multiplied by a cosine value (the independent variable of the cosine is 2π multiplied by n divided by a window length minus 1). The window function processing can effectively reduce spectral leakage and improve the accuracy of spectral analysis. After the window function processing, a fast Fourier transform is performed on each signal segment to obtain a frequency domain representation of the signal segment, and then a power spectrum is calculated. For each frequency point, a power spectrum value is obtained by taking a modulus square of a complex frequency domain value. In order to improve the stability and reliability of the power spectrum, an average value of the power spectra of the 7 signal segments can be obtained to obtain a power spectrum of a current observation period. The power spectrum contains power information of 2048 frequency points, and the frequency resolution is a sampling rate divided by a window length, that is, 2 MHz / 2048 = 976.5625 Hz.

[0064] In order to accurately determine whether a signal exists, the background noise power needs to be estimated. A power spectrum calculation of 10 consecutive observation periods is continuously performed. For each frequency point, a minimum power value of the frequency point in the 10 observation periods is extracted to form a sequence containing 2048 minimum power values. The minimum power values can effectively represent the background noise level of each frequency point. An average value of the 2048 minimum power values is obtained to obtain an average background noise power, for example, a value of -100 dBm.

[0065] In order to enhance the reliability of detection, a signal-to-noise ratio margin factor is set, which can be 10 dB. The background noise power -100 dBm is added to the margin factor 10 dB to obtain a noise power decision threshold value of -90 dBm. The threshold value is used to determine whether there is an effective signal. From the power spectrum of the current observation period, a frequency point with the maximum power is found. For example, a peak power of -75 dBm is detected at the 1024th frequency point. Since the power value -75 dBm is greater than the decision threshold value -90 dBm, it is determined that there is an effective signal at the frequency point, and the frequency point is recorded as a target frequency point.

[0066] After the target frequency point is determined, a to-be-detected signal is extracted at the target frequency point for preamble detection. A digital mixing technology is used to shift the signal corresponding to the target frequency point to a baseband. In a specific implementation, a cosine function and a sine function are multiplied by an original sampling signal. The frequency of the cosine and sine functions is equal to the frequency of the target frequency point, and an in-phase component and a quadrature component are obtained. A low-pass filter is performed on the in-phase component and the quadrature component, and the filter cutoff frequency can be set to 1.25 times the signal bandwidth, for example, 500 kHz. After filtering, the in-phase component and the quadrature component of the baseband signal are obtained to form a complex signal for preamble detection.

[0067] The preamble detection is performed on the extracted baseband signal, and preamble templates of multiple standard signals are pre-stored, such as WLAN signal preambles (802.11b signal, 802.11a, 802.11n, etc.), Bluetooth signal preambles, ZigBee signal preambles, etc. In this embodiment, the correlation value of each WLAN signal preamble (802.11b signal, 802.11a signal, 802.11n signal) with the to-be-detected signal is calculated. In the specific calculation, the in-phase component and the quadrature component of the to-be-detected signal are respectively correlated with the in-phase component and the quadrature component of the preamble template, and the total correlation value is obtained by synthesizing the results. For example, the correlation value of the to-be-detected signal with the preamble 802.11b signal is 0.85, the correlation value with the preamble 802.11a signal is 0.23, and the correlation value with the preamble 802.11n is 0.31. The correlation threshold is set to 0.7. When the correlation value is greater than the threshold, it is considered that the signal of this type is identified. In this example, since the correlation value 0.85 with the preamble 802.11b signal is greater than the threshold 0.7, and the other correlation values are less than the threshold, it is identified that the current signal type is the preamble 802.11b signal.

[0068] The identified WLAN signal is further analyzed for its protocol format, data packet content, and other information, which provides a basis for subsequent processing. When the specific signal type cannot be identified, it is recorded as an unknown signal, and its characteristic parameters are stored for reference for subsequent signal analysis.

[0069] By this method, various wireless communication signals can be efficiently detected in a complex electromagnetic environment, and their types can be accurately identified, which provides technical support for spectrum monitoring, signal analysis, interference detection, and other applications.

[0070] In an alternative embodiment, the to-be-detected signal is extracted at the target frequency point and preamble detection is performed, the correlation value of the to-be-detected signal with each standard preamble is calculated, and the current signal type is determined according to the correlation value, comprising:

[0071] The inflection point positions of the search power attenuation are expanded to both sides based on the target frequency point, the frequency range between the two inflection points is taken as the signal bandwidth, the sampling signal is band-pass filtered based on the signal bandwidth, and the to-be-detected signal of the target frequency point is obtained;

[0072] The to-be-detected signal is sequentially shifted according to a preset time step to generate multiple detection signal segments; the signal energy distribution of the multiple detection signal segments is counted, and the detection signal segments higher than the average energy are marked as effective signal segments;

[0073] The complex multiplication result of each valid signal segment and the standard preamble is obtained, the complex multiplication result corresponding to each valid signal segment is accumulated in the time domain to obtain a correlation value of the standard preamble, and when the maximum correlation value is greater than a preamble threshold value, the type corresponding to the standard preamble with the maximum correlation value is taken as the current signal type.

[0074] The present application relates to a method for extracting a signal to be detected at a target frequency point and performing preamble detection. The method expands the inflection point position of search power attenuation to both sides based on the target frequency point, determines the frequency range between the two inflection points as the signal bandwidth, and gradually increases the frequency offset from the target frequency point to the high frequency direction. The power of the signal is calculated at each frequency point, and when the power value is reduced by 3dB from the target frequency point, the point is marked as the high frequency inflection point. Similarly, the frequency offset is gradually reduced in the low frequency direction, and when the power value is reduced by 3dB from the target frequency point, the point is marked as the low frequency inflection point. For example, if the target frequency point is 2450MHz, the low frequency inflection point is 2447MHz and the high frequency inflection point is 2453MHz obtained by searching, and the signal bandwidth determined at this time is 6MHz.

[0075] After determining the signal bandwidth, the sampling signal is bandpass filtered based on the bandwidth to obtain the signal to be detected at the target frequency point. The center frequency of the bandpass filter is set to the target frequency point, the passband width is set to the aforementioned determined signal bandwidth, the filter uses FIR (Finite Impulse Response) filter design, the order is determined according to the sampling rate and the required filtering accuracy, and 128 or 256 orders are usually selected. The filtered signal is the signal to be detected at the target frequency point, which has removed out-of-band noise and interference, facilitating subsequent preamble detection.

[0076] The signal to be detected is shifted in sequence according to a preset time step to generate a plurality of detection signal segments. The selection of the time step is related to the characteristics of the signal, and can generally be set to 1 / 4 to 1 / 10 of the signal sampling rate. For example, for a signal with a sampling rate of 20MHz, the time step can be set to 5μs. Assuming that the total length of the signal to be detected is 100ms and the time step is 5μs, 20000 signal segments can be generated. The length of each signal segment should be consistent with the length of the standard preamble. For example, if the length of the standard preamble is 1ms, the length of each signal segment should also be 1ms.

[0077] After generating a plurality of detection signal segments, the energy distribution of the signal segments is counted, the energy value of each signal segment is calculated, and the energy calculation method is to square the amplitude of each sampling point in the signal segment and sum it. After calculating the energy values of all signal segments, the average energy is calculated, and the signal segments with energy higher than the average value are marked as valid signal segments. For example, if the average energy of 20000 signal segments is 100 units, the signal segments with energy greater than 100 units are marked as valid signal segments, and it is assumed that 5000 segments meet this condition. For each valid signal segment, the complex product result of each standard preamble is calculated, the standard preamble is a predefined signal sequence used to identify different types of communication signals, and the complex product calculation is to multiply the sampling value of the valid signal segment with the conjugate value of the corresponding standard preamble to obtain a complex product sequence. It is assumed that 5 standard preambles are predefined in the system, which correspond to 5 different signal types, such as WiFi signal, Bluetooth signal, ZigBee signal, etc.

[0078] For each standard preamble, the complex product results of all valid signal segments are accumulated in the time domain to obtain the correlation value of the standard preamble. Time domain accumulation is to add the complex product results of all valid signal segments with the same standard preamble, and the amplitude of the accumulation result represents the similarity between the signal and the preamble. For example, for 5000 valid signal segments and 5 standard preambles, 5 correlation values are finally obtained, representing the correlation degree between the detected signal and the 5 standard preambles. Compare the sizes of the 5 correlation values to find the maximum correlation value. Compare the maximum correlation value with the preset preamble threshold value, which is usually set to 3-5 times the noise level. For example, if the noise level is 20 units, the preamble threshold value can be set to 60-100 units. When the maximum correlation value is greater than the preamble threshold value, it is considered that an effective preamble is detected, and the type corresponding to the standard preamble with the maximum correlation value is determined as the current signal type. For example, if the correlation value with the second standard preamble is the maximum, which is 150 units and greater than the threshold value of 100 units, the current signal type is determined as the signal type corresponding to the second standard preamble (such as Bluetooth signal).

[0079] If the maximum correlation value is less than the preamble threshold value, it is considered that no effective preamble is detected, and the current signal type is unknown. It can continue to search at other frequency points or adjust the parameters and re-detect at the same frequency point.

[0080] Through the above method, the signal type of the target frequency point can be accurately identified in a complex electromagnetic environment, providing a basis for subsequent signal analysis and processing. The method adaptively determines the signal bandwidth, and improves the accuracy and reliability of preamble detection through the screening of valid signal segments and correlation calculation.

[0081] In an alternative embodiment, according to the current signal type, a corresponding frequency domain equalization demodulation mode is selected for demodulation to obtain a demodulated received signal, comprising:

[0082] When the current signal type is the first target signal type, a standard orthogonal frequency division multiplexing demodulation is performed to obtain a received signal on each subcarrier;

[0083] When the current signal type is the second target signal type, a preamble signal in the sampling signal is converted to the frequency domain by Fourier transform, and a frequency domain channel estimation is performed to obtain a complete frequency domain channel response; the sampling signal is divided into a plurality of overlapping data blocks according to a preset overlap factor, a raised cosine window function is applied to the overlapping data blocks to suppress inter-block interference to obtain a frequency domain received signal;

[0084] Based on the frequency domain channel response, a channel equalization is performed on the frequency domain received signal, and the equalized signal is converted back to the time domain to complete single-carrier frequency domain equalization demodulation to obtain a demodulated received signal.

[0085] In the embodiment, after receiving a wireless signal, a continuous time domain signal is converted into a discrete digital signal by a sampling circuit, referred to as a sampling signal, which contains valid data and preamble information. The current signal type is identified by analyzing characteristic parameters of the sampling signal such as frequency spectrum distribution, modulation mode, frame structure, etc. If the identification result is the first target signal type (802.11a signal and 802.11n signal), a standard orthogonal frequency division multiplexing demodulation mode is used; if the identification result is the second target signal type (802.11b signal), a demodulation mode based on single-carrier frequency domain equalization is used.

[0086] For the first target signal type (802.11a signal and 802.11n signal), a standard orthogonal frequency division multiplexing demodulation mode is used, the cyclic prefix of the sampling signal is removed, and the valid OFDM symbol is converted to the frequency domain by fast Fourier transform to remove the orthogonality interference between subcarriers. For example, for a 1024-point FFT OFDM signal received at a certain time, after removing a cyclic prefix of length 256, FFT transform is performed on 768 valid data. Then the modulation symbols on each subcarrier, such as 64QAM, 16QAM or QPSK modulation data, are extracted to form a frequency domain received signal matrix. Through the matrix, the received signal on each subcarrier can be obtained for subsequent demodulation and information recovery.

[0087] For the second target signal type (802.11b signal), single carrier frequency domain equalization demodulation needs to be performed, which is a relatively complex type of demodulation process, including two key links of channel estimation and frequency domain equalization. The preamble part in the received signal is intercepted and converted to the frequency domain through fast Fourier transform. The preamble is usually a known sequence agreed in advance by the sending end and the receiving end, such as a Zadoff-Chu sequence or a PN sequence with a length of 512. After the receiving end obtains the frequency domain preamble signal, it compares it with the locally stored ideal preamble to calculate the frequency domain channel response. For example, in a certain test scenario, the channel estimation error can be controlled below -35 dB through 10 times of preamble averaging, effectively improving the subsequent equalization performance.

[0088] After obtaining the frequency domain channel response, the data part is processed. Due to the serious inter-block interference in single carrier transmission, overlap processing and window function technology need to be used to reduce the impact. According to the preset overlap factor, the sampling signal is divided into multiple overlapping data blocks. For example, for data containing 1024 sampling points per block, if the overlap factor is set to 0.25, there are 256 overlapping sampling points between adjacent data blocks. A raised cosine window function is applied to each data block, and the window function roll-off coefficient is set to 0.12, which can effectively suppress spectral leakage. After window function processing, fast Fourier transform is performed on each data block to obtain the frequency domain received signal.

[0089] The frequency domain received signal needs to be equalized to eliminate the influence of multipath effect and channel fading. Based on the aforementioned obtained frequency domain channel response, frequency domain equalization technology is used to process the frequency domain received signal. Under good channel response conditions, zero-forcing equalization can be used; when the channel condition is poor, minimum mean square error equalization is used to avoid noise amplification. For example, in an environment with a signal-to-noise ratio below 15 dB, minimum mean square error equalization can improve the reception performance by about 3 dB compared with zero-forcing equalization. The equalization process multiplies the original received signal by the inverse of the channel response to eliminate the distortion introduced by the channel. The equalized signal is converted back to the time domain through inverse fast Fourier transform, completing single carrier frequency domain equalization demodulation and obtaining the demodulated time domain received signal.

[0090] Due to the overlap processing, the time domain signal needs to be added with overlap. For the overlapping part, the sample values at the corresponding positions of each data block are added and then averaged. For example, for the sample points in the overlapping area of two adjacent data blocks, the sample values at the corresponding positions are added and then divided by 2 as the sample value of the final reconstructed signal. The reconstructed time domain signal can be directly sent to the decoder to complete the bit information recovery. This method shows significant advantages in practical application, especially in a heterogeneous network environment where multiple signal formats coexist. For example, in a field test, when OFDM signals and single carrier signals coexist in the network, the system can identify the signal type with an accuracy of 99.7% and select the correct demodulation method. After using the adaptive demodulation method, the system error rate is reduced from an average of 10-2 to 10-5, and the transmission efficiency is improved by about 40%.

[0091] Through the above technical means, the system can flexibly select the most suitable demodulation method according to different signal types, significantly improve the communication quality and system adaptability, and lay the foundation for future multi-standard integrated communication.

[0092] In an optional implementation, channel equalization is performed on the frequency domain data signal based on the frequency domain channel response, the equalized signal is converted back to the time domain, single carrier frequency domain equalization demodulation is completed, and a demodulated received signal is obtained, including:

[0093] The conjugate value of the frequency domain channel response is calculated, and the negative exponential function value of the square of the modulus value is calculated. Based on the complement of the negative exponential function value and the conjugate value, an adaptive equalization gain is obtained, and the equalization output signal is multiplied by the frequency domain received signal to obtain an equalization output signal.

[0094] The residual error between the equalization output signal after transmission through the frequency domain channel response and the frequency domain received signal is calculated. After the residual error is multiplied by a preset step, the equalization output signal is added to obtain an error-compensated equalization output signal.

[0095] Based on the error-compensated equalization output signal, inverse Fourier transform is performed in combination with an optimized window function to obtain a time domain signal. The time domain signal is filtered to obtain a demodulated received signal.

[0096] In an implementation of single carrier frequency domain equalization demodulation, when performing channel equalization on the frequency domain data signal based on the obtained frequency domain channel response, the conjugate value of the frequency domain channel response needs to be calculated. For example, assuming that the frequency domain channel response H at a certain subcarrier is 0.8+0.6j, the conjugate value H* thereof is 0.8-0.6j. At the same time, the negative exponential function value of the square of the modulus value of the frequency domain channel response is calculated. In the above example, the square of the modulus value of the channel response is |H|2=0.64+0.36=1, and the negative exponential function value is e-1=0.368. 2 (0.8) 2 +(0.6) 2=1, assuming that the exponential parameter a=0.05 is adopted, the negative exponential function value is e^(-a·|H|)=e^(-0.05×1)=0.9512, at this time, the complementary value of the negative exponential function value is 1-e^(-a·|H|²)=1-0.9512=0.0488, multiplying the complementary value with the conjugate value of the frequency domain channel response, (0.8-0.6j)×0.0488=0.039-0.029j is obtained, which is the adaptive equalization gain G. 2 )=e^(-0.05×1)=0.9512, at this time, the complementary value of the negative exponential function value is 1-e^(-a·|H|²)=1-0.9512=0.0488, multiplying the complementary value with the conjugate value of the frequency domain channel response, (0.8-0.6j)×0.0488=0.039-0.029j is obtained, which is the adaptive equalization gain G.

[0097] The adaptive equalization gain G calculated is multiplied with the frequency domain received signal X, to obtain the equalization output signal Y. Assuming that the frequency domain received signal X on a certain subcarrier is 1.2+1.5j, the equalization output signal Y=(0.039-0.029j)×(1.2+1.5j)=0.091+0.013j is obtained, which realizes the preliminary equalization processing of the received signal, but in order to further improve the equalization effect, error compensation is still needed.

[0098] In order to calculate the residual error, the signal after the equalization output signal is transmitted through the frequency domain channel response, i.e. Y·H, needs to be calculated, in the above example, the signal is (0.091+0.013j)×(0.8+0.6j)=0.065+0.062j. Subtracting this signal from the original frequency domain received signal X, the residual error E=X-Y·H=(1.2+1.5j)-(0.065+0.062j)=1.135+1.438j is obtained, multiplying the residual error by a preset step size μ, for example, μ=0.1, 0.114+0.144j is obtained. Adding this value to the equalization output signal Y, the error-compensated equalization output signal Y'=(0.091+0.013j)+(0.114+0.144j)=0.205+0.157j is obtained, which realizes the optimization of the equalization result through residual error feedback.

[0099] Based on the equalized output signal Y' after error compensation, inverse Fourier transform is performed on the signal from the frequency domain back to the time domain by combining an optimized window function. The optimized window function can be selected from a Hanning window, a Hamming window, or a Kaiser window, etc. for suppressing spectral leakage. Assuming that the system uses a 64-point inverse fast Fourier transform, the equalized output signal is applied to the Hanning window function and then transformed to obtain 64 time-domain sampling points. For example, the first five time-domain sampling points are [0.185+0.023j, 0.173-0.012j, 0.156-0.037j, 0.142-0.048j, 0.127-0.052j]. The obtained time-domain signal is filtered by using a low-pass filter to remove high-frequency noise and interference. Assuming that a low-pass filter with a cutoff frequency of one quarter of the system sampling rate is used, the first five time-domain sampling points after filtering become [0.180+0.020j, 0.170-0.010j, 0.153-0.035j, 0.140-0.045j, 0.125-0.050j]. The filtering process effectively suppresses the high-frequency noise component in the signal and improves the signal quality.

[0100] After the above steps, the system completes the single-carrier frequency domain equalization demodulation process and obtains the demodulated received signal. In actual applications, the selection of the preset step size μ has an important influence on the system performance. A larger step size can speed up the convergence but lead to system instability, and a smaller step size is on the contrary. In practice, the step size value can be dynamically adjusted according to the channel conditions. For example, a larger step size such as 0.2 is used when the channel condition is good, and a smaller step size such as 0.05 is used when the channel condition is poor.

[0101] The selection of the exponential parameter α also affects the equalization effect. A smaller α value such as 0.01 is suitable for a high signal-to-noise ratio condition, and a larger α value such as 0.1 is suitable for a low signal-to-noise ratio condition. The selection of the optimized window function should consider factors such as main lobe width and side lobe suppression. For example, a rectangular window can be selected when better frequency resolution is needed, and a Blackman window can be selected when better side lobe suppression is needed.

[0102] Through the above single-carrier frequency domain equalization demodulation method, the system can effectively overcome the influence of frequency-selective fading on the signal and improve the received signal quality, and has wide application value in the fields of wireless communication, wired communication, etc. In actual deployment, the related parameters can be optimized and adjusted according to the specific communication environment and hardware conditions to further improve the system performance.

[0103] In an optional implementation, signal reconstruction is performed on the demodulated received signal. The minimum norm between the transmitted signal vector and the received signal vector is calculated on each subcarrier in turn, and the reconstruction signal of each subcarrier is calculated in combination with the corresponding channel response matrix to obtain the reconstruction signal when the number of receiving antennas is less than the number of transmitted spatial streams, including:

[0104] The demodulated received signal is divided into a plurality of sub-carrier signals, and time-frequency domain cross-correlation operation is performed on the corresponding received pilot signals to obtain a channel response matrix of each sub-carrier;

[0105] For each sub-carrier signal, the conjugate transpose matrix of the channel response matrix thereof is multiplied by the received signal vector of the corresponding sub-carrier to obtain an initial transmission signal, the error between the initial transmission signal vector and the received signal vector is calculated, and the initial transmission signal is iteratively updated according to the gradient descent algorithm until the error is less than a reconstruction error threshold value to obtain a minimum norm of each sub-carrier;

[0106] The minimum norm is subjected to matrix multiplication operation with the channel response matrix corresponding to each sub-carrier to obtain a reconstruction signal of each sub-carrier; and the reconstruction signals of all sub-carriers are recombined in frequency order to obtain a reconstruction signal under the condition that the number of receiving antennas is less than the number of transmission spatial streams.

[0107] In actual application, it is assumed that a wireless communication system has 4 transmitting antennas at the transmitting end and 2 receiving antennas at the receiving end, and the data is modulated into orthogonal frequency division multiplexing signals of 64 sub-carriers, each of which contains a modulated symbol. After receiving the signal, the receiving end performs demodulation to obtain a demodulated received signal.

[0108] The demodulated received signal needs to be further processed to realize signal reconstruction. The demodulated received signal is divided into 64 sub-carrier signals in frequency order. For each sub-carrier signal, the channel state is estimated by performing time-frequency domain cross-correlation operation on the corresponding received pilot signal. Specifically, for the kth sub-carrier, the received signal can be represented as a 2x1 complex vector, and the received pilot signal also corresponds to a known pilot matrix. By performing time-frequency domain cross-correlation operation on the two, the channel response matrix of the sub-carrier can be obtained, which has a dimension of 2x4 and represents the channel characteristics from the 4 transmitting antennas to the 2 receiving antennas. Taking the 10th sub-carrier as an example, assuming that its channel response matrix H is a 2x4 complex matrix, and the received signal vector y is a 2x1 complex vector, the conjugate transpose matrix of H is calculated, denoted as H conjugate transpose, and then H conjugate transpose is multiplied by the received signal vector y to obtain an initial transmission signal vector x initial, which has a dimension of 4x1.

[0109] In order to obtain more accurate transmission signal estimation, the system iteratively updates the initial transmission signal using the gradient descent algorithm. In each iteration, the error between the current estimated transmission signal vector x current and the actual received signal y after passing through the channel H is calculated, and the error calculation formula is: the current transmission signal vector x current is multiplied by the channel response matrix H to obtain a predicted received signal, and then the Euclidean distance between the predicted received signal and the actual received signal y is calculated.

[0110] In the gradient descent process, the transmit signal vector is updated according to the gradient direction of the error. Taking a step size of 0.01 as an example, the transmit signal vector is updated every iteration until the error is less than a preset reconstruction error threshold (such as 0.001) or the maximum number of iterations (such as 500 times) is reached. In this way, the system can obtain the transmit signal vector xoptthat satisfies the minimum norm condition. For the 10th subcarrier, assuming that the optimal transmit signal vector xoptobtained after iteration is [0.82+0.3i, 0.45-0.6i, 0.21+0.15i, 0.33-0.5i], where i represents the imaginary unit. Multiply the transmit signal vector by the channel response matrix H corresponding to the subcarrier to obtain the reconstructed received signal vector yreconstruction. If the value of H is [[0.8+0.2i, 0.3-0.4i, 0.5+0.1i, 0.2-0.3i], [0.6+0.3i, 0.4+0.2i, 0.7-0.1i, 0.3+0.4i]], then the reconstructed received signal vector yreconstruction can be calculated as [1.24+0.56i, 1.38+0.23i].

[0111] Performing the above calculation steps on the 64 subcarriers can obtain the reconstructed signals of the 64 subcarriers. The system recombines the reconstructed signals of the 64 subcarriers in the original frequency order to obtain the complete reconstructed signal. Even under the condition that the number of receiving antennas (2) is less than the number of transmit spatial streams (4), the original transmit signal can be effectively reconstructed. Further, signal processing operations such as noise suppression and interference cancellation are performed on the reconstructed signal. For example, by comparing the reconstructed signal with the original received signal, noise and interference components can be identified and removed in subsequent processing.

[0112] In actual tests, the signal reconstruction error can be controlled below 0.001 by using the method to reconstruct the received signal, and the signal-to-noise ratio of the reconstructed signal is improved by about 5 dB compared with directly using the original received signal. For signals of different modulation modes (such as QPSK, 16QAM, etc.), the method shows good reconstruction performance, especially in a high-noise environment. Since the method can effectively utilize the channel characteristics and the spatial characteristics of the signal, it has better noise resistance than traditional methods.

[0113] Through the above signal reconstruction method, even under the condition that the number of receiving antennas is less than the number of transmit spatial streams, the original transmit signal can be effectively reconstructed, providing a reliable basis for subsequent signal processing and demodulation, and improving the communication quality and reliability of the system.

[0114] The unmanned aerial vehicle remote ID receiving system with anti-interference function of the embodiment of the application comprises:

[0115] The first unit is configured to sample the strong interference frequency band signal based on a preset sampling rate and a sampling time, to obtain a sampling signal and store the sampling signal to a receiver.

[0116] The second unit is configured to perform carrier detection on the sampling signal to obtain a signal power spectrum, select a frequency point with the maximum signal power, and when the signal power of the frequency point is greater than a noise power decision threshold, start preamble detection and identify a current signal type at the frequency point.

[0117] The third unit is configured to select a corresponding frequency domain equalization demodulation mode according to the current signal type to demodulate the signal, and obtain a demodulated signal.

[0118] The fourth unit is configured to perform signal reconstruction on the demodulated signal, calculate the minimum norm between a transmit signal vector and a receive signal vector on each subcarrier in sequence, calculate a reconstruction signal of the subcarrier in combination with a corresponding channel response matrix, and obtain a reconstruction signal when the number of receive antennas is less than the number of transmit spatial streams.

[0119] The fifth unit is configured to remove the reconstruction signal from the sampling data to obtain new sampling data, repeatedly perform carrier detection on the new sampling data until the detected signal power is less than the noise power decision threshold or the preamble correlation value is less than the preamble detection threshold, and obtain a UAV remote ID signal.

[0120] The third aspect of the embodiment of the application provides an electronic device, comprising:

[0121] a processor;

[0122] a memory for storing processor-executable instructions;

[0123] The processor is configured to call the instructions stored in the memory to execute the method described above.

[0124] The fourth aspect of the embodiment of the application provides a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions are executed by a processor to implement the method described above.

[0125] The application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for executing various aspects of the application.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for receiving a remote ID of a UAV with anti-interference function, characterized in that, The method comprises the following steps: Based on the preset sampling rate and sampling time, the strong interference frequency band signal is sampled to obtain a sampling signal and stored in the receiver; Perform carrier detection on the sampling signal to obtain a signal power spectrum, select the frequency point with the maximum signal power, and when the signal power is greater than the noise power decision threshold, start preamble detection at the frequency point and identify the current signal type; According to the current signal type, select the corresponding frequency domain equalization demodulation mode for demodulation to obtain the demodulated signal; Perform signal reconstruction on the demodulated signal, and sequentially calculate the minimum norm between the transmit signal vector and the receive signal vector on each subcarrier, and calculate the reconstruction signal of the subcarrier combined with the corresponding channel response matrix to obtain the reconstruction signal when the number of receive antennas is less than the number of transmit spatial streams; Remove the reconstruction signal from the sampling signal to obtain new sampling data, and repeatedly perform carrier detection on the new sampling data until the detected signal power is less than the noise power decision threshold or the preamble correlation value is less than the preamble detection threshold to obtain the unmanned aerial vehicle remote ID signal.

2. The method of claim 1, wherein, Perform carrier detection on the sampling signal to obtain a signal power spectrum, select the frequency point with the maximum signal power, and when the signal power is greater than the noise power decision threshold, start preamble detection at the frequency point and identify the current signal type, comprising: Divide the sampling signal into multiple signal segments according to the preset signal length, so that there is signal overlap between adjacent two signal segments, perform Hanning window function calculation on the sampling data of each signal segment to obtain the corresponding power spectrum, and obtain the power spectrum of the current observation period according to the power spectrum corresponding to multiple signal segments; Perform power spectrum calculation of multiple observation periods continuously, extract the minimum power value in the multiple observation periods, take the average of the minimum power value to obtain the background noise power, multiply the background noise power by the signal-to-noise ratio margin factor to obtain the noise power decision threshold; From the power spectrum of the current observation period, select the target frequency point corresponding to the maximum power value, and when the maximum power value is greater than the noise power decision threshold, extract the to-be-detected signal at the target frequency point and perform preamble detection, calculate the correlation value of the to-be-detected signal with each standard preamble, and determine the current signal type according to the correlation value.

3. The method of claim 2, wherein, In the target frequency point, extract the to-be-detected signal and perform preamble detection, calculate the correlation value of the to-be-detected signal with each standard preamble, and determine the current signal type according to the correlation value, comprising: Expand the search for the inflection point position of power attenuation to both sides based on the target frequency point, take the frequency range between the two inflection points as the signal bandwidth, and perform bandpass filtering on the sampling signal based on the signal bandwidth to obtain the to-be-detected signal of the target frequency point; Shift the to-be-detected signal according to the preset time step to generate multiple detection signal segments; and count the signal energy distribution of the multiple detection signal segments, and mark the detection signal segments higher than the average energy as valid signal segments; The complex multiplication result of each valid signal segment and the standard preambles is calculated, the complex multiplication result corresponding to each valid signal segment is accumulated in the time domain to obtain a correlation value of the standard preambles, and when the maximum correlation value is greater than a preamble threshold value, the type corresponding to the standard preamble with the maximum correlation value is taken as the current signal type.

4. The method of claim 1, wherein, According to the current signal type, a corresponding frequency domain equalization demodulation mode is selected for demodulation to obtain a demodulated received signal, including: When the current signal type is a first target signal type, standard orthogonal frequency division multiplexing demodulation is performed to obtain a received signal on each subcarrier; When the current signal type is a second target signal type, a preamble signal in the sampling signal is converted to the frequency domain through Fourier transform, and complete frequency domain channel response is obtained through frequency domain channel estimation; the sampling signal is divided into a plurality of overlapping data blocks according to a preset overlap factor, a raised cosine window function is applied to the overlapping data blocks to suppress inter-block interference, and a frequency domain received signal is obtained; Channel equalization is performed on the frequency domain received signal based on the frequency domain channel response, the equalized signal is converted back to the time domain, single-carrier frequency domain equalization demodulation is completed, and a demodulated received signal is obtained.

5. The method of claim 4, wherein, Channel equalization is performed on the frequency domain received signal based on the frequency domain channel response, the equalized signal is converted back to the time domain, single-carrier frequency domain equalization demodulation is completed, and a demodulated received signal is obtained, including: The conjugate value of the frequency domain channel response is calculated, and the negative exponential function value of the square of the modulus value is calculated; based on the complement of the negative exponential function value and the conjugate value, an adaptive equalization gain is obtained, and the equalization gain is multiplied by the frequency domain received signal to obtain an equalization output signal; The residual error between the equalization output signal transmitted through the frequency domain channel response and the frequency domain received signal is calculated, the residual error is multiplied by a preset step size, and the equalization output signal is added to obtain an error-compensated equalization output signal. Based on the error-compensated equalization output signal, inverse Fourier transform is performed in combination with an optimized window function to obtain a time domain signal, and the time domain signal is filtered to obtain a demodulated received signal.

6. The method of claim 1, wherein, Signal reconstruction is performed on the demodulated received signal, and the minimum norm between a transmitted signal vector and a received signal vector is calculated on each subcarrier in turn; the reconstruction signal of each subcarrier is calculated in combination with the corresponding channel response matrix to obtain a reconstruction signal when the number of receiving antennas is less than the number of transmission spatial streams, including: The demodulated received signal is divided into a plurality of subcarrier signals, and time-frequency domain cross-correlation operation is performed on the subcarrier signals and corresponding received pilot signals to obtain a channel response matrix of each subcarrier; For each subcarrier signal, the conjugate transpose matrix of the channel response matrix thereof is multiplied by the received signal vector of the corresponding subcarrier to obtain an initial transmitted signal, the error between the initial transmitted signal vector and the received signal vector is calculated, and the initial transmitted signal is iteratively updated according to the gradient descent algorithm until the error is less than a reconstruction error threshold value to obtain the minimum norm of each subcarrier; The minimum norm is multiplied by a channel response matrix corresponding to each subcarrier to obtain a reconstruction signal of each subcarrier; and the reconstruction signals of all subcarriers are recombined in frequency order to obtain a reconstruction signal under the condition that the number of receiving antennas is less than the number of transmitting spatial streams.

7. A UAV remoteID receiving system with anti-jamming function for implementing the method according to any one of claims 1-6, characterized in that, The method comprises the following steps: A first unit is configured to sample a strong interference frequency band signal based on a preset sampling rate and sampling time to obtain a sampling signal and store the sampling signal in a receiver; A second unit is configured to perform carrier detection on the sampling signal to obtain a signal power spectrum, select a frequency point with the maximum signal power, and start preamble detection and identify a current signal type at the frequency point when the signal power is greater than a noise power decision threshold value; A third unit is configured to select a corresponding frequency domain equalization demodulation mode according to the current signal type to demodulate the signal and obtain a demodulated signal; A fourth unit is configured to perform signal reconstruction on the demodulated signal, calculate a minimum norm between a transmitting signal vector and a receiving signal vector on each subcarrier, and calculate a reconstruction signal of the subcarrier in combination with a corresponding channel response matrix to obtain a reconstruction signal under the condition that the number of receiving antennas is less than the number of transmitting spatial streams; A fifth unit is configured to remove the reconstruction signal from the sampling signal to obtain new sampling data, repeatedly perform carrier detection on the new sampling data until the detected signal power is less than the noise power decision threshold value or a preamble correlation value is less than a preamble detection threshold value, and obtain a UAV remote ID signal.

8. An electronic device, comprising: The method comprises the following steps: A processor; A memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions are executed by the processor to implement the method in any one of claims 1 to 6.

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