Drone signal synchronization detection method and device

Abstract

The embodiment of the application provides a kind of unmanned aerial vehicle signal synchronization detection method and device, method includes: by in first mode with high gain to signal blind search, signal strength is evaluated after identifying to broadcast pulse and switches to second gain, subsequently enters second mode;Signal is received using adjusted gain and is detected synchronously, further adjusts gain after detection success and enters third mode, realize the continuous tracking and stable reception of signal, wherein, synchronous detection process is carried out frequency domain correlation operation to the synchronization sequence in received signal and local reference sequence, by re-sampling reference sequence to actual signal sampling rate, and using the method that frequency domain multiplication is combined with time domain overlap addition, it is greatly reduced to calculation complexity, while ensuring the accurate positioning of synchronization peak, the application can improve the efficiency and accuracy of unmanned aerial vehicle signal detection and synchronization.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for synchronous detection of unmanned aerial vehicle (UAV) signals. Background Technology

[0002] With the widespread use of drones (especially consumer drones such as the DJI series), reliable identification and synchronization of their communication systems has become a key technical requirement. Currently, some drones broadcast droneID signals periodically with a low duty cycle. A typical pattern is a 640ms period, with only 1ms dedicated to 10MHz bandwidth droneID broadcasting, and the remaining time spent in silence or data transmission. This bursty, high dynamic range signal characteristic presents significant challenges to synchronization detection at the receiver, specifically including: Signal detection is difficult: the broadcast pulse duty cycle is extremely low (about 0.16%), and the receiver needs to quickly capture the brief signal during a long period of silence; Wide dynamic range: When a drone flies from a long distance (e.g., 4km) to a short distance (e.g., 10m), the path loss can change by up to 52dB, requiring the receiver to have wide dynamic gain adjustment capability; The propagation environment is complex: factors such as multipath delay (e.g., about 13μs for 4km) and Doppler frequency shift caused by high-speed movement (e.g., about 280-560Hz at a speed of 15m / s) further increase the difficulty of synchronization stability and accuracy; High real-time processing requirements: Traditional time-domain correlation-based synchronization methods have a large computational load under 10MHz bandwidth, making them difficult to implement in real time in embedded systems.

[0003] Therefore, there is an urgent need for a method for UAV signal synchronization detection that can improve the efficiency and accuracy of UAV signal detection and synchronization. Summary of the Invention

[0004] To address the problems in the prior art, this application provides a method and apparatus for detecting UAV signal synchronization, which can improve the efficiency and accuracy of UAV signal detection and synchronization.

[0005] To solve at least one of the above problems, this application provides the following technical solution: In a first aspect, this application provides a method for detecting unmanned aerial vehicle (UAV) signal synchronization, comprising: In the first working mode, the drone broadcast pulse signal is detected within a preset silence period of the drone signal with a first gain. If the drone broadcast pulse signal is detected, the signal strength of the drone broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. In the second working mode, the drone broadcast pulse signal is continuously received according to the second gain, and the drone broadcast pulse signal is synchronously detected. Based on the detection result after the synchronous detection, the second gain is adjusted to the third gain, and the third working mode is entered. The drone broadcast signal is continuously received and tracked in the third working mode to realize the synchronous detection of drone signals. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0006] Further, the step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm to determine the corresponding frequency domain correlation result includes: Based on the sampling rate, determine the corresponding number of Fast Fourier Transform points M; The reference sequence after the sampling rate transformation is padded with zeros to a length of M to determine the corresponding first zero-padded sequence.

[0007] Furthermore, the step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm to determine the corresponding frequency domain correlation result also includes: The synchronization sequence is divided into time domains according to a preset length to determine multiple corresponding synchronization sequence segments, wherein the length is determined based on the sampling rate; Each of the aforementioned synchronization sequence segments is padded with zeros to a length of M to determine the corresponding multiple second zero-padding sequences.

[0008] Furthermore, the step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm to determine the corresponding frequency domain correlation result also includes: Perform M-point Fast Fourier Transform on the first zero-padded sequence and each of the second zero-padded sequences respectively to determine the corresponding first frequency domain sequence and multiple second frequency domain sequences; Based on the first frequency domain sequence, each of the second frequency domain sequences is multiplied point by point in the frequency domain to determine the corresponding multiple frequency domain correlation results.

[0009] Further, the step of merging the time-domain correlation results according to a preset overlap-addition algorithm, and determining the corresponding detection result after synchronous detection based on the peak position of the merged time-domain correlation results, includes: Add the first X data points of the time-domain correlation result of the current synchronization sequence segment to the last X data points of the time-domain correlation result obtained in the previous calculation point by point to determine the corresponding time-domain cumulative result sequence; After continuously summing the time-domain correlation results of all synchronization sequence segments point by point, the corresponding total time-domain accumulation result sequence is determined; In the total accumulated result sequence in the time domain, the maximum peak value is searched, and the time domain index position corresponding to the maximum peak value position is the precise synchronization position of the UAV synchronization sequence. The drone signal is synchronized based on the location of the maximum peak value.

[0010] Further, in the first operating mode, detecting the presence of a drone broadcast pulse signal within a preset silence period of the drone signal with a first gain includes: Set a time detection window, the duration of which is greater than or equal to the silence period of the UAV signal; In the first working mode, within the time detection window, radio frequency signals are continuously received at a first gain to detect whether there is a drone broadcast pulse signal. The first working mode is a search mode, and the first gain is a fixed maximum gain, which is used to determine and detect drone signals over a wide range.

[0011] Further, adjusting the second gain to the third gain based on the detection result after the synchronization detection includes: Peak energy is extracted from the time-domain total accumulation result sequence obtained after the synchronous detection to determine the corresponding signal strength index; If the signal strength index is higher than the upper limit of the preset target reception range, then the second gain is reduced and the corresponding third gain is determined; If the signal strength index is lower than the preset target reception range lower limit, then the second gain is increased, and the corresponding third gain is determined.

[0012] Secondly, this application provides a drone signal synchronization detection device, comprising: The signal search and determination module is used to detect whether there is a drone broadcast pulse signal within a preset silence period of the drone signal in the first working mode with a first gain. If a drone broadcast pulse signal is detected, the signal strength of the drone broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. The signal acquisition and tracking module is used to continuously receive the UAV broadcast pulse signal according to the second gain in the second working mode, and to perform synchronous detection on the UAV broadcast pulse signal. Based on the detection result after the synchronous detection, the second gain is adjusted to the third gain, and the module enters the third working mode to continuously receive and track the UAV broadcast signal in the third working mode, thereby realizing synchronous detection of UAV signals. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0013] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the UAV signal synchronization detection method.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the UAV signal synchronization detection method.

[0015] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the aforementioned UAV signal synchronization detection method.

[0016] As can be seen from the above technical solution, this application provides a method and apparatus for UAV signal synchronization detection. In a first working mode, a high-gain blind search is performed on the signal. After identifying the broadcast pulse, the signal strength is evaluated and the system switches to a second gain, subsequently entering the second working mode. The adjusted gain is used to receive the signal and perform synchronization detection. After successful detection, the gain is further adjusted to enter a third working mode, achieving continuous signal tracking and stable reception. The synchronization detection process performs frequency domain correlation operations on the synchronization sequence in the received signal and the local reference sequence. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is significantly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts illustrating the UAV signal synchronization detection method in the embodiments of this application; Figure 2 This is a structural diagram of the UAV signal synchronization detection device in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0019] Figure label: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The acquisition, storage, use, and processing of data in this application all comply with relevant laws and regulations.

[0022] Given the widespread application of drones, reliable identification and synchronization of drone signals are key technologies, but current challenges include difficulties in signal detection and high computational complexity in synchronization. This application provides a drone signal synchronization detection method and apparatus. In a first operating mode, a blind search is performed on the signal at high gain. After identifying a broadcast pulse, the signal strength is evaluated, and the system switches to a second gain, subsequently entering the second operating mode. The adjusted gain is used to receive the signal and perform synchronization detection. Upon successful detection, the gain is further adjusted to enter a third operating mode, achieving continuous signal tracking and stable reception. The synchronization detection process performs frequency domain correlation operations between the synchronization sequence in the received signal and a local reference sequence. By resampling the reference sequence to the actual signal sampling rate and employing a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is significantly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of drone signal detection and synchronization.

[0023] To improve the efficiency and accuracy of UAV signal detection and synchronization, this application provides an embodiment of a UAV signal synchronization detection method, see [link to embodiment]. Figure 1 The UAV signal synchronization detection method specifically includes the following: Step S101: In the first working mode, within a preset silent period of the UAV signal, the presence of a UAV broadcast pulse signal is detected with a first gain. If a UAV broadcast pulse signal is detected, the signal strength of the UAV broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. Optionally, in this embodiment, the current challenges in drone signal detection are mainly as follows: The current patterns in DJI drone broadcasting drone IDs on the market include: The data transmission period is 640ms: 20MHz for data transmission and 10MHz for broadcasting DroneID. Every 640ms: A 1ms broadcast pulse (10MHz bandwidth) occurs. ↓ [1ms broadcast]---[639ms silence / PDSCH]---[1ms broadcast]....

[0024] Broadcasts can operate on the same frequency or hop frequencies within each 640ms interval.

[0025] challenge: Burst detection: 1ms broadcasts account for only 0.16% of a 640ms burst. Dynamic range: 4km → 10m distance change → approximately 52dB path loss change Multipath delay: ~13µs propagation delay for 4km Moving Doppler: 15m / s → ~280-560Hz frequency offset (2.4GHz / 5.8G) Initial unknown: The time of the first reception is uncertain. This solution focuses primarily on the synchronous design of the algorithm engineering, including two core designs: first, the AGC adjustment strategy for the received signal; and second, the detection of the UAV synchronization signal. The synchronization signal detection is integrated within the adjustment strategy.

[0026] Specifically, the AGC adjustment strategy for receiving signals mainly involves adjusting the gain settings to adaptively amplify or reduce the UAV signal, so as to maintain continuous reception and synchronization of the signal within the most suitable reception threshold, thereby solving the problem of difficult signal detection.

[0027] This step is the first stage of adjusting the strategy, using a search mode (blind search). The main objective in this stage is to find drone broadcast pulse signals that occur only 1ms.

[0028] We set AGC to a fixed high-gain mode (most sensitive) to quickly identify the presence of drone signals in the 640ms range; When the detection logic determines that a signal matching the pulse characteristics exists within the monitoring period, it first evaluates the signal strength of the pulse signal, including a rapid measurement of the signal power or amplitude. Based on this evaluation result, the system calculates and performs a gain adjustment operation, adjusting the currently excessively high fixed high gain mode down to a more suitable "secondary gain," with the aim of capturing specific UAV signals based on a more appropriate gain.

[0029] After this adjustment is completed, the system's working state immediately switches from "first working mode search mode" to "second working mode capture mode".

[0030] Step S102: In the second working mode, continuously receive the UAV broadcast pulse signal according to the second gain, perform synchronous detection on the UAV broadcast pulse signal, and adjust the second gain to the third gain according to the detection result after the synchronous detection, enter the third working mode, and continuously receive and track the UAV broadcast signal in the third working mode to realize UAV signal synchronous detection. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0031] Optionally, in this embodiment, the signal AGC adjustment strategy, based on step S101, further includes: In the second acquisition mode, the ZC sequence in the UAV broadcast signal is continuously and rapidly detected for 1280ms. Once the result is detected, the AGC gain is modified again for the broadcast signal (the second gain is adjusted to the third gain), and the third tracking mode is entered.

[0032] In the third-mode tracking mode, the energy of the ZC signal is reported at the same time each ZC synchronous detection, and the appropriate AGC is adjusted in time according to the ZC signal energy (the third gain is adjusted in a timely manner).

[0033] Optionally, in this embodiment, the signal synchronization detection strategy, interspersed within the adjustment strategy, includes: A common method for UAV signal synchronization detection involves comparing and calibrating the real-time UAV signal ZC sequence received via gain with a known ZC sequence (reference sequence). However, since the UAV signal detection bandwidth is 10MHz, performing synchronization based on the correlation between a 10MHz bandwidth real-time signal and a known ZC sequence is computationally too intensive and impractical for engineering applications. Furthermore, the sampling rate of a 10MHz bandwidth signal may not meet the 15.36MHz sampling rate required for signal processing. Therefore, improvements are needed for different sampling rates and engineering implementations. First, to reduce the workload of sampling rate transformation: we change the sampling rate of the ZC sequence so that the sampling rate of the known ZC sequence (reference sequence) becomes the sampling rate of the actual data, instead of the 15.36M sampling rate; Second, in order to reduce the computational load of the synchronization signal while ensuring the accuracy of the correlation peak position, we use frequency domain multiplication and then perform the overlap-add algorithm in the time domain.

[0034] Specifically, the standard known ZC sequence length is 1024 points, which meets the 15.36M standard sampling rate. The known ZC sequence is the reference sequence used for calibration with the real-time ZC sequence.

[0035] However, to obtain the actual data sampling rate, we can transform the known ZC sequence sampling rate into the actual sampling rate to obtain the number of sampling points N of the transformed reference ZC sequence.

[0036] Next, find a number of points M for the FFT (Fourier Transform): M=2 2^ log2(N)

[0037] The known reference ZC sequence is padded with MN zeros, and then subjected to an M-point FFT transformation to the frequency domain ZC_frq. Next, the real-time received ZC sequence is divided into lengths based on length X, wherein X is calculated using the following methods: X=2^ log2(N)

[0038] For each ZC sequence segment (data) after length division, pad with zeros to the length of X, and perform an M-point FFT transformation to the frequency domain data_frq; Perform frequency domain data multiplication: Y = ZC_frq. data_frq; Transform it again to the time domain: data_conv=ifft(Y); For the time-domain calculation results of each ZC sequence segment (data), the overlap_add algorithm is used. The last X data of the previous data_conv and the first X data of the current data_conv are added together to obtain the final result. The peak position is the synchronization position.

[0039] This approach overcomes the boundary effects introduced by block processing, ensuring the accuracy of correlation searches across the entire continuous signal. More importantly, compared to directly performing long-sequence time-domain correlation, the block-based overlapping addition method combined with frequency-domain calculation is an engineering approach that further optimizes computational efficiency while guaranteeing completely accurate results.

[0040] The position of the obtained peak is precisely determined as the synchronization position. The amplitude and sharpness of the peak can be used as a measure of signal quality (signal-to-noise ratio), providing a direct and reliable basis for subsequent gain adjustment (adjustment to the third gain).

[0041] The advantages of this embodiment are that it adopts a three-stage AGC adjustment for the open-loop reactive power control mode of UAV movement; for the synchronization algorithm, the sampling rate transformation only applies to the ZC sequence and does not change the data; and for the synchronization algorithm, the overlap_add algorithm is used to find the same effect of time-domain synchronization while reducing the amount of computation.

[0042] This example demonstrates how this embodiment combines the synchronization requirements of burst signal detection, large dynamic range, and high-speed movement, and achieves drone signal synchronization and detection through a three-stage AGC adjustment strategy and a time-domain synchronization strategy incorporating the overlap_add algorithm.

[0043] As described above, the UAV signal synchronization detection method provided in this application can perform blind search of the signal with high gain in the first working mode, identify the broadcast pulse, evaluate the signal strength and switch to the second gain, and then enter the second working mode; receive the signal using the adjusted gain and perform synchronization detection, and after successful detection, further adjust the gain to enter the third working mode to achieve continuous signal tracking and stable reception. In the synchronization detection process, frequency domain correlation operation is performed on the synchronization sequence in the received signal and the local reference sequence. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is greatly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization.

[0044] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S201: Determine the corresponding number of Fast Fourier Transform points M based on the sampling rate; Step S202: Pad the reference sequence after the sampling rate transformation with zeros to length M, and determine the corresponding first zero-padding sequence.

[0045] Optionally, in this embodiment, this step is a preliminary step in the synchronization detection algorithm to achieve efficient frequency domain correlation operations.

[0046] First, based on the actual sampling rate of the currently received signal, calculate and determine an optimal number of FFT transform points M.

[0047] M=2 2^ log2(N)

[0048] M is set to an integer power of 2 (e.g., 128, 256, 512, 1024, etc.) because the standard FFT algorithm is most efficient at computing sequences of power-of-2 lengths.

[0049] The logic for determining M is: First, a standard reference ZC sequence is known, with a length of 1024 points and a standard sampling rate of 15.36M; However, in order to obtain the actual data sampling rate, we transform the reference ZC sequence sampling rate into the actual sampling rate, and then obtain the number of sampling points N of the transformed reference ZC sequence. Then, we find the smallest power of 2 greater than or equal to N. To meet the needs of the subsequent "overlapping addition" algorithm, M is often taken as twice this value. Therefore, M is a suitable parameter that balances computational efficiency and algorithm requirements.

[0050] Next, the resampled local reference sequence is extended to length M through a "zero-padding" operation. Specifically, (M - N) samples with zero amplitude are added to the end of the sequence (or at a specific position according to the algorithm) to form a new sequence of length M, namely the "first zero-padding sequence".

[0051] In this step, the zero-padding operation itself does not change the essential spectral information of the original reference sequence (N points); it merely provides denser frequency domain sampling for its spectrum. This ensures that the local reference sequence and the received data block have the same number of points M before being transformed into the frequency domain, satisfying the basic dimensional requirements of frequency domain complex multiplication. Simultaneously, unifying the sequence length to a power of 2 M allows for the use of computationally efficient FFT / IFFT (Fourier Transform) libraries, which is crucial for achieving real-time performance in engineering applications.

[0052] Through step S202, this embodiment successfully padded the reference sequence with zeros, laying the foundation for subsequent high-efficiency frequency domain calculations.

[0053] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S301: Divide the synchronization sequence into time domains according to a preset length to determine multiple corresponding synchronization sequence segments, wherein the length is determined based on the sampling rate; Step S302: Pad each of the synchronization sequence segments with zeros to length M, and determine the corresponding multiple second zero-padding sequences.

[0054] Optionally, in this embodiment, this step is also a prerequisite step for implementing efficient frequency domain correlation operations in the synchronization detection algorithm.

[0055] Specifically, based on the number N of sampling points in the aforementioned reference ZC sequence, the segmentation length of the synchronization sequence is determined: the data is set to X = 2^ log2(N) .

[0056] Next, using this length as a window, starting from the beginning of the synchronization sequence, the complete synchronization sequence is sequentially cut into multiple temporally continuous "synchronization sequence segments". In practice, to improve processing efficiency and avoid missing signals, these segments are not completely independent, but have a certain degree of overlap, thus forming a sliding window processing flow.

[0057] By transforming the problem of processing continuous long signal sequences into the problem of processing multiple shorter data segments sequentially, the computational task is decomposed. This allows subsequent frequency domain correlation operations to be performed on data blocks of fixed length, thus enabling the use of efficient FFT / IFFT algorithms. Simultaneously, block processing reduces the computational resource requirements of a single operation, laying the foundation for real-time processing.

[0058] After the time domain is partitioned, the length of each synchronization sequence segment is not exactly equal to the preset number of FFT points M. Therefore, this step performs a "zero-padding" operation on each segment, that is, after the original data of each segment, several sampling points with a value of zero are added until the total length of the entire sequence reaches the preset target length M, thereby forming multiple "second zero-padding sequences".

[0059] Through step S302, this embodiment realizes the segmentation and zero-padding of the synchronization sequence, laying the foundation for subsequent high-efficiency frequency domain calculations.

[0060] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S401: Perform M-point Fast Fourier Transform on the first zero-padding sequence and each of the second zero-padding sequences respectively to determine the corresponding first frequency domain sequence and multiple second frequency domain sequences; Step S402: Multiply each of the second frequency domain sequences point by point in the frequency domain according to the first frequency domain sequence to determine the corresponding multiple frequency domain correlation results.

[0061] Optionally, in this embodiment, this step converts the two sets of time-domain sequences that have undergone length unification processing from the time dimension to the frequency dimension for analysis.

[0062] Specifically, process the first sequence.

[0063] Perform an M-point Fast Fourier Transform (FFT) on the first zero-padded sequence (the local reference sequence has a length of M after resampling and zero-padded). FFT is an efficient algorithm that decomposes the sequence into a series of complex representations of sine and cosine wave components of different frequencies, and the output is the "first frequency domain sequence".

[0064] Specifically, the second sequence group is processed.

[0065] Multiple second zero-padded sequences (data blocks segmented from the continuously received signal stream and padded with zeros of the same length) are subjected to M-point FFT one by one. After transformation, each data block yields a corresponding "second frequency domain sequence".

[0066] The first frequency domain sequence is multiplied by each of the second frequency domain sequences using complex multiplication. Specifically, complex points at the same frequency index positions in the two sequences are multiplied together. Performing this operation on each received data block generates a frequency domain correlation result. Therefore, the final result will be a sequence of multiple frequency domain correlation results equal to the number of data blocks.

[0067] Optionally, because the computational complexity of frequency domain multiplication is much lower than that of time domain sliding correlation, this step achieves an exponential improvement in computational efficiency, enabling the system to process high-bandwidth burst signals in real time.

[0068] Through step S402, this embodiment successfully and efficiently completes the correlation detection of multiple signal segments, optimizing the process processing efficiency.

[0069] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S501: Add the first X data points of the time-domain correlation result of the current synchronization sequence segment to the last X data points of the time-domain correlation result obtained in the previous calculation point by point to determine the corresponding time-domain cumulative result sequence; Step S502: After continuously adding the time-domain correlation results of all synchronization sequence segments point by point, determine the corresponding total time-domain accumulation result sequence; Step S503: In the total time-domain accumulation result sequence, search for the maximum peak value. The time-domain index position corresponding to the maximum peak value position is the precise synchronization position of the UAV synchronization sequence. Step S504: Synchronize the UAV signal according to the maximum peak position.

[0070] Optionally, in this embodiment, this step is a specific implementation step for finding the signal synchronization point.

[0071] First, after step S402, we obtain multiple frequency domain correlation result sequences. Then, after performing inverse Fourier transform on each sequence, we obtain multiple time domain correlation result sequences. These time domain correlation result sequences correspond to each of the synchronization sequence segments we previously divided.

[0072] Next, the overlap_add algorithm is used to superimpose and merge the time-domain correlation result sequences.

[0073] Specifically, the correlation response that did not completely end at the end of the previous segment is linearly superimposed with the correlation response that did not completely begin at the beginning of the current segment in the time domain. This addition perfectly repairs the correlation function waveform that was "cut off" at the boundary due to block processing, making the correlation value at the splice completely consistent with the result obtained by performing a complete time-domain correlation calculation on the entire signal, thus ensuring the accuracy of subsequent peak search.

[0074] The overlap length X is calculated in advance.

[0075] During continuous signal processing, the above steps are repeated. Each time a new data segment is processed, its relevant results are superimposed and added to the already accumulated results. Finally, after all segments have been processed, a long, continuous sequence of total time-domain summation results is obtained.

[0076] During the iterative accumulation process, a continuously growing accumulation sequence is maintained, with each new segment contributing only the superposition value of its non-overlapping and overlapping parts. The resulting total sequence fully reflects the correlation values ​​between the received signal and the local reference sequence at all possible time offsets throughout the entire observation period, forming a clear correlation function curve.

[0077] The entire time-domain cumulative result sequence is traversed to find the point with the largest amplitude (or absolute value), i.e., the maximum peak value. According to the theory of correlation function, when the synchronization sequence in the received signal is perfectly aligned with the local reference sequence in time, the correlation value will reach the theoretical maximum value (forming a sharp peak). Therefore, the time-domain index of the maximum peak value (i.e., the position number of the point in the sequence) is directly extracted, indicating the start time of the synchronization sequence in the UAV broadcast pulse.

[0078] Based on the found precise synchronization position, this index value is converted into a precise timestamp, and the sampling clock or buffer read pointer of subsequent signal processing (such as data demodulation) is adjusted accordingly to align it with the received drone signal in time.

[0079] Through step S504, this embodiment successfully locked the signal and achieved time synchronization, providing a crucial timing reference for the subsequent correct demodulation of data information such as Drone ID.

[0080] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S601: Set a time detection window, the duration of which is greater than or equal to the silence period of the UAV signal; Step S602: In the first working mode, within the time detection window, continuously receive radio frequency signals with a first gain to detect whether there is a drone broadcast pulse signal. The first working mode is a search mode, and the first gain is a fixed maximum gain, which is used to determine and detect drone signals over a wide range.

[0081] Optionally, in this embodiment, this step is an implementation of the search mode. The goal is to efficiently complete the initial detection of the signal when its occurrence time and intensity are completely unknown.

[0082] Specifically, a time detection window was set up, defining the baseline for the observation duration of search behavior.

[0083] First, a detection window is defined in the time dimension. The key requirement is that the duration of the window must be greater than or equal to the complete cycle of the target drone signal (e.g., a 640ms cycle, including a 1ms broadcast pulse and a subsequent 639ms silence or data transmission period). This ensures that at least one complete drone signal broadcast pulse can be included within a complete detection window, providing the most basic time guarantee for reliable detection.

[0084] Within this preset time window, the system enters its first operating mode (search mode). The core of this mode is to fix the gain of the receiving channel to a pre-calibrated high value, typically the system's maximum available gain or a fixed value to ensure extremely high sensitivity. The system then continuously receives RF signals from the environment using this gain and performs continuous power monitoring or simple energy detection on the received signals.

[0085] This step essentially trades high sensitivity for detection probability. By using a fixed high gain, the receiver achieves its maximum achievable detection sensitivity, enabling it to capture extremely weak signals. This directly addresses the significant path loss challenge posed by the potentially large initial distance of the drone (e.g., 4 kilometers), ensuring that even under the weakest signal conditions, the system has a chance to detect it.

[0086] Within the time detection window, the system continuously compares the received signal energy with the noise threshold. Once a burst pulse with energy significantly exceeding the background noise is detected at a certain moment, and its duration characteristics match the expected 1ms broadcast pulse, it can be preliminarily determined that "a drone broadcast pulse signal has been detected".

[0087] Through step S602, this embodiment successfully ensures that even under the weakest signal conditions, there is still a chance for the system to detect it.

[0088] In one embodiment of the UAV signal synchronization detection method of this application, it may further include the following: Step S701: Extract peak energy from the time-domain total accumulation result sequence obtained after the synchronization detection to determine the corresponding signal strength index; Step S702: If the signal strength index is higher than the upper limit of the preset target reception range, then reduce the second gain and determine the corresponding third gain; Step S703: If the signal strength index is lower than the preset target reception range lower limit, then increase the second gain and determine the corresponding third gain.

[0089] Optionally, in this embodiment, this step is performed in the second stage of capture mode. After synchronization detection, the gain value is adjusted according to the synchronization detection result so that the gain adapts to the synchronization detection result. Then, the third stage of tracking mode is entered, and the UAV signal is continuously tracked with the gain to maintain signal continuity.

[0090] Specifically, the relevant operational peak point obtained by the overlapping addition algorithm is located in the time-domain total accumulation result sequence obtained by the previous synchronous detection calculation. The amplitude (energy) of this peak point is extracted as a direct and reliable indicator for evaluating the current received signal strength. It comprehensively reflects the signal propagation loss, environmental interference, and the effect of the current receiver gain setting, and is more accurate than the original radio frequency energy detection.

[0091] At the same time, the system presets an ideal signal processing target range, which is generally the standard range for signal processing.

[0092] If the extracted signal strength index is too high, exceeding the upper limit of the range, it indicates that the signal is too strong and may cause receiver link saturation or nonlinear distortion. Therefore, the automatic gain control (AGC) system is instructed to reduce the current gain (second gain) to attenuate the signal to the optimal processing range, thereby determining the third gain.

[0093] Conversely, if the signal strength index is too low, below the lower limit of the range, it indicates that the signal is too weak and the signal-to-noise ratio is insufficient. The system instructs AGC to increase the gain to amplify the signal, ensuring the reliability of subsequent demodulation, and thus determining a new third gain.

[0094] In this step, the highly reliable signal energy information obtained after synchronization is used to achieve the transition from "coarse tuning" to "fine tuning." It effectively overcomes the shortcomings of relying solely on RF power detection, which may lead to misjudgments due to interference or noise, and directly links gain adjustment to the final demodulation performance.

[0095] Through step S703, this embodiment successfully ensures that the signal strength is stable within the optimal dynamic range of the analog-to-digital converter when it enters the tracking and demodulation module, thereby maximizing the signal-to-noise ratio and providing a solid foundation for accurate and stable decoding of Drone ID information.

[0096] To improve the efficiency and accuracy of UAV signal detection and synchronization, this application provides an embodiment of a UAV signal synchronization detection device for implementing all or part of the aforementioned UAV signal synchronization detection method. See [link to embodiment]. Figure 2 The UAV signal synchronization detection device specifically includes the following components: The signal search and determination module 10 is used to detect whether there is a drone broadcast pulse signal within a preset silence period of the drone signal in the first working mode with a first gain. If a drone broadcast pulse signal is detected, the signal strength of the drone broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. The signal acquisition and tracking module 20 is used to continuously receive the UAV broadcast pulse signal according to the second gain in the second working mode, perform synchronous detection on the UAV broadcast pulse signal, and adjust the second gain to the third gain according to the detection result after the synchronous detection, enter the third working mode, and continuously receive and track the UAV broadcast signal in the third working mode to realize UAV signal synchronous detection. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0097] As described above, the UAV signal synchronization detection device provided in this application embodiment can perform blind search of signals with high gain in the first working mode, identify broadcast pulses, evaluate signal strength, switch to the second gain, and then enter the second working mode; receive signals and perform synchronization detection using the adjusted gain, and further adjust the gain to enter the third working mode after successful detection, thereby achieving continuous signal tracking and stable reception. In the synchronization detection process, frequency domain correlation operation is performed on the synchronization sequence in the received signal and the local reference sequence. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is greatly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization.

[0098] From a hardware perspective, in order to improve the efficiency and accuracy of UAV signal detection and synchronization, this application provides an embodiment of an electronic device for implementing all or part of the UAV signal synchronization detection method, wherein the electronic device specifically includes the following: The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the UAV signal synchronization detection method and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the UAV signal synchronization detection method in the present embodiment, and the contents of the embodiments of the UAV signal synchronization detection method are incorporated herein, and repeated details will not be described again.

[0099] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0100] In practical applications, parts of the UAV signal synchronization detection method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0101] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0102] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0103] In one embodiment, the UAV signal synchronization detection method function can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: Step S101: In the first working mode, within a preset silent period of the UAV signal, the presence of a UAV broadcast pulse signal is detected with a first gain. If a UAV broadcast pulse signal is detected, the signal strength of the UAV broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. Step S102: In the second working mode, continuously receive the UAV broadcast pulse signal according to the second gain, perform synchronous detection on the UAV broadcast pulse signal, and adjust the second gain to the third gain according to the detection result after the synchronous detection, enter the third working mode, and continuously receive and track the UAV broadcast signal in the third working mode to realize UAV signal synchronous detection. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0104] As described above, the electronic device provided in this application embodiment performs a blind search for signals with high gain in a first working mode. After identifying a broadcast pulse, it evaluates the signal strength and switches to a second gain, then enters the second working mode. It receives the signal using the adjusted gain and performs synchronization detection. After successful detection, it further adjusts the gain to enter a third working mode, thereby achieving continuous signal tracking and stable reception. In the synchronization detection process, the synchronization sequence in the received signal is correlated with the local reference sequence in the frequency domain. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is greatly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization.

[0105] In another embodiment, the UAV signal synchronization detection method can be configured separately from the central processing unit 9100. For example, the UAV signal synchronization detection method can be configured as a chip connected to the central processing unit 9100, and the function of the UAV signal synchronization detection method can be realized through the control of the central processing unit.

[0106] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technologies.

[0107] like Figure 3 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0108] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0109] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0110] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0111] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0112] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0113] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0114] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the UAV signal synchronization detection method with a server or client as the execution subject in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the UAV signal synchronization detection method with a server or client as the execution subject in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step S101: In the first working mode, within a preset silent period of the UAV signal, the presence of a UAV broadcast pulse signal is detected with a first gain. If a UAV broadcast pulse signal is detected, the signal strength of the UAV broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. Step S102: In the second working mode, continuously receive the UAV broadcast pulse signal according to the second gain, perform synchronous detection on the UAV broadcast pulse signal, and adjust the second gain to the third gain according to the detection result after the synchronous detection, enter the third working mode, and continuously receive and track the UAV broadcast signal in the third working mode to realize UAV signal synchronous detection. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0115] As described above, the computer-readable storage medium provided in this application embodiment performs a blind search of the signal at high gain in a first working mode. After identifying the broadcast pulse, it evaluates the signal strength and switches to a second gain, then enters the second working mode. It receives the signal using the adjusted gain and performs synchronization detection. After successful detection, it further adjusts the gain to enter a third working mode, thereby achieving continuous signal tracking and stable reception. In the synchronization detection process, the synchronization sequence in the received signal is correlated with the local reference sequence in the frequency domain. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is greatly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization.

[0116] Embodiments of this application also provide a computer program product capable of implementing all steps of the UAV signal synchronization detection method with the execution subject being a server or client in the above embodiments. When this computer program / instruction is executed by a processor, it implements the steps of the UAV signal synchronization detection method. For example, the computer program / instruction implements the following steps: Step S101: In the first working mode, within a preset silent period of the UAV signal, the presence of a UAV broadcast pulse signal is detected with a first gain. If a UAV broadcast pulse signal is detected, the signal strength of the UAV broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. Step S102: In the second working mode, continuously receive the UAV broadcast pulse signal according to the second gain, perform synchronous detection on the UAV broadcast pulse signal, and adjust the second gain to the third gain according to the detection result after the synchronous detection, enter the third working mode, and continuously receive and track the UAV broadcast signal in the third working mode to realize UAV signal synchronous detection. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

[0117] As described above, the computer program product provided in this application performs a blind search of the signal with high gain in the first working mode. After identifying the broadcast pulse, it evaluates the signal strength and switches to the second gain, and then enters the second working mode. It receives the signal using the adjusted gain and performs synchronization detection. After successful detection, it further adjusts the gain to enter the third working mode, thereby achieving continuous signal tracking and stable reception. In the synchronization detection process, the synchronization sequence in the received signal is correlated with the local reference sequence in the frequency domain. By resampling the reference sequence to the actual signal sampling rate and using a combination of frequency domain multiplication and time domain overlap addition, the computational complexity is greatly reduced, while ensuring accurate positioning of the synchronization peak. This improves the efficiency and accuracy of UAV signal detection and synchronization.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for detecting unmanned aerial vehicle (UAV) signal synchronization, characterized in that, The method includes: In the first working mode, the drone broadcast pulse signal is detected within a preset silence period of the drone signal with a first gain. If the drone broadcast pulse signal is detected, the signal strength of the drone broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. In the second working mode, the drone broadcast pulse signal is continuously received according to the second gain, and the drone broadcast pulse signal is synchronously detected. Based on the detection result after the synchronous detection, the second gain is adjusted to the third gain, and the third working mode is entered. The drone broadcast signal is continuously received and tracked in the third working mode to realize the synchronous detection of drone signals. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

2. The UAV signal synchronization detection method according to claim 1, characterized in that, The step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm, and determining the corresponding frequency domain correlation result, includes: Based on the sampling rate, determine the corresponding number of Fast Fourier Transform points M; The reference sequence after the sampling rate transformation is padded with zeros to a length of M to determine the corresponding first zero-padded sequence.

3. The UAV signal synchronization detection method according to claim 2, characterized in that, The step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm, and determining the corresponding frequency domain correlation result, further includes: The synchronization sequence is divided into time domains according to a preset length to determine multiple corresponding synchronization sequence segments, wherein the length is determined based on the sampling rate; Each of the aforementioned synchronization sequence segments is padded with zeros to a length of M to determine the corresponding multiple second zero-padding sequences.

4. The UAV signal synchronization detection method according to claim 3, characterized in that, The step of calculating the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence according to the frequency domain correlation algorithm, and determining the corresponding frequency domain correlation result, further includes: Perform M-point Fast Fourier Transform on the first zero-padded sequence and each of the second zero-padded sequences respectively to determine the corresponding first frequency domain sequence and multiple second frequency domain sequences; Based on the first frequency domain sequence, each of the second frequency domain sequences is multiplied point by point in the frequency domain to determine the corresponding multiple frequency domain correlation results.

5. The UAV signal synchronization detection method according to claim 4, characterized in that, The step of merging the time-domain correlation results according to a preset overlap-addition algorithm, and determining the corresponding detection result after synchronous detection based on the peak position of the merged time-domain correlation results, includes: Add the first X data points of the time-domain correlation result of the current synchronization sequence segment to the last X data points of the time-domain correlation result obtained in the previous calculation point by point to determine the corresponding time-domain cumulative result sequence; After continuously summing the time-domain correlation results of all synchronization sequence segments point by point, the corresponding total time-domain accumulation result sequence is determined; In the total accumulated result sequence in the time domain, the maximum peak value is searched, and the time domain index position corresponding to the maximum peak value position is the precise synchronization position of the UAV synchronization sequence. The drone signal is synchronized based on the location of the maximum peak value.

6. The UAV signal synchronization detection method according to claim 1, characterized in that, In the first operating mode, detecting the presence of a drone broadcast pulse signal within a preset silence period of the drone signal with a first gain includes: Set a time detection window, the duration of which is greater than or equal to the silence period of the UAV signal; In the first working mode, within the time detection window, radio frequency signals are continuously received at a first gain to detect whether there is a drone broadcast pulse signal. The first working mode is a search mode, and the first gain is a fixed maximum gain, which is used to determine and detect drone signals over a wide range.

7. The UAV signal synchronization detection method according to claim 5, characterized in that, The step of adjusting the second gain to the third gain based on the detection result after the synchronization detection includes: Peak energy is extracted from the time-domain total accumulation result sequence obtained after the synchronous detection to determine the corresponding signal strength index; If the signal strength index is higher than the upper limit of the preset target reception range, then the second gain is reduced and the corresponding third gain is determined; If the signal strength index is lower than the preset target reception range lower limit, then the second gain is increased, and the corresponding third gain is determined.

8. A UAV signal synchronization detection device, characterized in that, The device includes: The signal search and determination module is used to detect whether there is a drone broadcast pulse signal within a preset silence period of the drone signal in the first working mode with a first gain. If a drone broadcast pulse signal is detected, the signal strength of the drone broadcast pulse signal is evaluated, and the first gain is adjusted to a second gain based on the evaluation result, and the second working mode is entered. The signal acquisition and tracking module is used to continuously receive the UAV broadcast pulse signal according to the second gain in the second working mode, and to perform synchronous detection on the UAV broadcast pulse signal. Based on the detection result after the synchronous detection, the second gain is adjusted to the third gain, and the module enters the third working mode to continuously receive and track the UAV broadcast signal in the third working mode, thereby realizing synchronous detection of UAV signals. The synchronous detection of the UAV broadcast pulse signal includes: Obtain the synchronization sequence contained in the UAV broadcast pulse signal; The preset reference sequence is resampled according to the actual sampling rate of the synchronization sequence so that the sampling rate of the reference sequence is consistent with the sampling rate of the synchronization sequence; Based on the frequency domain correlation algorithm, the correlation between the reference sequence after the sampling rate transformation and the synchronization sequence is calculated to determine the corresponding frequency domain correlation result. Perform an inverse Fourier transform on the frequency domain correlation results to determine the corresponding time domain correlation results; The time-domain correlation results are merged according to a preset overlapping addition algorithm, and the corresponding detection result after synchronous detection is determined based on the peak position of the merged time-domain correlation results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the UAV signal synchronization detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the UAV signal synchronization detection method according to any one of claims 1 to 7.

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