Unmanned aerial vehicle dynamic SFO compensation method and system based on combination of frequency domain and time domain

Abstract

The application discloses a dynamic SFO compensation method and system for an unmanned aerial vehicle based on combination of a frequency domain and a time domain, and the method comprises the following steps: acquiring a radio frequency signal by using the unmanned aerial vehicle, and performing frequency down conversion on the radio frequency signal to obtain a baseband signal; performing synchronous processing on the baseband signal to obtain a synchronization parameter; extracting a pilot sequence from the baseband signal based on the synchronization parameter, and calculating a sampling frequency offset based on the pilot sequence; calculating a resampling ratio based on the sampling frequency offset; and performing resampling on the baseband signal according to the resampling ratio to obtain a baseband signal after SFO compensation. The application can realize accurate SFO compensation and reduce the communication performance decline of the unmanned aerial vehicle caused by the SFO.

Description

Technical Field

[0001] This invention relates to a method and system for dynamic SFO compensation of unmanned aerial vehicles (UAVs) based on a combination of frequency and time domains, and belongs to the field of UAV communication. Background Technology

[0002] Compared to the limited payload and capabilities of a single drone, multiple drones can enhance system efficiency through complementary capabilities and coordinated actions. Therefore, drone applications are gradually shifting from single-platform to multi-platform deployments. Ambient backscatter communication (A2C) offers a promising approach for large-scale AIoT connectivity through passive operation. Backscatter tags do not actively transmit but modulate and reflect ambient radio frequency signals, harvesting energy from these waves and using load modulation to encode data. This architecture eliminates energy-intensive components such as oscillators and power amplifiers, significantly reducing power consumption and manufacturing costs. Therefore, AIoT drone systems combining drones and AIoT systems hold broad prospects and immense value.

[0003] While AIoT drone systems offer significant advantages, their communication reliability can be compromised by signal degradation, particularly by Sampling Frequency Offset (SFO) caused by differences and jitter in the sampling clock. SFO compensation specifically designed for resource-constrained AIoT drone systems remains a critical and unresolved research gap. Existing SFO solutions are primarily developed for active communication systems and are typically computationally intensive, making them unsuitable for the simplified and power-constrained hardware of typical AIoT devices. Furthermore, SFO compensation introduces a critical problem—the time misalignment between the transmitted and received signal waveforms. This time skew is particularly detrimental to common backscattering techniques, such as correlation-based synchronization and detection. In these methods, system features such as steering sequences or frame preambles are identified by associating the received signal with a known local copy; the SFO introduces a gradual drift relative to the expected sequence at the moment of sampling. This time drift causes significant decay and amplification of correlation peaks, thereby reducing synchronization accuracy and the probability of successful detection.

[0004] Therefore, in order to overcome the limitations of traditional control methods and fill the gap in SFO compensation for AIoT UAV systems, a dynamic SFO compensation method and system for UAVs based on the combination of frequency and time domains is proposed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and to provide a dynamic SFO compensation method and system for UAVs based on the combination of frequency and time domains. This method resamples the baseband signal by resampling the resampling ratio, thereby achieving accurate SFO compensation and reducing the degradation of UAV communication performance caused by SFO.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a dynamic SFO compensation method for unmanned aerial vehicles based on a combination of frequency and time domains, including:

[0008] The radio frequency signal is acquired using a drone, and the radio frequency signal is down-converted to obtain a baseband signal;

[0009] The baseband signal is synchronized to obtain synchronization parameters;

[0010] Based on synchronization parameters, pilot sequences are extracted from the baseband signal, and the sampling frequency offset is calculated based on the pilot sequences.

[0011] Calculate the resampling ratio based on the sampling frequency offset;

[0012] The baseband signal is resampled according to the resampling ratio to obtain the SFO-compensated baseband signal.

[0013] Optionally, the baseband signal is represented as:

[0014] ;

[0015] In the formula, This represents the nth sample point in the baseband signal; M represents the transmit power. This represents the channel coefficient between the radio frequency source and the reader in an AIoT drone communication system; This represents the channel coefficient between the passive tag and the reader in an AIoT drone communication system. This represents the channel coefficient between the radio frequency source and the passive tag in the AIoT drone communication system; the AIoT drone communication system includes a radio frequency source, a passive tag, and a reader; Indicates the transmission tag data symbol Reflection coefficient at time: This represents the noise at the nth sample point; Indicates the ideal sampling interval; Indicates the sampling clock offset; Indicates consideration and Then, the label data symbol for the nth sample point.

[0016] Optionally, the baseband signal is synchronized to obtain synchronization parameters; the synchronization parameters include a primary sample index. and pilot sequence sample number The synchronization process includes:

[0017] S21: Set the initial noise stage in the baseband signal and calculate the noise power of the initial noise stage;

[0018] S22: Calculate the adaptive detection threshold using the noise power;

[0019] S23: Get the set number of consecutive samples Filtering continuous samples starting from the beginning of the sample. The initial sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the initial sample segment is used as the primary sample index. ;

[0020] S24: Set search window parameters ,exist + Start filtering from position consecutive A fixed sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the sample in the fixed sample segment is used as the secondary sample index. ;

[0021] S25: Calculate the measured period based on the primary sample index and the secondary sample index;

[0022] S26: Determine whether the measured period is within the preset period range. If it is, calculate the number of pilot sequence samples based on the measured period and the number of pilot symbols. If not, adjust the number of consecutive samples. for +1 or adjust search window parameters And repeat steps S23-S26.

[0023] Optionally, the primary sample index The formula is expressed as:

[0024] ;

[0025] ;

[0026] In the formula, Indicates the number of consecutive samples; Indicates the adaptive detection threshold; Let represent the instantaneous energy of the (n+i)th sample; Indicates the intersection operation; This represents the (n+i)th sample point in the baseband signal.

[0027] Optionally, the secondary sample index The formula is expressed as:

[0028] ;

[0029] In the formula, Indicates the secondary sample index; Indicates search window parameters; Represents the logical AND operation.

[0030] Optionally, the search window parameters The adjustment range and the formula for the preset period range are expressed as follows:

[0031] ;

[0032] ;

[0033] In the formula, Indicates the known nominal period length; Indicates the actual measurement period.

[0034] Optionally, calculating the sampling frequency offset based on the pilot sequence includes:

[0035] Obtain the nominal pilot frequency;

[0036] Perform a Fourier transform on the pilot sequence to obtain the pilot spectrum, and calculate the pilot frequency based on the pilot spectrum;

[0037] Calculate the sampling frequency offset based on the nominal pilot frequency, pilot frequency, and ideal sampling frequency.

[0038] Optionally, the formula for calculating the sampling frequency offset is expressed as follows:

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] In the formula, Indicates the sampling frequency offset; Indicates the ideal sampling frequency; Indicates the ideal sampling interval; Indicates the pilot frequency; Indicates the nominal pilot frequency; The k-th component in the pilot spectrum is represented by argmax; argmax represents the peak function. Indicates the number of pilot sequence samples; Indicates the first pilot sequence One pilot sample; It represents the imaginary unit.

[0044] Optionally, the resampling ratio can be calculated using the following formula:

[0045] ;

[0046] ;

[0047] In the formula, Indicates the resampling ratio; This represents the actual sampling rate.

[0048] Secondly, the present invention provides a dynamic SFO compensation system for unmanned aerial vehicles based on a combination of frequency and time domains, comprising:

[0049] The downconversion module is used to acquire radio frequency signals using a drone and downconvert the radio frequency signals to obtain baseband signals.

[0050] The synchronization processing module is used to synchronize the baseband signal and obtain synchronization parameters;

[0051] The offset calculation module is used to extract pilot sequences from the baseband signal based on synchronization parameters, and to calculate the sampling frequency offset based on the pilot sequences;

[0052] The sampling ratio calculation module is used to calculate the resampling ratio based on the sampling frequency offset;

[0053] The compensation module is used to resample the baseband signal according to the resampling ratio to obtain the SFO-compensated baseband signal.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0055] This invention proposes a dynamic SFO compensation method and system for UAVs based on a combination of frequency and time domains. The method first receives a radio frequency (RF) signal based on an AIoT UAV communication system and down-converts it to obtain a baseband signal. Then, it performs synchronization processing on the baseband signal, eliminating dependence on fine phase or waveform details and accurately locating the start position of a complete data packet. Pilot sequences are extracted after synchronization processing. Finally, the sampling frequency offset is calculated based on the pilot sequence to obtain the resampling ratio. The baseband signal is then resampled, and the sampling time is dynamically adjusted directly in the time domain to correct accumulated errors in real time, resulting in the SFO-compensated baseband signal. This invention organically combines frequency domain estimation and time domain compensation, effectively overcoming the limitations of traditional control methods, improving the accuracy of SFO estimation, mitigating the performance degradation of UAV communication caused by SFO, and significantly enhancing the reliability and feasibility of AIoT UAV communication systems. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0057] Figure 1 The flowchart shown is a method for dynamic SFO compensation of unmanned aerial vehicles based on the combination of frequency domain and time domain in an embodiment of the present invention.

[0058] Figure 2 The diagram shown is a schematic representation of the synchronization processing flow in an embodiment of the present invention.

[0059] Figure 3 The diagram shown illustrates the relationship between decision success rate and scaling factor K under different signal-to-noise ratios in an embodiment of the present invention with 10% SFO.

[0060] Figure 4 The diagram shown illustrates the relationship between MSE and FFT points under various SFO conditions in this embodiment of the invention.

[0061] Figure 5 The diagram shown illustrates the relationship between bit error rate performance and signal-to-noise ratio under different configurations in this embodiment of the invention. Detailed Implementation

[0062] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0063] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0064] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0065] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0066] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0067] Example 1

[0068] like Figure 1 As shown, this embodiment of the invention provides a dynamic SFO compensation method for UAVs based on a combination of frequency and time domains, including the following steps:

[0069] S1: Use a drone to acquire radio frequency signals and down-convert the radio frequency signals to obtain baseband signals;

[0070] S2: Perform synchronization processing on the baseband signal to obtain synchronization parameters;

[0071] S3: Based on the synchronization parameters, extract the pilot sequence from the baseband signal, and calculate the sampling frequency offset based on the pilot sequence;

[0072] S4: Calculate the resampling ratio based on the sampling frequency offset;

[0073] S5: Resample the baseband signal according to the resampling ratio to obtain the SFO-compensated baseband signal.

[0074] In this embodiment, step S1 involves using a drone to acquire radio frequency signals and down-converting the radio frequency signals to obtain baseband signals, including:

[0075] S11. Define the carrier signal transmitted by the radio frequency source of the AIoT drone communication system. :

[0076]

[0077] in, Indicates the transmission power; For carrier frequency, Indicates the initial phase; It is a time variable; It is the imaginary unit;

[0078] S12. Define the signal input to the passive tag of the AIoT drone communication system. :

[0079]

[0080] in, This represents the channel coefficient between the radio frequency source and the passive tag;

[0081] S13. After receiving the carrier signal, the passive tag dynamically adjusts its reflection coefficient to modulate its own data onto the carrier. Therefore, the passive tag's modulated signal can be represented as... :

[0082]

[0083] in, Indicates the symbol for the label data; Represents the transmission tag data symbol Reflectance coefficient at time; The expression is:

[0084]

[0085] in, and Respectively represent and The relevant load impedance and tag antenna impedance;

[0086] S14. The reader of the AIoT drone communication system receives the superposition of the signal backscattered from the passive tag and the direct path signal from the radio frequency source. This combined received signal at the reader, i.e., the radio frequency signal, can be expressed as:

[0087]

[0088] in, This represents the channel coefficient between the radio frequency source and the reader in an AIoT drone communication system; This represents the channel coefficient between the passive tag and the reader in an AIoT drone communication system. This indicates that the mean is zero and the variance is... Gaussian white noise;

[0089] S15. Due to the presence of SFO, the demodulated (down-converted) baseband signal at the reader can be expressed as:

[0090]

[0091] In the formula, This represents the nth sample point in the baseband signal; the AIoT drone communication system includes a radio frequency source, a passive tag, and a reader. Indicates the transmission tag data symbol Reflectance coefficient at time; This represents the noise at the nth sample point; Indicates the ideal sampling interval; Indicates the sampling clock offset; Indicates consideration and Then, the label data symbol for the nth sample point.

[0092] In this embodiment, step S1 effectively characterizes an AIoT drone communication system by defining parameters such as the carrier signal transmitted by the radio frequency source, the signal input to the passive tag, the passive tag modulation signal, and the reflection coefficient. This establishes a theoretical foundation and a quantitative description of the problem, and obtains the baseband signal, which facilitates the accurate quantification of the impact of SFO on the performance of the AIoT drone communication system.

[0093] like Figure 2 As shown, in this embodiment, step S2 performs synchronization processing on the baseband signal to obtain synchronization parameters; the synchronization parameters include the primary sample index. and pilot sequence sample number The synchronization process includes:

[0094] S21: Set the initial noise stage in the baseband signal and calculate the noise power of the initial noise stage;

[0095] Specifically, assuming the baseband signal is in the front Each sample represents the initial noise stage, and the noise power is calculated based on the following formula:

[0096]

[0097] in, This represents the noise power during the initial noise phase. This represents the number of samples in the initial noise phase of the baseband signal. 1 represents Instantaneous energy; The first noise stage in the baseband signal represents the initial noise phase. One sample;

[0098] S22: Calculate the adaptive detection threshold using the noise power;

[0099] Specifically, the formula for calculating the adaptive detection threshold is as follows:

[0100]

[0101] in, Indicates the adaptive detection threshold; The scaling factor was determined through Monte Carlo simulation.

[0102] S23: Get the set number of consecutive samples Filtering continuous samples starting from the beginning of the sample. The initial sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the initial sample segment is used as the primary sample index. ;

[0103] Specifically, the primary sample index The formula is expressed as:

[0104] ;

[0105] ;

[0106] In the formula, Indicates the primary sample index; Indicates the number of consecutive samples. The choice is a trade-off between detection robustness and potential latency, and can be configured according to specific operating scenarios and reliability requirements; Indicates the adaptive detection threshold; Let represent the instantaneous energy of the (n+i)th sample; Indicates the intersection operation; This represents the (n+i)th sample point in the baseband signal.

[0107] S24: Set search window parameters ,exist + Start filtering from position consecutive A fixed sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the sample in the fixed sample segment is used as the secondary sample index. ;

[0108] Specifically, secondary sample index The formula is expressed as:

[0109] ;

[0110] In the formula, Indicates the secondary sample index; Indicates search window parameters; Represents the logical AND operation.

[0111] S25: Calculate the measured period based on the primary sample index and the secondary sample index;

[0112] S26: Determine whether the measured period is within the preset period range. If it is, calculate the number of pilot sequence samples based on the measured period and the number of pilot symbols. If not, adjust the number of consecutive samples. for +1 or adjust search window parameters And repeat steps S23-S26.

[0113] Specifically, search window parameters The formula for adjusting the range is expressed as follows:

[0114] ;

[0115] For symbol frequency offset coefficient In this case, these boundary values ​​are determined by the extreme case substitution method, and The range of variation can be adjusted according to different operating scenarios;

[0116] ;

[0117] In summary: ;

[0118] In the formula, Indicates the known nominal period length;

[0119] Specifically, the formula for the preset period range is expressed as follows:

[0120] ;

[0121] In the formula, Indicates the actual measurement period.

[0122] In this embodiment, by employing the above-described scheme, and utilizing the characteristic that the energy when there is a signal is much higher than the energy when there is only noise, the position of the beginning of a complete data packet (primary sample index) can be accurately located without relying on the fine phase or waveform of the signal. This solves the problem of peak attenuation and drift in traditional UAV communication SFO compensation methods, which prevents the correct frame header from being found.

[0123] In this embodiment, step S3, based on synchronization parameters, extracts a pilot sequence from the baseband signal and calculates the sampling frequency offset based on the pilot sequence, specifically including:

[0124] 1) Extract pilot sequence:

[0125] Based on the primary sample index obtained in S2 (Frame start point) and number of pilot sequence samples (Pilot sequence length), so we can extract the pilot sequence portion from the baseband signal y[n], denoted as , =0,1,...,Ns-1;

[0126] 2) Calculate the sampling frequency offset:

[0127] S321: Obtain the nominal pilot frequency;

[0128] Specifically, the nominal pilot frequency is obtained directly through the protocol or through ideal pilot analysis without sampling frequency offset:

[0129] Ideal pilot analysis without sampling frequency offset: When there is no sampling frequency offset (denoted as...) When =0), let Indicates the ideal sampling frequency The received ideal discrete-time pilot tone. The length of this pilot sequence is... Each sampling point. Through the ideal pilot... implement The point fast Fourier transform is used to analyze its spectral components, namely:

[0130]

[0131] in, Represents the first tone in the single-tone spectrum of an ideal discrete-time pilot. One component; The first tone of an ideal discrete-time pilot tone d represents the... One sampling point.

[0132] The nominal pilot frequency is determined by finding the frequency bin index corresponding to the largest amplitude value in the ideal discrete-time pilot monotone spectrum. ,Right now:

[0133]

[0134] Where argmax represents the peak function; Indicates the ideal sampling frequency; ; Indicates the ideal sampling interval;

[0135] S322: Perform a Fourier transform on the pilot sequence to obtain the pilot spectrum, and calculate the pilot frequency based on the pilot spectrum;

[0136] Specifically,

[0137] ;

[0138] ;

[0139] In the formula, Indicates the pilot frequency; The k-th component in the pilot spectrum is represented by argmax; argmax represents the peak function. Indicates the number of pilot sequence samples; Indicates the first pilot sequence One pilot sample; It represents the imaginary unit.

[0140] S323: Calculate the sampling frequency offset based on the nominal pilot frequency, pilot frequency and ideal sampling frequency.

[0141] Specifically, the formula for calculating the sampling frequency offset is as follows:

[0142] ;

[0143] In the formula, Indicates the sampling frequency offset;

[0144] In this embodiment, the formula for calculating the resampling ratio in step S4 is expressed as follows:

[0145] ;

[0146] ;

[0147] In the formula, Indicates the resampling ratio; This represents the actual sampling rate.

[0148] SFO estimation is performed in the frequency domain using the periodicity of the pilot sequence. Frequency domain methods are insensitive to sampling time drift and are more suitable for low signal-to-noise ratio AIoT drone communication environments. Once the SFO size is estimated, i.e. Subsequently, the sampling time is dynamically adjusted directly in the time domain to correct the accumulated error in real time. The distortion of the baseband signal waveform after SFO compensation is significantly reduced, providing high-quality input for subsequent system channel estimation, equalization, and decoding.

[0149] In this embodiment, when the baseband signal is resampled according to the resampling ratio in step S5, the resampling ratio is usually an irrational number. At this time, it is necessary to use the continued fraction approximation method to find the closest rational number to represent the resampling ratio. The continued fraction approximation method is common knowledge to those skilled in the art and will not be described in detail here.

[0150] Specifically, resampling can be achieved through interpolation, including linear interpolation, cubic spline interpolation, and polynomial interpolation; interpolation methods are common knowledge to those skilled in the art and will not be elaborated here.

[0151] Example 2

[0152] This embodiment also proposes a dynamic SFO compensation method for UAVs based on a combination of frequency and time domains, based on Embodiment 1. An AIoT UAV communication system was constructed for simulation verification. The system includes one radio frequency source, one passive tag, and one UAV node equipped with a reader. Various sampling frequency offset (SFO) conditions were set in the simulation, including rational SFOs of 0.1%, 1%, 5%, and 10%, and an irrational SFO of 1.0003%. Performance was evaluated under different signal-to-noise ratio (SNR) conditions (9dB, 10dB, 12dB), as detailed below:

[0153] like Figure 3 The diagram shows the relationship between decision success rate and scaling factor K under different signal-to-noise ratios under 10% SFO conditions.

[0154] The effect of scaling factor K on synchronization performance for different signal-to-noise ratio (SNR) values ​​of 9dB, 10dB, and 12dB shows that for all tested SNR levels, the success rate increases sharply as the scaling factor K increases from 1 to approximately 3. This indicates that very low scaling factors K lead to poor performance, possibly due to a high false alarm rate. Performance peaks or plateaus are observed in the range of K=3 to K=5. At SNR=12dB, the success rate is close to 1 and remains high when K>3. However, for lower SNRs (9dB and 10dB), increasing K beyond the optimal range (approximately 3-5) results in a gradual decrease in success rate. This suggests that excessively high thresholds lead to increased false negative rates at lower SNRs. The results indicate that there is an optimal range between K=3 and K=5, within which the system achieves an optimal balance between high detection rate and low false alarm rate. This general conclusion provides clear guidance for parameter settings under different SNR conditions, ensuring the robustness of the method in this invention under varying channel conditions.

[0155] Figure 4 The relationship between the mean squared error (MSE) of the proposed SFO estimation algorithm and different Fast Fourier Transform (FFT) sizes is presented, covering four SFO magnitudes (0.1%, 1%, 5%, and 10%). Experimental results reveal a consistent trend: for the same FFT size, larger SFOs exhibit higher MSE values. This behavior arises because stronger frequency offsets (e.g., a 10% SFO) produce more significant spectral characteristics in the received signal, enabling more accurate frequency estimation under fixed computational constraints. Conversely, smaller offsets (e.g., a 0.1% SFO) require significantly larger FFT sizes to achieve comparable accuracy due to their weaker spectral characteristics. Therefore, the method of this invention can achieve effective SFO estimation at different FFT sizes. For large offsets (e.g., a 10% SFO), high accuracy can be achieved even with a smaller computational load (number of FFT points); for small offsets, the accuracy requirements can be met by increasing computational resources (larger FFTs), demonstrating the flexibility of the method.

[0156] Figure 5The relationship between bit error rate (BER) performance and signal-to-noise ratio (SNR) is illustrated for three different configurations: (I) a 10% symbol frequency offset (SFO) without compensation; (II)-(III) systems with 1% and 10% SFO respectively, both using the proposed estimation and compensation scheme; (IV) a system with approximately 1.0003% irrational SFO with compensation; and (V) a system with 10% SFO, configured for frame synchronization using a related method. As can be seen from the figure, the frequency- and time-domain combined UAV dynamic SFO compensation method proposed in this invention not only effectively compensates for SFO of conventional proportions (0.1%-10%), but more importantly, successfully solves the practical challenge of handling irrational SFO (such as 1.0003%), which is difficult for traditional methods to handle. Figure 5 The results show that even under irrational SFO, the BER performance can still be close to ideal synchronization after compensation. This verifies the excellent adaptability of the proposed method to complex and non-ideal frequency offsets, which is of great significance for the actual communication environment of dynamic platforms such as UAVs.

[0157] Example 3

[0158] This embodiment also proposes a UAV dynamic SFO compensation system based on frequency domain and time domain combination, used to implement the UAV dynamic SFO compensation method based on frequency domain and time domain combination proposed in Embodiment 1, including:

[0159] The downconversion module is used to acquire radio frequency signals using a drone and downconvert the radio frequency signals to obtain baseband signals.

[0160] The synchronization processing module is used to synchronize the baseband signal and obtain synchronization parameters;

[0161] The offset calculation module is used to extract pilot sequences from the baseband signal based on synchronization parameters, and to calculate the sampling frequency offset based on the pilot sequences;

[0162] The sampling ratio calculation module is used to calculate the resampling ratio based on the sampling frequency offset;

[0163] The compensation module is used to resample the baseband signal according to the resampling ratio to obtain the SFO-compensated baseband signal.

[0164] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0165] Example 4

[0166] This invention provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of the UAV dynamic SFO compensation method based on frequency domain and time domain combination as described in Embodiment 1.

[0167] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0168] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] 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.

[0170] 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.

[0171] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A dynamic SFO compensation method for UAVs based on frequency domain and time domain combination, characterized in that, include: The radio frequency signal is acquired using a drone, and the radio frequency signal is down-converted to obtain a baseband signal; The baseband signal is synchronized to obtain synchronization parameters; Based on synchronization parameters, pilot sequences are extracted from the baseband signal, and the sampling frequency offset is calculated based on the pilot sequences. Calculate the resampling ratio based on the sampling frequency offset; The baseband signal is resampled according to the resampling ratio to obtain the SFO-compensated baseband signal. The synchronization parameters include the primary sample index. and pilot sequence sample number ; The synchronization process includes: S21: Set the initial noise stage in the baseband signal and calculate the noise power of the initial noise stage; S22: Calculate the adaptive detection threshold using the noise power; S23: Get the set number of consecutive samples Filtering continuous samples starting from the beginning of the sample. The initial sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the initial sample segment is used as the primary sample index. ; S24: Set search window parameters ,exist + Start filtering from position consecutive A fixed sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the sample in the fixed sample segment is used as the secondary sample index. ; S25: Calculate the measured period based on the primary sample index and the secondary sample index; S26: Determine whether the measured period is within the preset period range. If it is, calculate the number of pilot sequence samples based on the measured period and the number of pilot symbols. If not, adjust the number of consecutive samples. for +1 or adjust search window parameters And repeat steps S23-S26.

2. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 1, characterized in that, The baseband signal is represented as: ； In the formula, This represents the nth sample point in the baseband signal; M represents the transmit power. This represents the channel coefficient between the radio frequency source and the reader in an AIoT drone communication system; This represents the channel coefficient between the passive tag and the reader in an AIoT drone communication system. This represents the channel coefficient between the radio frequency source and the passive tag in the AIoT drone communication system; the AIoT drone communication system includes a radio frequency source, a passive tag, and a reader; Indicates the transmission tag data symbol Reflection coefficient at time; This represents the noise at the nth sample point; Indicates the ideal sampling interval; Indicates the sampling clock offset; Indicates consideration and Then, the label data symbol for the nth sample point.

3. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 2, characterized in that, The primary sample index The formula is expressed as: ； ； In the formula, Indicates the number of consecutive samples; Indicates the adaptive detection threshold; Let represent the instantaneous energy of the (n+i)th sample; Indicates the intersection operation; This represents the (n+i)th sample point in the baseband signal.

4. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 3, characterized in that, The secondary sample index The formula is expressed as: ； In the formula, Indicates the secondary sample index; Indicates search window parameters; Represents the logical AND operation.

5. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 4, characterized in that, The search window parameters The adjustment range and the formula for the preset period range are expressed as follows: ； ； In the formula, Indicates the known nominal period length; Indicates the actual measurement period.

6. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 5, characterized in that, Calculating the sampling frequency offset based on the pilot sequence includes: Obtain the nominal pilot frequency; Perform a Fourier transform on the pilot sequence to obtain the pilot spectrum, and calculate the pilot frequency based on the pilot spectrum; Calculate the sampling frequency offset based on the nominal pilot frequency, pilot frequency, and ideal sampling frequency.

7. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 6, characterized in that, The formula for calculating the sampling frequency offset is as follows: ； ； ； ； In the formula, Indicates the sampling frequency offset; Indicates the ideal sampling frequency; Indicates the ideal sampling interval; Indicates the pilot frequency; Indicates the nominal pilot frequency; This represents the k-th component in the pilot spectrum; argmax represents the peak function; Indicates the number of pilot sequence samples; Indicates the first pilot sequence One pilot sample; It represents the imaginary unit.

8. The UAV dynamic SFO compensation method based on frequency domain and time domain combination according to claim 7, characterized in that, The formula for calculating the resampling ratio is expressed as: ； ； In the formula, Indicates the resampling ratio; This represents the actual sampling rate.

9. A UAV dynamic SFO compensation system based on frequency domain and time domain combination, employing the UAV dynamic SFO compensation method based on frequency domain and time domain combination as described in any one of claims 1-8, characterized in that, include: The downconversion module is used to acquire radio frequency signals using a drone and downconvert the radio frequency signals to obtain baseband signals. The synchronization processing module is used to synchronize the baseband signal and obtain synchronization parameters; The offset calculation module is used to extract pilot sequences from the baseband signal based on synchronization parameters, and to calculate the sampling frequency offset based on the pilot sequences; The sampling ratio calculation module is used to calculate the resampling ratio based on the sampling frequency offset; The compensation module is used to resample the baseband signal according to the resampling ratio to obtain the SFO-compensated baseband signal; The synchronization parameters include the primary sample index. and pilot sequence sample number ; The synchronization process includes: S21: Set the initial noise stage in the baseband signal and calculate the noise power of the initial noise stage; S22: Calculate the adaptive detection threshold using the noise power; S23: Get the set number of consecutive samples Filtering continuous samples starting from the beginning of the sample. The initial sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the initial sample segment is used as the primary sample index. ; S24: Set search window parameters ,exist + Start filtering from position consecutive A fixed sample segment whose instantaneous energy exceeds the adaptive detection threshold is selected, and the starting position of the sample in the fixed sample segment is used as the secondary sample index. ; S25: Calculate the measured period based on the primary sample index and the secondary sample index; S26: Determine whether the measured period is within the preset period range. If it is, calculate the number of pilot sequence samples based on the measured period and the number of pilot symbols. If not, adjust the number of consecutive samples. for +1 or adjust search window parameters And repeat steps S23-S26.

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