Multistage correlation preamble detection method and system based on ZC sequence differential coding, related equipment and medium

Abstract

The invention discloses a multistage correlation preamble detection method and system based on ZC sequence differential coding, related equipment and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: generating a complex ZC sequence, and obtaining a binary real number lead code sequence through differential coding and symbol judgment; performing down-sampling and filtering on the received oversampling signal, and then calculating baseband signal energy through a sliding window; dividing a local lead code and a receiving signal segment into M sub-segments for correlation operation, and accumulating absolute values to obtain a synthetic correlation value; and combining sliding window energy to normalize a synthetic correlation value, and finally detecting a normalized sequence peak value to determine the initial position of a lead code. According to the invention, the low signal-to-noise ratio adaptability and the anti-frequency offset and anti-multipath capabilities are greatly improved, the calculation complexity is reduced, the resource-limited equipment is adapted, the precise detection of the lead code is realized, and the reliable establishment of a communication link under a complex channel is ensured.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to a method, system, related equipment, and medium for detecting multi-level correlated preambles based on ZC sequence differential coding. Background Technology

[0002] Preamble synchronization is the primary core component of the physical layer protocol stack in wireless communication. It is a fundamental prerequisite for initiating subsequent carrier synchronization, frame synchronization, and data demodulation. Its detection performance directly determines whether the communication link can be established quickly and stably, and is a crucial prerequisite for ensuring the transmission reliability and data integrity of the entire communication system. Traditional Bluetooth Low Energy (BLE) systems, to simplify hardware implementation, generally use short-period pseudo-random sequences with fixed structures as preambles, typically such as alternating binary sequences like "10101010" or "01010101". This design can meet basic synchronization requirements in ideal short-range, low-interference communication scenarios. However, in the complex channel environments of low-Earth orbit satellite IoT communication and long-range Bluetooth communication, its inherent defects are significantly amplified, facing insurmountable technical bottlenecks, specifically manifested as follows:

[0003] 1. Insufficient adaptability to low signal-to-noise ratio: Due to the huge path loss of low-Earth orbit satellite links and the signal attenuation of Bluetooth long-distance transmission, the channel signal-to-noise ratio is often lower than -5dB. Traditional preamble autocorrelation side lobes are high and are easily confused with the main peak under low signal-to-noise ratio, which greatly reduces the link establishment success rate. 2. Weak resistance to frequency offset: In low-orbit communication scenarios, Doppler frequency offset can reach ±50kHz. In Bluetooth communication, there is also a device crystal oscillator frequency offset. Traditional preamble detection methods are highly sensitive to frequency offset. When frequency offset exists, the detection probability drops significantly, and additional frequency offset compensation operations will increase system latency and hardware complexity. 3. High computational complexity: The sliding correlation operation used in traditional preamble detection has a computational complexity of O(N²), which is difficult to meet the real-time signal processing requirements in resource-constrained on-board processing units and IoT terminals. 4. Insufficient multipath robustness: Traditional peak detection algorithms are susceptible to multipath interference, which can cause timing synchronization errors to exceed one symbol period, thereby increasing the bit error rate of subsequent signal demodulation by an order of magnitude.

[0004] There is currently no effective solution to address the shortcomings of traditional fixed preamble detection methods, which are difficult to adapt to the complex channel requirements of low-Earth orbit satellite IoT and long-distance Bluetooth communication. Therefore, a high-performance, low-complexity preamble detection method is urgently needed. Summary of the Invention

[0005] In view of the above problems, the present invention provides a multi-level correlation preamble detection method, system, related equipment and medium based on ZC sequence differential coding, aiming to achieve: maintaining high detection probability under extremely low signal-to-noise ratio; having natural robustness to large carrier frequency offsets above ±50kHz without introducing additional processing delay; significantly reducing computational complexity and adapting to the resource constraints of satellite platforms and IoT terminals; and improving timing accuracy under multipath interference.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a multi-level correlation preamble detection method based on ZC sequence differential coding, comprising the following steps: Preamble generation: Generate a complex ZC sequence, and perform differential encoding and sign decision on the ZC sequence to obtain a binary real number preamble sequence; Signal preprocessing: The received oversampled signal is downsampled and filtered to obtain the baseband signal, and then the energy of the baseband signal is calculated using a sliding window. Segmented correlation operation: The preamble sequence stored locally and the received signal segment in the sliding window are divided into M sub-segments, where M is a positive integer. The correlation value of each pair of sub-segments is calculated, and the absolute values ​​of the correlation values ​​of all sub-segments are summed to obtain the composite correlation value. Normalization: The energy of the sliding window is used to normalize the synthesized correlation values ​​to obtain normalized correlation values; Peak detection and localization: Peak detection is performed on the normalized correlation value sequence to determine the peak point that exceeds the threshold and is a local maximum. The position corresponding to the peak point is the preamble start position.

[0007] Furthermore, the preamble generation step specifically includes: S110, According to the formula Generate a complex ZC sequence z of length N, where, is the root index, j is the imaginary unit, n is the sequence index, and N is the length of the ZC sequence; S120. Perform a difference operation on the complex ZC sequence: The difference sequence is obtained. Where n is the sequence index, for Conjugate; S130, for the difference sequence Perform a sign determination on the real part of each element. If Output bit 1 if Output bit 0; finally, a binary real number preamble sequence is generated.

[0008] Furthermore, the signal preprocessing step specifically includes: S210. The 8x oversampled signal is downsampled and converted into a 1x baseband signal. S220. A low-pass filter is used to filter the baseband signal to suppress high-frequency noise, ionospheric scintillation interference and scattering clutter; S230. Use a sliding window with a length consistent with the local ZC sequence, according to the formula... Calculate the energy of the baseband signal, where k is the starting index of the sliding window, i is the sampling point number of the baseband signal, N is the length of the ZC sequence, and x(i) is the value of the i-th sampling point of the baseband signal.

[0009] Furthermore, the segmentation correlation calculation steps specifically include: S310. Divide the sliding window segment of the local preamble sequence and the preprocessed baseband signal into M=4 equal-length sub-segments, each sub-segment having a length of L, and the starting index of the m-th sub-segment being... The ending index is , m∈[1,M]; S320. Calculate the correlation value for each pair of corresponding local sub-segments and received signal sub-segments, using the following formula:

[0010] in: This represents the i-th value of the m-th sub-segment in the local system. This represents the i-th value of the m-th sub-segment of the received signal; S330. Take the absolute values ​​of the correlation values ​​of the M sub-segments and sum them to obtain the total correlation result. The formula is:

[0011] Where C is the synthetic correlation value.

[0012] Furthermore, the calculation formula for the normalization process is as follows:

[0013] in, These are the normalized correlation values; For the i-th value of the local ZC sequence preamble sequence, Signal energy starting at position k, Synthetic correlation values ​​starting at position k, where N is the length of the ZC sequence.

[0014] Furthermore, the peak detection and localization step specifically includes: S410, Set the detection threshold to 0.7 times the maximum normalized correlation value; S420. Select points that exceed the threshold and are local maxima from the normalized correlation value sequence. The index position corresponding to the point is the starting position of the preamble in the received signal, thereby achieving sub-symbol level precision synchronous positioning.

[0015] In a second aspect, embodiments of the present invention provide a multi-level correlation preamble detection system based on ZC sequence differential coding, applying the method described in the first aspect embodiment, the system comprising: Preamble generation module: used to generate complex ZC sequences, and perform differential encoding and sign decision on the ZC sequences to obtain binary real number preamble sequences; Signal preprocessing module: used to downsample and filter the received oversampled signal, and then calculate the energy of the baseband signal using a sliding window after obtaining the baseband signal; The segmented correlation operation module is used to divide the preamble sequence stored locally and the received signal segment in the sliding window into M sub-segments, where M is a positive integer. It calculates the correlation value of each pair of sub-segments, and sums the absolute values ​​of the correlation values ​​of all sub-segments to obtain the composite correlation value. Normalization module: used to normalize the synthesized correlation values ​​using the energy of the sliding window to obtain normalized correlation values; Peak detection and localization module: used to perform peak detection on the normalized correlation value sequence, determine the peak point that exceeds the threshold and is a local maximum value, and the position corresponding to the peak point is the preamble start position.

[0016] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the multi-level correlation preamble detection method based on ZC sequence differential coding described in the first aspect embodiment above.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-level correlated preamble detection method based on ZC sequence differential coding described in the first aspect embodiment above.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a multi-level correlation preamble detection method, system, related equipment and medium based on ZC sequence differential coding, which has the following beneficial effects: 1. Excellent low signal-to-noise ratio detection performance: By combining the low sidelobe characteristics of the ZC sequence with energy normalization processing, the preamble detection probability is ≥95% in a low signal-to-noise ratio channel environment of -10dB to -18dB, which greatly improves the identification of related peaks and effectively reduces the false alarm rate and false detection rate, which is far superior to the traditional fixed preamble detection method. 2. Strong anti-interference capability: Differential coding design eliminates the dependence on absolute phase, and segmented correlation and incoherent accumulation effectively suppress the influence of phase rotation. The synergistic effect of the two improves the anti-frequency offset capability of this method to over ±50kHz, and the performance loss is ≤1dB when frequency offset exists. No additional frequency offset pre-estimation module is required, which reduces system latency and hardware complexity. At the same time, it greatly improves the robustness under multipath interference, making the timing synchronization error ≤0.2 symbol period, effectively ensuring the performance of subsequent signal demodulation. 3. Low computational complexity: By downsampling, the amount of computational data is reduced to 1 / 8 of the original amount. Differential coding converts complex number operations into real number operations. The piecewise correlation design simplifies the correlation operation process. The overall computational amount is reduced by 40% compared with the traditional full complex number correlation method. At the same time, the power consumption of the device is reduced, which is suitable for the needs of resource-constrained devices such as on-board processing units and IoT terminals, and meets the battery life requirements of low power Bluetooth devices. 4. Strong compatibility and practicality: This method does not require significant modification to the hardware architecture of existing low-power Bluetooth receiver systems and can be directly integrated into existing systems. It is especially suitable for communication scenarios with strong Doppler variations, such as low-orbit satellites, and provides stable synchronization support for GFSK modulation-based communication systems. It can be widely used in scenarios such as direct communication between satellites and ground terminals and long-distance Bluetooth communication, and has broad application prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart of the overall technical architecture provided in the embodiments of the present invention.

[0021] Figure 2 This is a flowchart of the preamble generation module provided in an embodiment of the present invention.

[0022] Figure 3 This is a flowchart of the segmentation-related calculation module provided in an embodiment of the present invention.

[0023] Figure 4 This is a performance comparison chart of different algorithms provided in the embodiments of the present invention.

[0024] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: like Figure 1 As shown, this embodiment of the invention discloses a multi-level correlation preamble detection method based on ZC sequence differential coding, including the following steps: S1. Preamble generation steps: Generate a complex ZC sequence, and perform differential encoding and symbol decision on the ZC sequence to obtain a binary real number preamble sequence.

[0027] This step abandons the traditional fixed pseudo-random preamble format. Through a combination of ZC sequence generation, differential coding, and symbol decision, a customized preamble sequence adapted to complex channels is generated. This completes the format conversion and characteristic optimization from complex-domain ZC sequence to binary real-domain preamble, creating a local reference sequence with low sidelobes and strong frequency offset resistance. This serves as the core local reference for preamble detection, providing basic sequence support for subsequent correlation operations and peak detection. It is specifically adapted to complex wireless communication scenarios with low signal-to-noise ratio and high Doppler frequency offset, such as low-Earth orbit satellite IoT communication and long-range Bluetooth communication. Relying on the ideal constant envelope characteristics and extremely low autocorrelation sidelobes of the ZC sequence itself, the identification of correlation peaks under low signal-to-noise ratio is improved from the root. The differential coding operation effectively eliminates the sequence's dependence on absolute phase, significantly enhancing robustness to carrier frequency offset and crystal oscillator frequency offset. The symbol decision converts complex number operations into real number operations, reducing the device's storage requirements by 50% while retaining the excellent correlation characteristics of the ZC sequence. This perfectly adapts to the computational and storage requirements of resource-constrained devices such as on-board processing units and IoT terminals.

[0028] S2. Signal preprocessing: The received oversampled signal is downsampled and filtered to obtain the baseband signal. Then, the energy of the baseband signal is calculated using a sliding window.

[0029] This step performs multi-stage preprocessing optimization on the high-multiplied oversampled RF raw signal acquired at the receiving end. It sequentially completes core operations such as rate reduction, interference removal, and energy quantization, converting the oversampled signal with high redundancy and noise interference into a clean baseband signal suitable for subsequent calculations. Simultaneously, it acquires a signal energy reference value, providing accurate data support for subsequent normalization processing. As a pre-processing step for preamble detection, it optimizes received signals with high transmission attenuation, high noise interference, and high data redundancy in low-Earth orbit satellite and long-range Bluetooth communications, reducing the computational load and interference impact of subsequent segmentation, correlation, and normalization processes. This optimizes the signal for high-noise, high-redundancy, and low-signal-to-noise ratio satellite communications. To meet the signal processing requirements of satellite channels and long-range Bluetooth channels, downsampling converts the 8x oversampled signal into a 1x baseband signal, reducing the amount of data for subsequent calculations to 1 / 8 of the original, significantly reducing overall computational complexity. Low-pass filtering effectively suppresses high-frequency noise, ionospheric scintillation interference, and scattering clutter in narrowband satellite channels, ensuring the integrity and purity of the baseband signal. Sliding window energy calculation is based on a window of the same length as the local ZC sequence, providing an accurate energy benchmark for the normalization of subsequent results. This effectively eliminates the adverse effects of energy fluctuations and path losses during signal transmission on detection accuracy, reducing the false alarm rate and missed detection rate of subsequent peak detection.

[0030] S3. Segmented Correlation Calculation: Divide the preamble sequence stored locally and the received signal segment in the sliding window into M sub-segments, where M is a positive integer. Calculate the correlation value of each pair of sub-segments, and sum the absolute values ​​of the correlation values ​​of all sub-segments to obtain the composite correlation value.

[0031] This step breaks away from the traditional overall sliding correlation operation, segmenting the local preamble sequence and the received signal segment into equal-length segments. Through segment-by-segment correlation operations and non-coherent accumulation, it achieves accurate correlation matching between the local sequence and the received signal. The core objective is to reduce frequency offset sensitivity and balance computational complexity with correlation detection performance. As the core correlation operation for preamble detection, it replaces the traditional high-complexity, frequency-off-sensitive overall sliding correlation algorithm. It is specifically suitable for complex high-frequency communication scenarios such as Doppler frequency offsets of up to ±50kHz in low-Earth orbit satellite communication and crystal oscillator frequency offsets in long-range Bluetooth communication. It also adapts to the real-time computational needs of resource-constrained devices such as onboard processing units and IoT terminals. The segmented design... The method significantly reduces the phase rotation within a single segment, and the incoherent accumulation operation effectively eliminates the phase difference between sub-segments, making the overall correlation detection result insensitive to carrier frequency offset. It can effectively tolerate carrier frequency offsets of ±50kHz and above with a performance loss of ≤1dB. The overall correlation operation is decomposed into sub-segment correlations, which greatly reduces the amount of computation compared to the traditional full complex number correlation method, meeting the real-time processing requirements of resource-constrained equipment. When the segmentation parameter M is 4, the phase rotation within a single segment is ≤π / 2, achieving an optimal trade-off between frequency offset tolerance and correlation gain. This avoids the problems of excessively short single-segment length and insufficient correlation gain caused by excessively large M, and also avoids the problems of severe phase rotation and low frequency offset tolerance caused by excessively small M, thus balancing detection performance and computational efficiency.

[0032] S4. Normalization: Using the energy of the sliding window, the synthesized correlation value is normalized to obtain a normalized correlation value.

[0033] This step, based on the sliding window energy benchmark obtained in the signal preprocessing stage, standardizes and calibrates the synthesized correlation value obtained from segmented correlation operations. The core objective is to eliminate correlation value distortion caused by path loss, channel fading, and energy fluctuations during signal transmission. This provides a unified reference standard for correlation values ​​at different locations and with different energies, significantly improving the identification of correlation peaks. As a crucial step connecting correlation operations and peak detection in preamble detection, this step optimizes and calibrates the synthesized correlation value, specifically adapting to the high signal energy attenuation and fluctuations in low-Earth orbit satellite and long-range Bluetooth communications. In obvious low signal-to-noise ratio (SNR) scenarios, this method provides a comparable and accurate normalized correlation value sequence for subsequent precise peak detection and location. Through energy normalization processing, it effectively avoids the problem of correlation value distortion caused by path loss, channel fading, and random energy fluctuations in signal transmission. This significantly enhances the distinction between the main peak and side lobes of the correlation under low SNR conditions, greatly reduces the false alarm rate and false negative rate of peak detection, and enables the method to maintain a preamble detection probability of ≥95% even in extremely low SNR channel environments ranging from -10dB to -18dB. The detection performance is far superior to traditional preamble detection methods.

[0034] S5. Peak detection and localization: Perform peak detection on the normalized correlation value sequence to determine the peak point that exceeds the threshold and is a local maximum. The position corresponding to the peak point is the preamble start position.

[0035] This step performs precise peak filtering and location positioning on the normalized correlation value sequence. By setting reasonable detection thresholds and filtering local maxima, it accurately identifies valid peaks from a massive number of correlation values. The core of this step is to achieve precise timing synchronization and start-up position positioning of the preamble in the received signal. This provides a precise time synchronization reference for subsequent signal demodulation and data parsing. As the final core step in preamble detection, it achieves precise preamble synchronization and positioning, specifically suitable for complex channel synchronization scenarios involving multipath interference and frequency offset interference in low-Earth orbit satellite and long-range Bluetooth communications. It provides a precise time synchronization reference for subsequent signal demodulation and data transmission. To ensure the accurate establishment and stable transmission of the entire communication link, the detection threshold is set to 0.7 times the maximum normalized correlation value. This threshold design effectively balances false alarms and missed detections, avoiding false triggers caused by noise interference and preventing the missed detection of effective peak values. Based on threshold screening and local maximum value determination, preamble position positioning with sub-symbol level accuracy can be achieved. In multipath interference scenarios, the timing synchronization error is ≤0.2 symbol periods, which is far superior to the synchronization error of more than 1 symbol period of traditional methods. This effectively avoids the problem of the bit error rate of subsequent signal demodulation increasing by an order of magnitude, greatly improving the reliability of link establishment and ensuring the normal operation of subsequent communication links.

[0036] The above constitutes the complete technical flow of a multi-level correlation preamble detection method based on ZC sequence differential coding. Each step is interconnected and progressively advanced, from core reference sequence generation to received signal preprocessing, then to core correlation calculations, result calibration and optimization, ultimately achieving accurate peak detection and preamble localization. This forms a preamble detection scheme adapted to complex communication scenarios such as low-Earth orbit satellite IoT and long-range Bluetooth. The entire process is designed with low signal-to-noise ratio adaptation, high frequency offset resistance, low computational complexity, and high multipath robustness as its core features. Through innovative designs such as differential coding, segmented correlation, noncoherent accumulation, and energy normalization, it replaces the traditional fixed preamble overall sliding correlation detection method. Each step is designed to address the pain points of detection under complex channels, achieving a comprehensive improvement in preamble detection performance while also considering the computational and storage needs of resource-constrained devices such as on-board processing units and IoT terminals. Ultimately, it achieves accurate, efficient, and stable preamble detection, providing key technical support for the reliable establishment of communication links under complex channels.

[0037] The following example illustrates a multi-level correlation preamble detection method based on ZC sequence differential coding according to the present invention: This invention is designed for the specific scenarios of low-Earth orbit satellite communication and long-range Bluetooth communication. It abandons the traditional fixed preamble and synchronization word scheme and achieves a balance between high performance and low complexity through innovative designs such as differential coding, segmented correlation, and noncoherent accumulation. Its technical process includes five core steps: preamble generation, signal preprocessing, segmented correlation operation, normalization processing, and peak detection and positioning. This process is adapted to satellite-to-ground signal processing, ensuring detection performance while minimizing computational complexity.

[0038] The specific technical solution is as follows: Step 1: Differential coding to generate a preamble adapted for narrowband satellite communication: ZC sequences, as pseudo-random sequences with ideal constant envelope properties and extremely low autocorrelation sidelobes, exhibit high correlation peak distinctiveness and strong noise immunity, making them naturally suitable for communication scenarios with low signal-to-noise ratios and high interference. Therefore, this invention selects ZC sequences as the base sequence for preambles and converts them into binary real number sequences suitable for narrowband satellite communication and Bluetooth terminals through differential coding. The specific process is as follows: Figure 2 As shown.

[0039] Differential coding is the core innovative design of this step, and its value lies in multi-dimensional performance optimization: On the one hand, by extracting the relative phase change information of adjacent sequence samples, differential coding can completely eliminate the dependence on the absolute phase of the signal. The Doppler frequency offset in narrowband satellite communication is essentially a linear phase rotation, and this design can significantly improve the preamble's tolerance to carrier frequency offset. On the other hand, converting the original complex ZC sequence into a binary real sequence reduces the device's storage space requirements for the sequence, while simplifying subsequent correlation operations from multiplication and addition in the complex domain to binary operations in the real domain. This significantly reduces the computational complexity of the narrowband satellite's on-board processing unit and the low-power Bluetooth terminal, perfectly adapting to the hardware capabilities of such resource-constrained devices. More importantly, differential coding does not destroy the excellent correlation characteristics of the ZC sequence itself; its core advantages, such as extremely low autocorrelation sidelobes and sharp correlation peaks, are fully preserved, ensuring that the preamble can still be accurately detected in low signal-to-noise ratio scenarios. This includes the following steps: S110. Generate the fundamental complex number ZC sequence: According to the formula Generate a complex ZC sequence of length N, where N is the generation length, and the adaptation requirement is set to 256; The root index is set to 1 to ensure the lowest possible autocorrelation sidelobe of the sequence, which is suitable for low signal-to-noise ratio scenarios. j is the imaginary unit, n is the sequence index (with values ​​of 0, 1, ..., N-1, representing the nth element), and N is the length of the ZC sequence.

[0040] S120. Calculate the conjugate product of adjacent samples: Differential encoding of the ZC sequence: ; Where n is the sequence index (same as S110). for Conjugate; Phase change information of adjacent samples is extracted to eliminate absolute phase dependence. Due to the special structure of the ZC sequence, the phase difference after differentiation has a deterministic regularity and is not sensitive to carrier frequency offset.

[0041] S130, Symbol decision to generate preamble: Hard decision is made on the real part of the difference result: like Output bit "1"; like Output bit "0"; Finally, a binary real number preamble sequence is obtained. This symbol decision step is a crucial final step in the differential coding process, and its technical benefits are reflected in two core advantages: First, the characteristics of differential coding make the preamble inherently robust to carrier frequency offset. Since differential coding extracts the relative phase change of adjacent samples rather than relying on absolute phase information, even the linear phase rotation caused by Doppler frequency offset in low-Earth orbit satellite communication will not destroy the correlation characteristics of the preamble, ensuring detection stability in high-frequency offset scenarios. Second, by converting the complex ZC sequence into a binary real number sequence, the subsequent correlation operations are directly simplified from operations in the complex field to binary operations in the real field. At the same time, the storage requirements of the device for the preamble sequence are reduced from complex double-value storage to real single-value storage, resulting in a decrease in overall storage requirements. This significantly reduces the hardware burden on resource-constrained devices such as narrowband satellite on-board processing units and low-power Bluetooth terminals, achieving a balance between performance and implementation cost.

[0042] Step 2, Signal Preprocessing Module: To address the prevalent high noise, high redundancy, and strong interference characteristics of satellite channels, and to effectively reduce the complexity of subsequent related calculations, suppress interference, and improve detection accuracy, this invention performs the following preprocessing on the received signal: S210, Downsampling Processing: The receiving end first acquires the original radio frequency signal that has been oversampled by 8 times. This signal contains a large number of redundant sampling points, and directly using it for related calculations would significantly increase computing power consumption. This invention converts the 8-fold oversampled radio frequency signal into a baseband signal with a symbol rate of 1 time through downsampling processing. Without losing core synchronization information, it significantly reduces the amount of data processed in subsequent steps, reducing the overall computational complexity to 1 / 8 of the original. This effectively reduces the computing power load on the onboard processing unit and the Bluetooth Low Energy terminal, providing crucial support for real-time synchronization detection.

[0043] S220, Signal Filtering: After downsampling, this invention further employs a low-pass filter to filter the baseband signal. By precisely filtering out high-frequency components exceeding the signal bandwidth, it effectively suppresses interference components commonly found in narrowband satellite channels, such as high-frequency noise, ionospheric scintillation interference, and multipath scattering clutter. This operation not only significantly improves the signal-to-noise ratio of the baseband signal but also avoids peak distortion and misjudgment caused by various non-ideal channel interferences in subsequent related calculations. This maximizes the integrity and purity of the baseband signal, laying a reliable foundation for subsequent accurate preamble detection.

[0044] S230, Sliding window energy calculation: Using a sliding window with the same length as the local ZC sequence, the energy of the preprocessed baseband signal is calculated. The energy calculation formula is as follows: , where k is the starting index of the sliding window, i is the sampling point number of the baseband signal, N is the length of the ZC sequence, and x(i) is the value of the i-th sampling point of the baseband signal.

[0045] The baseband signal energy value calculated through a sliding window will serve as the core energy benchmark for subsequent normalization of related calculation results. This benchmark can effectively offset the random energy fluctuations caused by path loss, channel fading, and obstruction interference during signal transmission, avoiding misjudgment or submersion of related peaks due to energy differences. This significantly improves the stability and accuracy of preamble detection, ensuring accurate identification of valid preambles even in complex channel environments.

[0046] Step 3: Segmented accumulation of correlated and incoherent data: To further enhance the robustness of the system in high Doppler frequency offset environments and overcome the shortcomings of traditional overall correlation operations being overly sensitive to frequency offset and prone to severe peak attenuation, this invention adopts an innovative design of segmented correlation and incoherent accumulation. This significantly reduces the sensitivity of preamble detection to carrier frequency offset from an algorithmic structure perspective, thereby significantly improving the stability of correlation peaks and detection reliability in high frequency offset scenarios. The specific implementation process is as follows: Figure 3 As shown, the main steps include: S310, Sequence Segmentation: Divide the sliding window segment of the local ZC sequence and the received signal into M equal-length sub-segments, M=4, with each segment having a length of 64. Determine the starting index of each sub-segment as follows: The ending index is .

[0047] The selection of the segmentation parameter M is a key trade-off in the segmented correlation design of this invention, requiring a fine balance between frequency offset tolerance and correlation gain. When M is too large, the length of a single sequence segment will be excessively shortened, causing the effective energy within each segment to be dispersed, making it difficult to effectively improve the correlation gain, thereby weakening the signal-to-noise ratio gain of preamble detection. When the value of M is too small, the phase rotation within a single segment will become severe, and the frequency offset tolerance will decrease accordingly. In high-frequency offset scenarios, the relevant peak values ​​are prone to significant attenuation, which cannot meet the application requirements of ±50kHz Doppler frequency offset in low-Earth orbit satellite communication. This invention, through theoretical analysis and simulation verification, determines that when M=4, the phase rotation within a single segment is precisely controlled within... It can achieve the optimal trade-off between frequency offset tolerance and correlation gain, ensuring sufficient frequency offset robustness while effectively improving the identification of correlation peaks.

[0048] S320, Sub-segment Related Operations: For each pair of corresponding local segments and received signal segments, calculate the correlation value:

[0049] in, This represents the i-th value of the m-th sub-segment in the local system. This represents the i-th value of the m-th sub-segment of the received signal.

[0050] S330, Incoherent Accumulation: The total correlation result is obtained by summing the absolute values ​​of the correlation values ​​of the M sub-segments.

[0051] Incoherent accumulation cleverly eliminates the phase differences caused by frequency offset between segments by summing the magnitudes of the correlation results of each segment, rather than directly linearly superimposing the complex correlation values. This process makes the final total correlation peak no longer dependent on the consistency of the phase of each segment, fundamentally weakening the impact of frequency offset on the correlation results. This makes the total correlation peak highly insensitive to carrier frequency offset, significantly improving the stability and reliability of preamble detection in scenarios with large frequency offsets.

[0052] Step 4, Energy Normalization: To completely eliminate the impact of energy fluctuations caused by path loss, channel fading, and obstruction interference during signal transmission on the correlation results, and to ensure the stability and accuracy of the preamble detection threshold, this invention normalizes the total correlation result after segmented incoherent accumulation. The normalization formula is as follows:

[0053] in, These are the normalized correlation values. For the i-th value of the local ZC sequence preamble sequence, The signal energy in S230 starting at position k. is the synthetic correlation value starting at position k in S330, and N is the length of the ZC sequence.

[0054] Step 5, Peak Detection and Location: For the normalized correlation result sequence Peak detection includes the following steps: S410, Set the detection threshold: To accurately distinguish between valid preambles and noise interference in complex channel environments, this invention dynamically sets the detection threshold to 0.7 times the maximum normalized correlation value. This design, through an adaptive threshold strategy, effectively avoids false triggering caused by spurious peaks formed by noise or interference signals, while ensuring reliable acquisition of valid preambles even under harsh conditions such as low signal-to-noise ratio and large frequency offset, significantly improving the robustness and accuracy of detection.

[0055] S420, Peak Filtering: After normalization and threshold determination, this invention further filters out relevant peak points from the received signal whose amplitude exceeds the detection threshold and is a maximum value within a local interval. The index position corresponding to this peak point is the precise starting position of the preamble in the received signal sequence. By performing fine interpolation and fitting on the relevant peak positions, this method can achieve sub-symbol level timing accuracy, effectively suppress timing offsets caused by multipath interference and noise, and ultimately achieve high-precision and high-stability preamble synchronization positioning.

[0056] The following simulation experiment further illustrates the multi-level correlation preamble detection method based on ZC sequence differential coding of this invention: The simulation experiment was based on a GFSK modulated signal with a carrier frequency of 2MHz and an 8x oversampling. A multipath fading channel and Doppler frequency offset scenario were constructed, with a frequency offset range of ±60kHz and a Doppler rate of change of -720Hz / s. Synchronization was achieved using a ZC sequence differential coding multi-level correlation detection algorithm. Figure 4 As shown, the difference in communication performance between the method of this invention and the conventional method is illustrated in a frequency offset scenario of ±50kHz: The horizontal axis represents the signal-to-noise ratio (SNR), ranging from -20dB to -12dB, the low SNR range; the vertical axis represents the bit error rate (BER), with a smaller value indicating better performance.

[0057] The blue curve represents the method of this invention, whose bit error rate remains consistently low, stable in the SNR range of -20dB to -16dB, and rapidly decreases after SNR > -16dB, eventually approaching 10 at SNR = -12dB. -4 ; The orange curve represents the traditional method, which has a significantly higher bit error rate, with a sharp jump around SNR=-18dB, and its overall performance is far inferior to the method of this invention.

[0058] In summary, in low signal-to-noise ratio scenarios with a frequency offset of ±50kHz, the method of this invention has significantly better anti-interference capability and bit error rate performance than traditional solutions, and is more suitable for complex channels such as low-Earth orbit satellites and long-range Bluetooth.

[0059] This invention effectively solves the performance bottleneck of traditional preamble detection methods in complex channel scenarios through multi-dimensional innovative design. This invention selects the ZC sequence as the basic sequence of the preamble and converts the complex form of the ZC sequence into a binary real number sequence through differential coding technology. This not only completely eliminates the dependence on the absolute phase of the signal, but also significantly improves the anti-interference robustness of preamble detection against significant Doppler frequency offset and crystal oscillator differences in Bluetooth devices in low-Earth orbit satellite communication scenarios. At the same time, it greatly reduces the storage overhead and computing load of narrowband satellite devices and IoT Bluetooth terminals, adapting to the hardware implementation requirements of resource-constrained devices.

[0060] To address the technical challenge of correlation peak attenuation in high-frequency bias scenarios, this invention employs a design combining segmented correlation and incoherent accumulation. The entire preamble sequence is divided into several segments for independent correlation calculations, and the correlation results from each segment are then incoherently merged. This effectively eliminates the adverse effects of phase rotation caused by frequency bias on correlation detection, stably tolerating Doppler frequency bias of ±50kHz and equipment crystal oscillator frequency bias without any loss in detection performance within this frequency bias range. Simultaneously, this design reduces the overall computational load, meeting the performance requirements of onboard processing units and IoT terminals for real-time signal processing.

[0061] To address the issues of low correlation peak identification and susceptibility to noise interference in low signal-to-noise ratio (SNR) scenarios, this invention combines sliding window energy calculation with a local preamble sequence energy benchmark to normalize the correlation results after segmented incoherent accumulation. This significantly enhances the identification of effective correlation peaks in low SNR environments, effectively reduces the false alarm rate and false negative rate during synchronization detection, and greatly improves the reliability and stability of preamble synchronization in weak signal scenarios.

[0062] This invention incorporates low-complexity optimization design into the entire signal processing process. By reducing redundant data through downsampling and replacing complex field operations with real field operations, it significantly reduces the computational complexity and power consumption of the entire preamble detection process. It is perfectly suited for application scenarios where low-orbit satellite platform payload resources are limited and IoT terminal battery life is limited, thus balancing high performance and engineering feasibility.

[0063] Example 2: Based on the same inventive concept, embodiments of the present invention also provide a multi-level correlation preamble system based on ZC sequence differential coding, the system comprising: Preamble generation module: used to generate complex ZC sequences, and perform differential encoding and sign decision on the ZC sequences to obtain binary real number preamble sequences; Signal preprocessing module: used to downsample and filter the received oversampled signal, and then calculate the energy of the baseband signal using a sliding window after obtaining the baseband signal; The segmented correlation operation module is used to divide the preamble sequence stored locally and the received signal segment in the sliding window into M sub-segments, where M is a positive integer. It calculates the correlation value of each pair of sub-segments, and sums the absolute values ​​of the correlation values ​​of all sub-segments to obtain the composite correlation value. Normalization module: used to normalize the synthesized correlation values ​​using the energy of the sliding window to obtain normalized correlation values; Peak detection and localization module: used to perform peak detection on the normalized correlation value sequence, determine the peak point that exceeds the threshold and is a local maximum value, and the position corresponding to the peak point is the preamble start position.

[0064] Since the principle of this system and the problem it solves is similar to the aforementioned multi-level correlation preamble detection method based on ZC sequence differential coding, the implementation of this system can refer to the implementation of the aforementioned method, and the repeated parts will not be repeated.

[0065] Example 3: Based on the same inventive concept, the present invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it is able to implement the multi-level correlation preamble detection method based on ZC sequence differential coding as described in any one of Embodiments 1.

[0066] like Figure 5 As shown, the electronic device may include: a processor 10, a communication interface 20, a memory 30, and a communication bus 40, wherein the processor 10, the communication interface 20, and the memory 30 communicate with each other via the communication bus 40. The processor 10 can call logical instructions in the memory 30 to execute a multi-level correlated preamble detection method based on ZC sequence differential coding, the method including: Preamble generation: Generate a complex ZC sequence, and perform differential encoding and sign decision on the ZC sequence to obtain a binary real number preamble sequence; Signal preprocessing: The received oversampled signal is downsampled and filtered to obtain the baseband signal, and then the energy of the baseband signal is calculated using a sliding window. Segmented correlation operation: The preamble sequence stored locally and the received signal segment in the sliding window are divided into M sub-segments, where M is a positive integer. The correlation value of each pair of sub-segments is calculated, and the absolute values ​​of the correlation values ​​of all sub-segments are summed to obtain the composite correlation value. Normalization: The energy of the sliding window is used to normalize the synthesized correlation values ​​to obtain normalized correlation values; Peak detection and localization: Peak detection is performed on the normalized correlation value sequence to determine the peak point that exceeds the threshold and is a local maximum. The position corresponding to the peak point is the preamble start position.

[0067] Example 4: This invention also provides a computer-readable storage medium containing a program for executing a multi-level correlation preamble detection method based on ZC sequence differential coding according to Embodiment 1 above. The program can be executed on a processor.

[0068] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The program stored on this medium is loaded into the processor's memory and executed to perform various functions. This storage medium, connected to hardware devices, enables the computer to perform the steps of Embodiment 1 described above.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0071] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-level correlation preamble detection method based on ZC sequence differential coding, characterized in that, Includes the following steps: Preamble generation: Generate a complex ZC sequence, and perform differential encoding and sign decision on the ZC sequence to obtain a binary real number preamble sequence; Signal preprocessing: The received oversampled signal is downsampled and filtered to obtain the baseband signal, and then the energy of the baseband signal is calculated using a sliding window. Segmented correlation operation: The preamble sequence stored locally and the received signal segment in the sliding window are divided into M sub-segments, where M is a positive integer. The correlation value of each pair of sub-segments is calculated, and the absolute values ​​of the correlation values ​​of all sub-segments are summed to obtain the composite correlation value. Normalization: The energy of the sliding window is used to normalize the synthesized correlation values ​​to obtain normalized correlation values; Peak detection and localization: Peak detection is performed on the normalized correlation value sequence to determine the peak point that exceeds the threshold and is a local maximum. The position corresponding to the peak point is the preamble start position.

2. The method as described in claim 1, characterized in that, The preamble generation step specifically includes: S110, According to the formula Generate a complex ZC sequence z of length N, where, is the root index, j is the imaginary unit, n is the sequence index, and N is the length of the ZC sequence; S120. Perform a difference operation on the complex ZC sequence: The difference sequence is obtained. Where n is the sequence index, for Conjugate; S130, for the difference sequence Perform a sign determination on the real part of each element. If Output bit 1 if Output bit 0; finally, a binary real number preamble sequence is generated.

3. The method as described in claim 1, characterized in that, The signal preprocessing steps specifically include: S210, downsampling the 8x oversampled signal to convert it into a 1x baseband signal; S220. A low-pass filter is used to filter the baseband signal to suppress high-frequency noise, ionospheric scintillation interference and scattering clutter; S230. Use a sliding window with a length consistent with the local ZC sequence, according to the formula... Calculate the energy of the baseband signal, where k is the starting index of the sliding window, i is the sampling point number of the baseband signal, N is the length of the ZC sequence, and x(i) is the value of the i-th sampling point of the baseband signal.

4. The method as described in claim 1, characterized in that, The segmentation-related calculation steps specifically include: S310. Divide the sliding window segment of the local preamble sequence and the preprocessed baseband signal into M equal-length sub-segments, each sub-segment having a length of L, and the starting index of the m-th sub-segment being... The ending index is , m∈[1,M]; S320. Calculate the correlation value for each pair of corresponding local sub-segments and received signal sub-segments, using the following formula: in: This represents the i-th value of the m-th sub-segment in the local system. This represents the i-th value of the m-th sub-segment of the received signal; S330. Take the absolute values ​​of the correlation values ​​of the M sub-segments and sum them to obtain the total correlation result. The formula is: Where C is the synthetic correlation value.

5. The method as described in claim 1, characterized in that, The calculation formula for the normalization process is as follows: in, These are the normalized correlation values; For the i-th value of the local ZC sequence preamble sequence, Signal energy starting at position k, Synthetic correlation values ​​starting at position k, where N is the length of the ZC sequence.

6. The method as described in claim 1, characterized in that, The peak detection and localization step specifically includes: S410, Set the detection threshold to 0.7 times the maximum normalized correlation value; S420. Select points that exceed the threshold and are local maxima from the normalized correlation value sequence. The index position corresponding to the point is the starting position of the preamble in the received signal, thereby achieving sub-symbol level precision synchronous positioning.

7. A multi-level correlation preamble detection system based on ZC sequence differential coding, characterized in that, The system comprises: Preamble generation module: used to generate complex ZC sequences, and perform differential encoding and sign decision on the ZC sequences to obtain binary real number preamble sequences; Signal preprocessing module: used to downsample and filter the received oversampled signal, and then calculate the energy of the baseband signal using a sliding window after obtaining the baseband signal; The segmented correlation operation module is used to divide the preamble sequence stored locally and the received signal segment in the sliding window into M sub-segments, where M is a positive integer. It calculates the correlation value of each pair of sub-segments, and sums the absolute values ​​of the correlation values ​​of all sub-segments to obtain the composite correlation value. Normalization module: used to normalize the synthesized correlation values ​​using the energy of the sliding window to obtain normalized correlation values; Peak detection and localization module: used to perform peak detection on the normalized correlation value sequence, determine the peak point that exceeds the threshold and is a local maximum value, and the position corresponding to the peak point is the preamble start position.

8. A computer 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 multi-level correlation preamble detection method based on ZC sequence differential coding as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multi-level correlation preamble detection method based on ZC sequence differential coding as described in any one of claims 1 to 6.

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