High-efficiency capturing method for low-signal-to-noise-ratio direct sequence spread spectrum channel

Abstract

The invention discloses a high-efficiency capturing method for a low-signal-to-noise-ratio direct sequence spread spectrum channel. The method comprises the following steps: carrying out matched filtering and signal extraction on a signal subjected to analog-to-digital conversion; performing selective phase shift and vector accumulation on at least two groups of signal frame fragments in the extracted signal, and fusing into an accumulated signal; performing frequency domain transformation and analysis on the accumulated signal to obtain frequency spectrum data; and retrieving the spectrum data, identifying candidate peak values, and outputting a signal arrival position and frequency offset. Preferably, peak value time sequence phase fingerprint verification is applied to the candidate peak value, and a real signal peak value is identified by establishing a phase evolution model and performing bidirectional consistency verification; moreover, during vector accumulation, a high-precision phase shift method based on a non-uniform lookup table and an error diffusion mechanism is adopted to compensate quantization errors. According to the invention, the capture performance and reliability under a low signal-to-noise ratio are effectively improved.

Description

Technical Field

[0001] The present invention relates to a method for capturing a direct sequence spread spectrum channel, in particular to a high-efficiency method for capturing a low signal-to-noise ratio direct sequence spread spectrum channel. Background Art

[0002] Direct sequence spread spectrum (DSSS) communication technology, due to its inherent advantages such as interference resistance, fading robustness, low probability of intercept, and support for code division multiple access, plays a pivotal role in modern wireless communication systems. In satellite communications, in particular, and particularly in applications such as the Internet of Things (IoT) and low-data-rate transmission, DSSS communication offers significant technical value by enabling the use of small, low-cost user terminal antennas while effectively minimizing interference with neighboring satellites. However, these applications often present challenges such as weak signals and poor channel conditions. To ensure rapid and reliable communication links under extremely low signal-to-noise ratio (SNR) conditions, the receiver must prioritize accurate acquisition of the spread spectrum signal. Therefore, research on efficient acquisition methods in low-SNR direct sequence spread spectrum channels is not only a key technical bottleneck for improving DSSS system performance but also a prerequisite for its widespread application in satellite IoT, emergency communications, deep space exploration, and other fields, possessing significant engineering application value.

[0003] Currently, various approaches have been developed for capturing direct sequence spread spectrum signals. In hardware implementation, a fundamental and widely used method is based on a time-domain sliding correlator. This method performs a point-by-point sliding correlation operation between the received signal and a locally generated pseudorandom (PN) preamble sequence. When the correlation value exceeds a preset threshold, the signal is considered captured. To improve computational efficiency, frequency-domain capture methods have been introduced. Typically, this method performs a fast Fourier transform (FFT) on the received signal, converting the time-domain correlation operation into a frequency-domain product operation, thereby reducing computational complexity to a certain extent. When faced with extremely low signal-to-noise ratio (SNR), existing techniques generally extend the preamble sequence length to accumulate more signal energy in exchange for improved capture performance. For example, Chinese patent publication number CN109792771B proposes a solution where doubling the preamble sequence length can achieve a SNR gain of approximately 3dB. Furthermore, to address frequency deviations between the receiver and transmitter, some approaches employ strategies based on multiple parallel processing channels. Each channel searches for a preset frequency offset hypothesis, casting a wide net to cover the range of possible frequency uncertainty. These methods currently constitute the mainstream technical framework for DSSS signal acquisition.

[0004] While existing technologies offer a range of signal capture solutions, their inherent limitations are becoming increasingly apparent as applications pursue higher efficiency, greater robustness, and reduced implementation complexity. For example, existing solutions suffer from poor capture performance at low signal-to-noise ratios and insufficient reliability in extremely harsh channels. Summary of the Invention

[0005] The present application provides a high-efficiency capture method for a low signal-to-noise ratio direct sequence spread spectrum channel to solve two core problems of low capture performance under low signal-to-noise ratio conditions and insufficient reliability under extremely harsh channels.

[0006] According to one aspect of the present application, matched filtering and signal extraction are performed on the signal after analog-to-digital conversion to obtain an extracted signal; selective phase shifting and vector accumulation are performed on at least two groups of signal frame segments in the extracted signal to fuse them into an accumulated signal; the accumulated signal is transformed and analyzed in the frequency domain to obtain spectrum data; the spectrum data is retrieved and candidate peaks that meet a preset threshold are identified, and the candidate peak information is extracted; based on the candidate peak information, the signal arrival position and frequency deviation are calculated and output.

[0007] In an exemplary embodiment, the signal arrival position and frequency deviation are calculated and output based on the candidate peak information, including: applying a peak timing phase fingerprint verification to the candidate peak information in combination with the accumulated signal to obtain the true signal peak information; and calculating and outputting the signal arrival position and frequency deviation based on the true signal peak information.

[0008] In an exemplary embodiment, a peak timing phase fingerprint verification is applied in combination with the accumulated signal to obtain the true signal peak information, including: decomposing the accumulated signal into multiple sliding windows with time overlap, and performing frequency domain transformation on the data in each sliding window to construct a window spectrum sequence; in the window spectrum sequence, for each candidate peak in the candidate peak information, extracting its phase value at the corresponding frequency position in each window spectrum to form a phase evolution sequence that characterizes the change of the candidate peak phase over time, and extracting the true signal peak information based on this.

[0009] In an exemplary embodiment, the true signal peak information is extracted based on the phase evolution sequence, including: mathematically modeling the phase evolution sequence corresponding to each candidate peak, fitting a quadratic polynomial model through the least squares method, and obtaining a set of model parameters that characterize the dynamic characteristics of the sequence; based on the model parameters, quantizing the phase curvature or phase continuity score of the phase evolution sequence, and judging whether the corresponding candidate peak is a true signal peak based on the quantization result, and extracting the peak information.

[0010] In an exemplary embodiment, selective phase shifting and vector accumulation are performed, including: according to a pre-stored target phase shift angle, searching or interpolating and calculating a complex phase shift coefficient of a predetermined accuracy in a pre-constructed non-uniform resolution lookup table with different angular resolutions in different phase intervals; performing a complex multiplication operation on a signal frame segment using the complex phase shift coefficient to obtain a precisely phase-shifted signal frame segment, and performing subsequent vector accumulation.

[0011] In an exemplary embodiment, a phase shift error diffusion mechanism is further included, which calculates a complex phase shift coefficient based on a target phase shift angle and uses the coefficient to perform operations, including: reading a historical phase shift error stored in a previous phase shift operation, and compensating the current target phase shift angle based on the historical phase shift error to obtain a compensated phase shift angle; based on the compensated phase shift angle, determining a complex phase shift coefficient of a predetermined accuracy in a non-uniform resolution lookup table; after completing the phase shift operation on a signal frame segment using the complex phase shift coefficient, calculating the difference between the actual phase shift angle represented by the complex phase shift coefficient and the target phase shift angle, generating and storing the current phase shift error as the historical phase shift error for the next phase shift operation.

[0012] In an exemplary embodiment, the non-uniform resolution lookup table is dynamically updated, and its updating method includes: periodically counting the frequency distribution of the target phase shift angles used in past phase shift operations to identify the angle intervals used at high frequencies; adaptively improving the angle resolution of the angle intervals used at high frequencies, and updating the non-uniform resolution lookup table to provide more appropriate phase shift accuracy for commonly used angles in subsequent phase shift coefficient calculations.

[0013] In an exemplary embodiment, selective phase shifting and vector accumulation are performed, and a structured search strategy is also included for two groups of signal frame segments, specifically: direct vector accumulation is performed on the two groups of signal frame segments to obtain a first accumulated signal, and subsequent frequency domain transformation and analysis and peak recognition are performed on the first accumulated signal; on the premise that a candidate peak meeting a preset threshold is not identified based on the first accumulated signal, a π phase shift is applied to one group of signal frame segments, and vector accumulation is performed with the other group of signal frame segments to obtain a second accumulated signal, and frequency domain transformation and analysis and peak recognition are performed on the second accumulated signal.

[0014] In an exemplary embodiment, the strategy further includes: if two attempts at the structured search strategy for two groups of signal frame segments fail to identify a candidate peak that meets a preset threshold, the number of accumulated signal frame segments is increased to four groups; and for the four groups of signal frame segments, different preset phase combinations are used in turn for phase shifting and vector accumulation, and the accumulated signal generated by each combination is used to perform frequency domain transformation and analysis and peak identification until the candidate peak is successfully identified.

[0015] In an exemplary embodiment, the accumulated signal is transformed and analyzed in the frequency domain to obtain spectrum data, including: performing frequency domain transformation and analysis on the accumulated signal, specifically, performing point-by-point sliding window sampling on the accumulated signal, and performing correlation operation with a local sequence to obtain a correlated signal; dividing the correlated signal into a predetermined number of groups according to a pre-stored code chip rate representing the system symbol rate, and summing the data in each group to generate a grouped summed sequence; and performing a fast Fourier transform on the grouped summed sequence to obtain spectrum data.

[0016] The above technical solution can effectively improve the capture performance under low signal-to-noise ratio and improve the reliability under extremely harsh channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a method for efficiently capturing a low signal-to-noise ratio direct sequence spread spectrum channel according to an embodiment of the present application.

[0020] Figure 2 This is a flow chart for calculating and outputting signal arrival position and frequency deviation based on candidate peak information according to an embodiment of the present application.

[0021] Figure 3 This is a flow chart of applying peak timing phase fingerprint verification to obtain real signal peak information according to an embodiment of the present application.

[0022] Figure 4 This is a flowchart of extracting true signal peak information based on a phase evolution sequence according to an embodiment of the present application.

[0023] Figure 5 is a flowchart of performing selective phase shifting and vector accumulation according to an embodiment of the present application.

[0024] Figure 6 This is a spectrum diagram obtained by extracting the signal after matched filtering without accumulation and performing PMF_FFT operation according to an embodiment of the present application.

[0025] Figure 7 This is a spectrum diagram obtained by performing PMF_FFT operation after doubling the accumulation according to the embodiment of the present application.

[0026] Figure 8 This is a spectrum diagram obtained by performing PMF_FFT operation after accumulating the phase shift by π times according to the embodiment of the present application.

[0027] Figure 9 This is a spectrum diagram obtained by performing PMF_FFT operation after accumulating 4 times the phase shift π according to the embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present invention described herein may be practiced in sequences other than those illustrated or described herein.

[0030] It should be noted that to clearly illustrate the steps of this application, serial numbers are assigned to each step in the specification. These serial numbers are for illustrative purposes only and do not limit the order in which the steps must be executed. In actual operation, depending on the technical requirements of the specific implementation scenario, the steps may be executed in a different order than shown in the specification, and in some cases, parallel processing between steps may be implemented.

[0031] like Figures 1 to 9 As shown, embodiment 1 proposes a high-efficiency acquisition method for a low signal-to-noise ratio direct sequence spread spectrum channel, comprising the following steps:

[0032] Step S101: performing matched filtering on the analog-to-digital converted signal to generate a matched filtered signal.

[0033] In the embodiment of the present application, the analog-to-digital converted signal refers to the analog intermediate frequency signal from the antenna, which is a digital complex baseband signal obtained after down-conversion, filtering and analog-to-digital converter (ADC) sampling.

[0034] The impulse response of the matched filter is the time deconvolution and conjugation of the known signal waveform. In the present invention, it refers to the process of correlating the received digital signal with a locally stored, known pseudo-random (PN) preamble sequence.

[0035] Specifically, the ADC samples at a frequency four times the chip rate. A matched filter module stores a preamble sequence (e.g., 3072 chips long) that matches the transmitter's. The digital complex baseband signal stream output by the ADC is continuously fed into the matched filter for complex correlation. The resulting signal is the matched filtered signal Sm.

[0036] Specifically, the goal of matched filtering is to maximize the signal-to-noise ratio (SNR) of a specific signal in a noisy environment. By correlating it with a known preamble sequence, the signal energy dispersed over a long symbol period can be concentrated into a single peak, making it easier to detect the presence of the signal in subsequent detection steps.

[0037] Step S102: performing signal extraction on the matched filtered signal to obtain a extracted signal. In the embodiment of the present application, quadruple decimation is performed on the matched filtered signal to reduce the amount of computation required in subsequent processing steps.

[0038] Decimation is a downsampling operation in digital signal processing (DSP). It is used to extract a point from a discrete signal sequence at regular intervals. Decimation by four means retaining one point out of every four consecutive sampling points.

[0039] Specifically, a signal extraction module processes the matched filtered signal Sm output from step S101. Since the ADC uses quadruple sampling, the sampling rate of Sm is also four times the chip rate. The module extracts a point every four sampling points to generate the extracted signal Sd. For example, from Sm(0), Sm(1), Sm(2), Sm(3), Sm(4), ..., the extractor may extract Sm(0), Sm(4), Sm(8), ..., and generate Sd(0), Sd(1), Sd(2), ...

[0040] Specifically, this step reduces data processing and computational complexity. By decimating by four, the data required for all subsequent processing (such as accumulation and FFT) is reduced by 75%, which is crucial for implementing high-speed signal processing on resource-constrained hardware (such as FPGAs). However, this step introduces two manageable issues: a) Because the decimation position can be random, the optimal sampling point may be missed, resulting in a signal-to-noise ratio loss of approximately 1-2 dB; and b) the detected signal position may have an error of approximately ±2 sampling points. These issues are compensated and corrected through subsequent cumulative gain and precise re-search steps.

[0041] It can be understood that the above means performing matched filtering and signal extraction on the signal after analog-to-digital conversion to obtain the extracted signal.

[0042] Step S103: performing selective phase shifting and vector accumulation on at least two groups of signal frame segments in the extracted signal to fuse them into an accumulated signal.

[0043] A signal frame segment is a signal segment extracted from the decimated signal Sd, with a length equal to one physical frame length, L. For example, Sd(0) to Sd(L-1) is the first frame segment, and Sd(L) to Sd(2L-1) is the second frame segment. Vector accumulation refers to the complex addition of multiple complex signal frame segments at corresponding positions.

[0044] Specifically, in this embodiment, a basic two-frame direct accumulation is performed. An accumulator module stores the first signal frame segment Sd(0)...Sd(L-1) in local RAM. Furthermore, when the second signal frame segment Sd(L)...Sd(2L-1) arrives, it is complex-added point-by-point with the first frame segment stored in RAM to generate an accumulated signal Sa. Specifically, Sa(i) = Sd(i) + Sd(L+i), where i ranges from 0 to L-1.

[0045] Specifically, because the preamble sequence of each frame in a spread spectrum communication system operating in continuous or quasi-continuous channel mode is fixed and identical, by coherently summing corresponding portions of multiple frames, the signal components are superimposed in phase, while the noise components are superimposed incoherently. This effectively improves the signal-to-noise ratio (SNR). In theory, ideal summation of two frames can achieve a 3dB SNR gain, which is sufficient to compensate for the loss caused by decimation in step S102 and achieve additional processing gain.

[0046] Step S104: Perform frequency domain transformation and analysis on the accumulated signal to obtain spectrum data. This includes performing frequency domain transformation and analysis on the accumulated signal, specifically, performing a point-by-point sliding window sampling operation on the accumulated signal and performing a correlation operation with the local sequence to obtain a correlated signal; dividing the correlated signal into a predetermined number of groups based on a pre-stored chip rate representing the system symbol rate, and summing the data in each group to generate a grouped summed sequence; and performing a fast Fourier transform on the grouped summed sequence to obtain spectrum data.

[0047] PMF-FFT (Partial Matched Filter-Fast Fourier Transform) is used for frequency domain capture, and this step is its specific implementation. Optionally, this step is completed by a PMF-FFT module, and the specific steps are as follows:

[0048] Step 1: Point-by-point sliding window correlation. The accumulated signals Sa(0)...Sa(L-1) generated in step S103 are subjected to point-by-point sliding window calculations. Each time, a window with a length equal to the length of the preamble sequence (e.g., 3072 points) is extracted and correlated with the locally stored preamble sequence to obtain correlated signals Sc(0)...Sc(3071) with a length of 3072 points.

[0049] Step 2: Group summation: Perform group summation on Sc according to the system chip rate Rc.

[0050] Optionally, when the chip rate is 8Mcps > Rc ≥ 4Mcps, Sc is divided into 128 groups, each with 24 points. The grouped summation sequence is Sca(0) = Sc(0) + ... + Sc(23), Sca(1) = Sc(24) + ... + Sc(47), ..., up to Sca(127).

[0051] When Rc≥8Mcps, it is divided into 64 groups, with 48 points in each group.

[0052] When Rc<4Mcps, it is divided into 256 groups, with 12 points in each group.

[0053] Step 3: Fast Fourier Transform. Perform a 128-point FFT operation on the grouped summed sequence Sca (e.g., 128 points in length) to obtain the final spectrum data F(0)...F(127).

[0054] Specifically, the PMF-FFT structure decomposes a large time-domain correlation operation into multiple small correlations (implemented through group summation) and an FFT operation. The advantage is that the phase rotation caused by carrier frequency offset manifests as a peak shift in the FFT frequency bins. By searching for peaks in the frequency domain, signal position detection and a rough estimate of the frequency offset can be achieved simultaneously, with far less computational effort than compensating for and searching for every possible frequency offset in the time domain. Different grouping methods correspond to different frequency offset estimation ranges. For example, 128 groups can support a frequency offset range of at least ±166.47 kHz (when Rc ≥ 1 Mcps).

[0055] Step S105: searching the spectrum data and identifying candidate peaks that meet a preset threshold, extracting the candidate peak information; and calculating and outputting the signal arrival position and frequency offset based on the candidate peak information.

[0056] Candidate peak information typically includes the peak amplitude, the FFT frequency bin index where the peak occurs, and the start position of the time window corresponding to the peak. The signal arrival position refers to the starting sampling point of the preamble sequence in the received signal stream. Frequency deviation refers to the difference between the carrier frequency of the received signal and the receiver's local oscillator frequency.

[0057] Specifically, the threshold detection module processes the spectrum data F outputted in step S104. Peak retrieval refers to searching for the point with the largest amplitude in the spectrum data F(0)...F(127). Threshold determination refers to comparing the amplitude of the peak with a preset noise threshold. The threshold can be dynamically adjusted according to the channel environment, or set to a fixed value based on experience. If the peak amplitude exceeds the threshold, it is determined to be successfully captured, and the peak becomes a candidate peak. Furthermore, information is calculated and outputted, specifically including the signal arrival position and frequency deviation.

[0058] The signal arrival position is the starting position of the time window corresponding to the PMF-FFT operation that generated the peak, which is the approximate signal arrival position. Frequency offset calculation involves directly correlating the FFT frequency index of the candidate peak with the frequency offset. For example, if the peak is at the kth frequency, the frequency offset Δf = k * (sampling rate / number of FFT points). The system ultimately outputs these two pieces of information. If the peak does not meet the threshold, the process transitions to other search strategies (as described in Example 4).

[0059] Regarding frequency offset calculation: The FFT frequency index of the candidate peak directly corresponds to the frequency offset. To obtain a more accurate frequency offset estimate, the Rife algorithm or other interpolation estimation algorithm can be used based on the detected spectral peak and its adjacent frequency bins, resulting in a frequency offset estimate with higher frequency resolution than the FFT.

[0060] By comparing it with a threshold, we can preliminarily determine whether the detected peak is a real signal or random noise. Once confirmed, the key synchronization parameters required for communication demodulation: time and frequency can be parsed from the peak information.

[0061] Example 2 provides another implementation method for verifying true signal peaks based on peak timing phase fingerprints. This method enhances the reliability of peak identification, particularly in extremely low signal-to-noise ratio scenarios. This method, in step S105 of the previous example, inserts execution after a candidate peak meeting a preset threshold is retrieved and before the final output.

[0062] Step S201: Apply a peak timing phase fingerprint verification to the candidate peak information in combination with the accumulated signal to obtain the real signal peak information. Based on the real signal peak information, calculate and output the signal arrival position and frequency offset.

[0063] Specifically, at low signal-to-noise ratios, the amplitude of some noise spikes may occasionally exceed the threshold, leading to false alarms (incorrect detections). True signal peaks not only have high amplitudes, but their phase variations over time (caused by carrier frequency offset and timing drift) should also be continuous and smooth. However, the phase of noise peaks is random and irregular over time. This step exploits this essential difference as a more robust discrimination mechanism than a single amplitude threshold.

[0064] Step S202: decompose the accumulated signal into multiple sliding windows with time overlap, and perform frequency domain transformation on the data in each sliding window to construct a window spectrum sequence.

[0065] The specific steps are as follows:

[0066] Step 1: Obtain the accumulated signal Sa from step S103.

[0067] Step 2: Set the sliding window parameters. For example, set the window length to 3072 points and the overlap rate to 50%.

[0068] Step 3: Generate a sliding window data sequence. If the total length of Sa is 6144, three sliding windows can be generated: W1 is Sa(0-3071), W2 is Sa(1536-4607), and W3 is Sa(3072-6143).

[0069] Step 4: Perform FFT on each window data W1, W2, W3 to obtain the window spectrum sequence Fw1, Fw2, Fw3.

[0070] Step S203: In the window spectrum sequence, for each candidate peak in the candidate peak information, extract its phase value at the corresponding frequency position in each window spectrum to form a phase evolution sequence that characterizes the change of the candidate peak phase over time, and extract the true signal peak information based on the phase evolution sequence.

[0071] Specifically, assuming that n candidate peaks are detected in the previous embodiment, forming a set P = {p1, p2, ..., pn}. For any candidate peak pi (whose frequency position is fi):

[0072] Extract the complex value at frequency fi in Fw1 and calculate its phase to obtain φi(1).

[0073] Extract the complex value at frequency fi in Fw2 and calculate its phase to obtain φi(2).

[0074] Extract the complex value at frequency fi in Fw3 and calculate its phase to obtain φi(3).

[0075] Thus, the phase evolution sequence of the candidate peak pi is obtained as {φi(1), φi(2), φi(3)}. The phase sequences of all peaks form a phase sequence matrix Φ.

[0076] Step S204: extracting true signal peak information based on the phase evolution sequence.

[0077] Specifically, a mathematical model is built for the phase evolution sequence corresponding to each candidate peak, and a quadratic polynomial model is fitted using the least squares method to obtain a set of model parameters that characterize the dynamic characteristics of the sequence.

[0078] Based on the model parameters, the phase curvature or phase continuity score of the phase evolution sequence is quantified, and based on the quantization result, it is determined whether the corresponding candidate peak is a true signal peak, and the peak information is extracted.

[0079] The decision is based on an iterative robust estimation process and further includes a bidirectional consistency check. This is a multi-stage refinement process with the following steps:

[0080] Step 1: Initial Modeling and Fitting: For each peak phase evolution sequence {φi(k)}, establish a time-phase model: φ(t) = a0 + a1*t + a2*t² / 2. Use the least squares method to fit the model parameters [a0i, a1i, a2i] corresponding to each peak, forming the model parameter matrix A. Simultaneously calculate the phase curvature κi = |a2i|.

[0081] Step 2: Iterative robust estimation:

[0082] Loop 1: Using the current model parameters A, the phase of each window is predicted and the prediction error e is calculated. Anomalous phase points whose prediction error exceeds a preset statistical threshold (e.g., 2 standard deviations) are identified. These anomalous points are temporarily removed from the phase evolution sequence, and a quadratic polynomial model is refitted to the remaining phase points to obtain an updated set of model parameters A.

[0083] Loop 2-3: Repeat the above process 2 times, for a total of 3 iterations. Finally, a more robust optimized model parameter matrix A_opt is obtained after outliers are eliminated.

[0084] Bidirectional consistency check:

[0085] Forward prediction: Use A_opt to forward predict the phase φ_forward of the third window based on the phase value of the first window.

[0086] Backward prediction: Using A_opt, according to the actual phase value of the third window, the phase φ_backward of the first window is reversely deduced.

[0087] Calculate the consistency metric Ci = |φ_forward(i) - φ_backward(i)| / (2π). The smaller the value, the more consistent the phase evolution trajectory is with the physical model, and the better the consistency.

[0088] Final decision: Set a threshold, for example, consistency measure C < 0.1 and phase curvature κ < κ_threshold. Only candidate peaks that meet both conditions are finally confirmed as true signal peaks p_true.

[0089] The following is a specific and reproducible example to illustrate the working process of the present invention in a low signal-to-noise ratio environment.

[0090] Scene setting:

[0091] Communication system: direct sequence spread spectrum system.

[0092] Signal parameters: chip rate Rc = 8 Mcps, physical frame preamble length is 3072 chips.

[0093] Channel conditions: The signal has a frequency offset of +61.23 kHz and an SNR of -12.5 dB, which is a typical low SNR and harsh environment.

[0094] The capture process includes the following steps:

[0095] Step 1: Signal Entry and Preliminary Processing (Example 1): The receiver performs matched filtering and decimation by four on the ADC-sampled signal. Due to an SNR of -12.5dB, if a single-frame signal is directly processed using PMF-FFT, the signal peak energy is approximately 100-120, drowning in the noise and making it impossible to set an effective threshold for detection.

[0096] Step 2: Structured search: The system initiates a structured search strategy.

[0097] First attempt (direct accumulation of two groups): The system performs direct vector accumulation (Sa = Sd_1 + Sd_2) on two consecutive signal frame segments Sd_1 and Sd_2. A PMF-FFT operation is performed on Sa. The peak energy increases to approximately 160, and the signal-to-noise ratio improves somewhat, but still not significantly. Assume that this peak value does not reach the preset threshold.

[0098] Second attempt (two phase shifts and accumulation): The system applies a π phase shift to the second signal, Sd_2, and then accumulates it using Sa = Sd_1 + phase_shift(Sd_2,π). The peak energy drops to between 100 and 120, making the effect even worse. This is because the actual phase difference between the two frames caused by the +61.23kHz frequency offset is not close to π.

[0099] Third attempt (4 accumulation and phase shift): Since the first two attempts failed, the system increases the number of accumulated signal groups to 4. Different phase shift combinations are then used for phase shift accumulation. When the phase shift combination [0, π / 2, π, 3π / 2] is used, Sa = Sd_1 + shift(Sd_2, π / 2) + shift(Sd_3, π) + shift(Sd_4, 3π / 2).

[0100] Step 3: Peak identification and verification, specifically: Figure 9 In the spectrum data, a sharp peak with an energy value of 350 can be clearly seen, which is much higher than the noise floor. This peak passes the initial amplitude threshold detection.

[0101] Furthermore, the system preferably initiates peak timing phase fingerprint verification. A sliding window phase extraction is performed on the peak to obtain a phase evolution sequence. Since this is a true signal peak, its phase evolution sequence will exhibit good continuity and smoothness. After iterative robust estimation and bidirectional consistency verification, its consistency metric C will be much less than 0.1, and the phase curvature κ will also be at a very low level. However, in this verification step, the phase sequences of some other noise points with slightly higher amplitudes on the spectrum graph will appear random, and they will not pass the model fitting and consistency verification, and will therefore be eliminated.

[0102] The precise re-search and output further includes: Through the above steps, the system confirms the existence of the true signal peak and calculates the frequency offset of approximately +61.23kHz from its frequency point position, thereby obtaining a rough signal arrival location. Furthermore, the system initiates the precise re-search module. The estimated frequency offset is used to calibrate the frequency offset of the original, undecimated matched filtered signal Sm. Based on this, a fine correlation is performed within a small window near the rough location (for example, three samples forward and three samples backward), ultimately outputting a signal arrival location accurate to a single sample point.

[0103] From this specific example, it can be seen that the present invention can achieve good signal capture even under extremely harsh conditions such as SNR=-12.5dB by working together with multiple means such as multi-frame accumulation, structured phase shift search, and advanced peak verification.

[0104] Example 3: A predetermined precision phase shifting method based on an adaptive lookup table and error diffusion is proposed. This embodiment, as a preferred implementation of the selective phase shifting and vector accumulation steps in Example 1, provides a technical solution aimed at solving the quantization error and cumulative energy leakage problems caused by traditional phase shifting operations (such as simple I / Q switching).

[0105] The method comprises the following steps:

[0106] Step S301: Initialize the adaptive resolution lookup table.

[0107] A non-uniform resolution lookup table (LUT) is a data structure used to store the mapping relationship between phase angles and complex phase shift coefficients. Its characteristic is that it has different angular resolutions (step sizes) in different phase intervals.

[0108] Phase-shift sensitivity refers to the magnitude of the quantization error produced by a specific phase angle when a simplified phase-shift operation (such as I / Q swapping) is used. Typically, sensitivity is highest near odd multiples of 45°.

[0109] This step is executed during system initialization and includes the following steps:

[0110] Step 1: Create a phase shift sensitivity map:

[0111] Divide the phase space from 0 to 2π into 16 or more basic intervals. Analyze the errors caused by applying coarse phase shifts (such as I / Q swapping to approximate π / 2 phase shifts) within each interval through simulation or theoretical calculation. Mark regions with larger errors, such as 45°±5° and 135°±5°, as highly sensitive, and the remaining regions as less sensitive.

[0112] Step 2: Build a non-uniform lookup table LUT:

[0113] Based on the sensitivity map, a lookup table is generated with non-uniform resolution. For highly sensitive areas, precise phase shift coefficient values ​​are generated with a higher resolution (e.g., 1° steps). For less sensitive areas, phase shift coefficient values ​​are generated with a lower resolution (e.g., 5° steps). Each table entry contains an angle θ and its corresponding precise complex phase shift coefficient c = [cos(θ), sin(θ)].

[0114] Step 3: Initialize error diffusion parameters:

[0115] Set a cumulative error vector E for storing historical phase shift errors, whose length is equal to the maximum number of accumulations, and initialize all its elements to zero. At the same time, set an error diffusion coefficient α, for example, α = 0.3.

[0116] This initialization step can achieve the highest possible phase shift accuracy with limited storage resources. By performing sensitivity analysis on the phase space and concentrating storage and computing resources in the most sensitive areas where errors are most likely to occur, optimal resource allocation is achieved.

[0117] Step S302: Based on the pre-stored target phase shift angle, a complex phase shift coefficient of a predetermined accuracy is searched or interpolated in a pre-constructed non-uniform resolution lookup table with different angular resolutions in different phase intervals; the complex phase shift coefficient is used to perform a complex multiplication operation on the signal frame segment to obtain the signal frame segment after precise phase shifting, and subsequent vector accumulation is performed.

[0118] The method further includes a phase shift error diffusion mechanism, which calculates a complex phase shift coefficient according to a target phase shift angle and uses the coefficient to perform operations.

[0119] This step is called when a phase shift operation needs to be performed on a signal frame segment.

[0120] Read the historical phase shift error stored in the previous phase shift operation, and compensate the current target phase shift angle based on the historical phase shift error to obtain the compensated phase shift angle:

[0121] Assuming this is the nth accumulation, the target phase shift angle is θ_target. The system reads the previous historical error e_prev = E(n-1) from the accumulated error vector E. Furthermore, the actual phase shift angle after compensation is calculated as θ_comp = θ_target - α * e_prev.

[0122] Based on the compensated phase-shift angle, a complex phase-shift coefficient of a predetermined precision is determined in a non-uniform resolution lookup table. Specifically, two adjacent table entries on either side of the target phase-shift angle are located in the non-uniform resolution lookup table. A set of interpolation weights that satisfy an energy conservation constraint is calculated. This constraint ensures that the modulus of the final complex phase-shift coefficient generated by weighted combination of the complex phase-shift coefficients in the two adjacent table entries is 1. The complex phase-shift coefficients of the two adjacent table entries are weightedly summed using this set of interpolation weights to obtain a complex phase-shift coefficient of a predetermined precision.

[0123] In the lookup table LUT, the two most adjacent table entries on both sides of θ_comp are found through binary search or other efficient search algorithms, and are recorded as [θ1, c1] and [θ2, c2].

[0124] Compute a set of interpolation weights w1 and w2 that satisfy the energy conservation constraint (i.e., the modulus of the interpolated coefficients is 1). This can be achieved by solving the equations w1 + w2 = 1 and |w1*c1 + w2*c2|² = 1. For computational simplicity, linear interpolation of the weights can also be used, followed by normalization.

[0125] The interpolation weights are used for weighted summation to obtain the interpolated phase shift coefficient c_interp = w1*c1 + w2*c2. This coefficient c_interp is the final complex phase shift coefficient c_final of predetermined precision.

[0126] The complex phase shift coefficient is used to perform complex multiplication on the signal frame fragment to obtain the signal frame fragment after precise phase shift, and then perform subsequent vector accumulation:

[0127] Perform complex multiplication on the input signal frame segment Sd and the calculated c_final to obtain a phase-shifted signal Sd_shifted=Sd*c_final.

[0128] After completing the phase shift operation on the signal frame segment using the complex phase shift coefficient, the difference between the actual phase shift angle represented by it and the target phase shift angle is calculated, and the current phase shift error is generated and stored as the historical phase shift error for the next phase shift operation:

[0129] Calculate the actual phase shift angle θ_real = atan2(imag(c_final),real(c_final)). Based on this, calculate the phase shift error for this operation, e_current = θ_real - θ_target. Furthermore, store this error in the cumulative error vector E, that is, E(n) = e_current, for use in the next phase shift.

[0130] Specifically, this step uses a closed-loop prediction-compensation-correction mechanism to dynamically compensate for the tiny errors introduced by table lookup and interpolation in each operation and propagate them to the next operation. This error diffusion mechanism effectively prevents the one-way accumulation of errors, ensuring that the total phase deviation remains at an extremely low level after multiple accumulations, thereby ensuring the efficiency of vector accumulation and the ultimate signal-to-noise ratio gain.

[0131] Step S303: The non-uniform resolution lookup table is dynamically updated, and the updating method includes: periodically counting the frequency distribution of target phase shift angles used in past phase shift operations, and identifying the angle intervals used at high frequencies;

[0132] Adaptively improve the angular resolution of high-frequency angle intervals and update the non-uniform resolution lookup table to provide more appropriate phase shift accuracy for commonly used angles in subsequent phase shift coefficient calculations.

[0133] The specific steps include:

[0134] Step 1: Count usage frequency: The system maintains an angle usage counter U. Each time a phase shift operation is performed, the counter of the corresponding angle interval is incremented by one.

[0135] Step 2: Dynamically adjust the resolution: Every preset period (for example, after 100 phase shifts), the system analyzes the counter U and identifies the angle intervals that are used frequently (for example, the intervals that are used more than the average value plus one standard deviation).

[0136] Step 3: Update the Lookup Table: For these frequently used intervals, if the current step size is larger, the system recalculates and updates the lookup table entries for that interval at a higher resolution (for example, from a 5° step size to a 1° step size). To prevent the lookup table from growing indefinitely, a total size limit can be set. When adding new entries, some entries for the least frequently used intervals can be removed.

[0137] Specifically, the dynamic update mechanism enables adaptive learning capabilities in the lookup table. In actual operation, due to specific frequency offsets, the required phase shift angles may be concentrated in certain areas. This mechanism dynamically reallocates limited storage resources to these hotspots, further improving the phase shift accuracy of commonly used angles without increasing overall costs.

[0138] Example 4: Proposing a structured multi-stage phase shift and accumulation search strategy. This embodiment provides a systematic search strategy for selective phase shift and vector accumulation steps, aiming to address the problem of phase difference with uncertain amplitude caused by unknown frequency offset.

[0139] Step S401: performing direct vector accumulation on two groups of signal frame segments to obtain a first accumulated signal, and performing subsequent frequency domain transformation and analysis and peak recognition on the first accumulated signal;

[0140] On the premise that no candidate peak meeting the preset threshold is identified based on the first accumulated signal, a π phase shift is applied to one group of signal frame fragments, and vector accumulation is performed with another group of signal frame fragments to obtain a secondary accumulated signal, and frequency domain transformation and analysis as well as peak recognition are performed on the secondary accumulated signal.

[0141] The strategy is the starting point of the capture process and consists of the following steps:

[0142] Step 1: First Search (Direct Accumulation): Take the first set of signal frame segments, Sd(0)...Sd(L-1), and the second set, Sd(L)...Sd(2L-1), and perform direct vector accumulation to obtain the first accumulated signal, Sa_1. Perform subsequent PMF-FFT and peak identification on Sa_1. If a peak meeting the threshold is successfully identified, the acquisition is successful, and the process ends.

[0143] Step 2: Second Search (π Phase Shift and Accumulation): If the first search fails, the phase shifter module is activated to apply a π phase shift to the second set of signal frame segments Sd(L)...Sd(2L-1). This is then vector-accumulated with the first set of signals to obtain the secondary accumulated signal Sa_2. PMF-FFT and peak identification are then performed on Sa_2.

[0144] Specifically, the phase difference Δφ between the two frames is unknown. If Δφ is close to 0, direct accumulation works best. If Δφ is close to π, direct accumulation will cause signal cancellation, while π-shifted accumulation achieves in-phase superposition. These two searches cover both extremes of the phase difference and can effectively handle most scenarios involving two-frame accumulation.

[0145] Step S402: if the two attempts of the structured search strategy for the two groups of signal frame segments fail to identify a candidate peak that meets the preset threshold, then the number of accumulated signal frame segments is increased to four groups;

[0146] The four groups of signal frame fragments are sequentially subjected to phase shifting and vector accumulation using different preset phase combinations. The accumulated signal generated by each combination is used to perform frequency domain transformation and analysis as well as peak recognition until the candidate peak is successfully identified.

[0147] If both searches in step S401 fail, the system determines that a higher signal-to-noise ratio gain is needed and automatically increases the number of accumulated frames to four groups (Sd_1, Sd_2, Sd_3, Sd_4). Furthermore, the phase shifter will try multiple different phase combinations in a preset order for phase shift accumulation.

[0148] Optionally, the preset phase combinations may include:

[0149] Combination a: Phase shift the second, third, and fourth signal groups by [π / 2, π, 3π / 2] respectively.

[0150] Combination b: Phase shift the second, third, and fourth signal groups by [π, 0, π] respectively.

[0151] Combination c: Phase shift the second, third, and fourth signal groups by [3π / 2,π,π / 2] respectively.

[0152] Each time a combination is tried, an accumulated signal Sa is generated and a PMF-FFT and peak identification are performed. Once the identification is successful, the search stops.

[0153] Specifically, the cumulative sum of the four signals yields a theoretical gain of 4.17dB to 6dB, enabling even lower signal-to-noise ratios. By trying multiple preset phase combinations, it's equivalent to using different rotation matrices in four-dimensional vector space to align the signal vectors. This further increases the probability of finding a solution that allows the four signals to superimpose in phase, systematically resolving the problem of unknown frequency offset.

[0154] Step S403: Phase shifting is performed on the four groups of signal frame segments. When the phase shift angle in the preset phase combination is π / 2, π, or 3π / 2, the phase shift operation specifically involves swapping or inverting the in-phase and quadrature components of the signal frame segments to achieve the corresponding phase rotation without consuming multiplier computing resources. When the phase shift angle is π / 2, the swapping or inverting operation specifically involves inverting the quadrature components of the original signal frame segments and using them as the in-phase components of the phase-shifted signal frame segments; and directly using the in-phase components of the original signal frame segments as the quadrature components of the phase-shifted signal frame segments.

[0155] This is a preferred implementation method for efficiently performing the specific angle phase shift in step S402. For a complex signal Sd=I+jQ:

[0156] Phase shift π / 2(j): Sd_shifted=(-Q)+j(I);

[0157] Phase shift π(-1): Sd_shifted=(-I)+j(-Q);

[0158] Phase shift 3π / 2(-j): Sd_shifted=(Q)+j(-I).

[0159] Specifically, the core advantage of this step is that it consumes no computing resources. In hardware implementations such as FPGAs, complex multipliers are an excellent logical resource. Through simple wiring swaps (I / Q swaps) and inverters (logical NOT gates), phase rotations of integer multiples of π / 2 can be achieved, eliminating the need for multipliers and further reducing hardware cost, power consumption, and processing latency.

[0160] Embodiment 5 provides a method for re-searching the precise signal arrival position based on prior information.

[0161] Specifically, this embodiment describes a final refinement step performed after successfully capturing a signal and obtaining a rough signal arrival position and frequency offset through the method of any of the aforementioned embodiments.

[0162] Step S501: The calculated signal arrival position is used as the prior signal arrival position (rough signal arrival position), and the frequency offset of the matched filtered signal is calibrated using the frequency offset. Within the matched filtered signal after frequency offset calibration, a search window (a small-scale signal sequence) of a predetermined width is defined with the prior signal arrival position as the center, and the signal within the search window is intercepted as the signal sequence to be refined (a small-scale signal sequence). The signal sequence to be refined is further correlated, and the position with the maximum correlation peak is used as the final output of the precise signal arrival position. The specific steps are as follows:

[0163] Step 1: Frequency offset calibration. Obtain the coarse frequency offset Δf and coarse position Pos_coarse from the acquisition phase. Take the original, undecimated matched filtered signal Sm. Perform frequency offset compensation on Sm to obtain the calibrated signal Sw(t) = Sm(t)*exp(-j*2*π*Δf*t).

[0164] Step 2: Define the search window and truncation sequence. With Pos_coarse as the center, define a search window of predetermined width. Considering the maximum uncertainty introduced by quadruple decimation, the window width can be set to include three additional samples forward and backward. This extracts seven signal sequences from Sw that are the length of the preamble sequence and have overlapping starting positions for the refined search.

[0165] Step 3: Re-correlation and precise positioning. Perform a complete complex cross-correlation operation on each of the seven sequences with the local preamble sequence. Compare the peak values ​​of the seven correlation results. The peak with the largest amplitude is used as the starting position of the final output signal, which is accurate to a single sampling point.

[0166] Specifically, this step eliminates the position uncertainty introduced by step S102 (signal extraction) in Example 1. Frequency offset calibration is performed to maximize signal energy concentration during re-correlation. Furthermore, a narrow, predetermined-accuracy search around the coarse position on the original, undecimated signal can improve the signal position accuracy from ±2 samples to ±0 samples with minimal additional computational effort, providing a good initial value for subsequent demodulator lock.

[0167] In another preferred embodiment of the present invention, the acquisition method can be designed and implemented based on the Xilinx xc7z100ffg900 chip as a hardware platform. The method is applicable to continuous channels or quasi-continuous channels.

[0168] A continuous channel is a channel mode in which signal frames are sent continuously without any data idle periods. The format of the transmitted frame is shown in Table 1, where the preamble sequence and frame content appear alternately and continuously.

[0169] Table 1 Continuous channel transmission frame

[0170] Leading 1 Frame Content 1 Leading 2 Frame Content 2 Leading 3 Frame content 3 Leading 4 Frame Content 4 …… …… …… …… Leading N Frame content N

[0171] A quasi-continuous channel is one in which signal frames are transmitted irregularly, with random-length idle periods between frames. The format of the transmitted frame is shown in Table 2.

[0172] Table 2 Quasi-continuous channel transmission frame

[0173] Leading 1 Frame Content 1 Leading 2 Frame Content 2 No data sent No data sent Leading 3 Frame content 3 No data sent Leading 4 Frame Content 4 No data sent …… …… Leading N Frame content N

[0174] The present invention utilizes the characteristics of continuous and slow changes in signal parameters (such as frequency deviation, phase deviation, and bit timing offset) in these two channels to improve capture performance through multi-frame processing.

[0175] According to another aspect of the present application, some steps in Example 1 may also be (the same steps as in Example 1 are not described in detail):

[0176] Step S103: performing selective phase shifting and vector accumulation on at least two groups of signal frame segments in the extracted signal to fuse them into an accumulated signal.

[0177] Specifically, because the preamble sequence of each frame in a spread spectrum communication system is fixed and identical in continuous or quasi-continuous channel mode, by coherently adding corresponding parts of multiple frames of signals, the signal components are superimposed in phase, while the noise components are superimposed incoherently. This can effectively improve the signal-to-noise ratio.

[0178] Without considering the impact of bit timing offset, the ideal accumulation of the two sets of signals can achieve a 3dB signal-to-noise ratio gain. Even considering the impact of frequency offset, the phase shifting processing of the present invention can still achieve a gain of 1.51dB to 3dB. This is sufficient to compensate for the loss caused by decimation in step S102 and achieve additional processing gain.

[0179] Step S105: searching the spectrum data and identifying candidate peaks that meet a preset threshold, extracting the candidate peak information; and calculating and outputting the signal arrival position and frequency deviation based on the candidate peak information.

[0180] According to one aspect of the present application, the effect of multi-frame accumulation may also be affected by the bit timing drift at both the transmitting and receiving ends. However, in the application scenario of the present invention, such as the communication between a geostationary satellite and a ground station, the relative speed is low and the Doppler effect is not obvious. At the same time, the crystal oscillator technology of modern terminal equipment is becoming increasingly mature. Based on this analysis, the normalized bit timing deviation caused by the Doppler effect and the crystal oscillator technology in the system is usually no more than 1×10 -7 Based on this deviation level, the bit timing deviation between two signal vector groups does not exceed 1%, and the bit timing deviation between four signal vector groups does not exceed 2.3%. Therefore, in such applications, the effect of the signal-to-noise ratio enhancement caused by bit timing deviation is limited and almost negligible, which provides feasibility support for the technical solution of multi-frame accumulation adopted in the present invention.

[0181] Example 6: Provide a system environment and parameter configuration example to centrally illustrate the specific parameters and environment of the system. In this embodiment, the main technical indicators of the designed demodulator are set as follows:

[0182] Coded modulation method: binary phase shift keying (1 / 2BPSK) with a code rate of 1 / 2.

[0183] Spreading factor: configurable to 1, 2, 4, 8, or 16.

[0184] Chip rate range: supports 1Mcps to 16Mcps.

[0185] Demodulation threshold: Set the ratio of bit energy to noise power spectral density (Eb / n0) to not less than 2.5dB.

[0186] Specifically, these parameters together define a typical high spreading ratio and low signal-to-noise ratio working scenario. For example, when the spreading factor is selected as 16, the ratio of the symbol energy of the signal before despreading to the noise power spectrum density (Es / n0) and the demodulation threshold (Eb / n0) are related to Es / n0(dB)=Eb / n0(dB)-10*log10(spreading factor). Substituting the numerical values, we can obtain Es / n0=2.5-10*log10(16)≈2.5-12.04=-9.54dB. Taking into account the influence of the bit rate, the signal-to-noise ratio of the signal will be even lower when entering the capture module. This shows that the target working environment of the capture device designed by the present invention is in such a harsh channel where the signal energy is far lower than the noise level. The present invention achieves reliable capture of signals with Es / n0 as low as approximately -12.5dB under the system environment defined by the above indicators through a combination of a series of technical means described in the aforementioned embodiments, such as multi-frame accumulation, predetermined precision phase shifting, efficient frequency domain search, and high-reliability peak verification, thereby meeting the overall design requirements of the system.

[0187] Direct sequence spread spectrum technology in satellite communications has the advantages of strong anti-fading, anti-multipath interference, anti-interception capabilities and low transmission power density. It is suitable for small-aperture antennas and can reduce interference from neighboring satellites. Its main users are the Internet of Things or other low-data-rate transmission application scenarios.

[0188] This application designs a demodulator, whose main indicators are as follows:

[0189] Code modulation: 1 / 2BPSK;

[0190] Spreading factors: 1, 2, 4, 8, 16;

[0191] Chip rate: 1Mcps-16Mcps;

[0192] Demodulation threshold: Eb / n0 = 2.5 dB;

[0193] In direct sequence spread spectrum, a single bit of information is converted to multiple bits after a spreading sequence. Furthermore, the symbol is modulated and transmitted without changing the system's transmit power, resulting in a low frequency density of the spread signal. According to the demodulator specifications in this application, when the spreading factor is 16, Es / n0 after spreading is -12.5dB. The present invention provides a high-efficiency acquisition method for a low signal-to-noise ratio direct sequence spread spectrum channel, specifically:

[0194] Step S601: Start the matched filter module to perform matched filtering on the signal after analog-to-digital conversion (ADC), and the signal after matched filtering is recorded as Sm;

[0195] Step S602: Start the signal extraction module and perform quadruple decimation on Sm. The extracted signal is recorded as Sd. The Sd signal with a length of L is stored in the local RAM (where the channel physical frame length is recorded as L, the ADC is quadruple sampling, and the number of sampling points per physical frame is 4L), recorded as Sd(0), Sd(1) ... Sd(L-1);

[0196] Step S603: Start the accumulator module, then take Sd(L)…Sd(2L-1) from the Sd sequence and add it to Sd(0), Sd(1)…Sd(L-1) in the RAM to obtain Sa(0)…Sa(L-1);

[0197] Step S604: Start the PMF_FFT module, perform FFT operation on the Sa(0)…Sa(L-1) parts after matching;

[0198] Step S605: Start the threshold detection module to perform peak search and threshold judgment on the spectrum of Sa(0)...Sa(L-1). If the threshold does not meet the conditions, go to S606; otherwise, go to S610.

[0199] Step S606: If the current search is the first of two accumulated groups, go to step S607; if the current search is the second of two accumulated groups, go to step S608; if the current search is the first, second, or third of four accumulated groups, go to step S609; otherwise, go to step S603 and reinitialize the parameters;

[0200] Step S607: Start the phase shifter module, perform a π phase shift on the Sd(L)…Sd(2L-1) sequence, and add it to Sd(0), Sd(1)…Sd(L-1) to obtain Sa(0)…Sa(L-1), and go to S604;

[0201] Step S608: Start the accumulator module, and continuously take Sd(0), Sd(1)…Sd(L-1), Sd(L), Sd(L+1)…Sd(2L-1), Sd(2L), Sd(2L+1)…Sd(3L-1), Sd(3L), Sd(3L+1)…Sd(4L-1) from the Sd sequence, add them up, obtain Sa(0)…Sa(L-1), and go to S604;

[0202] Step S609: Start the phase shifter module, perform phase shift accumulation on the Sd signal, obtain Sa(0)…Sa(L-1), and go to step S604;

[0203] Step S610: Start the frequency deviation detection module, calculate the frequency deviation, and output the signal arrival position and frequency deviation;

[0204] Step S611: starting the frequency offset calibration module to perform frequency offset calibration on the signal;

[0205] Step S612: Start the signal re-search module, use the prior information of the signal in step S610 to re-estimate the signal in a small range, and output the accurate signal arrival position.

[0206] In a further embodiment, the received signal chip rate Rb is set to 8 Mcps, the frequency deviation is +61.23 kHz, and the SNR is -12.5 dB:

[0207] The signal is extracted after matched filtering without accumulation and PMF_FFT operation is performed. Its frequency domain characteristics are as follows: Figure 6 shown.

[0208] The signal is doubled according to the steps in the device and then PMF_FFT operation is performed.

[0209] The signal is shifted by π and then PMF_FFT operation is performed after accumulating it twice according to the steps in the device.

[0210] According to one aspect of the present application, the present invention successfully addresses the technical challenge of balancing computational complexity and search performance in traditional acquisition methods by performing fourfold decimation on the signal after matched filtering and decomposing the subsequent correlation operation into a structure combining partial matched filtering and fast Fourier transform (PMF_FFT). Specifically, the fourfold decimation directly reduces the amount of data required for subsequent processing by 75%, saving hardware cache and power consumption. The PMF_FFT structure transforms a highly complex full-length time-domain correlation operation into a fixed-computation FFT operation, whose frequency bin positions directly correspond to the signal's frequency offset information. This time-frequency conversion strategy enables the receiver to search a wide frequency offset range (for example, covering a frequency offset range of at least ±333.33 kHz at a specific bit rate) in a single operation, without the need for multiple parallel hardware channels. Therefore, this method can achieve efficient, wide-range acquisition of high-chip-rate signals on resource-constrained hardware platforms (such as Xilinx FPGAs), improving the system's practicality and cost-effectiveness.

[0211] According to another aspect of the present application, the invention effectively addresses the core technical issue of uncertain multi-frame signal accumulation gain under unknown frequency offset by designing a structured search strategy that evolves from two to four accumulation groups, and from simple phase shifting to multiple phase combinations. The strategy is essentially an automated, hierarchical solution search process. Through two low-cost approaches—direct accumulation and π-shifted accumulation—the system quickly covers the two most extreme cases of inter-frame phase differences close to 0 or π due to frequency offset. If failure indicates extremely low signal-to-noise ratio (SNR) and complex phase relationships, the system automatically upgrades to four accumulation groups to achieve a theoretical gain of up to 6dB. By trying multiple preset phase combinations (e.g., [π / 2, π, 3π / 2]), the system systematically searches for the optimal solution that aligns the signal vectors in four-dimensional vector space. This intelligent search mechanism eliminates the reliance on prior knowledge of channel frequency offset during the acquisition process, improving the system's adaptability and robustness in real, variable satellite communication scenarios and increasing the success rate of link establishment in poor channel conditions.

[0212] Specifically, the present invention addresses the problem of signal energy leakage caused by inaccurate phase shift quantization during multi-frame accumulation by introducing a predetermined-precision phase shifting method based on a non-uniform lookup table and a phase-shift error diffusion mechanism. When accumulating signals over four or more frames, the tiny quantization errors introduced by each phase shift operation accumulate, potentially causing the modulus length of the resultant vector to be significantly smaller than the theoretical value, thereby weakening the signal-to-noise ratio gain achieved through accumulation. The error diffusion mechanism in the present invention pre-compensates the current target phase shift angle by θ_comp = θ_target - α * e_prev by reading the historical phase shift error e_prev stored from the previous operation, effectively acting as a dynamic error correction process. This approach reduces the long-term accumulated average phase error to zero. This allows for high-precision alignment of signal vectors during multi-frame accumulation, achieving a certain degree of approaching the theoretical signal-to-noise ratio gain (for example, a gain of nearly 6dB when accumulating four groups). For systems that need to operate at extremely low signal-to-noise ratios (such as -12.5dB), this extra 1-2dB of gain is crucial, directly determining the success or failure of capture, and can be translated into extending the communication distance or reducing the terminal antenna size requirements.

[0213] On this basis, the present invention improves the reliability of capture decisions under extremely low signal-to-noise ratios by introducing a peak timing phase fingerprint verification technology, solving the high false alarm rate problem inherent in traditional amplitude threshold-based judgment methods. Instead of viewing a peak in the spectrum in isolation, it is considered a snapshot of a signal during its time evolution. By extracting the phase of the peak within multiple overlapping time windows, a phase evolution sequence Φ is formed, and a quadratic polynomial model is applied to it: φ(t) = a0 + a1*t + a2*t² / 2. The phase evolution of a real signal should be smooth and have a deterministic pattern (caused by a stable frequency offset), while the phase evolution of a noise spike is random and discontinuous. The present invention further uses iterative robust estimation and bidirectional consistency checking to strengthen this criterion, ensuring that only peaks whose phase evolution trajectories fully conform to the physical model are confirmed as real signals. This method of dual-verification of signals from two dimensions, energy and behavior, is equivalent to adding a true-false identifier with a predetermined accuracy to the capture system. It can effectively filter out the interference of noise pseudo-peaks, allowing the subsequent demodulation loop to lock on the real signal. This is of core value in ensuring the stable operation of unattended terminals or mission-critical communication links.

[0214] Furthermore, the present invention addresses the position accuracy loss caused by initial signal decimation by adding a precise re-search step after coarse acquisition, achieving a good balance between acquisition efficiency and final accuracy. While the initial quadruple decimation improves search efficiency, it also introduces timing ambiguity of approximately ±2 sampling points. Directly passing this ambiguous position information to the subsequent tracking loop would require a longer convergence time and could even result in lock failure. The present invention's re-search step utilizes the estimated frequency offset to calibrate the original, undecimated matched filtered signal Sm, eliminating phase rotation interference. Furthermore, it eliminates the need for a global search and instead performs full-resolution correlation operations within a very small window (e.g., seven sampling points) near the coarse position. This coarse-to-fine strategy improves timing accuracy from multiple sampling points to a single sampling point with minimal additional computational cost. This provides a high-quality initial anchor point for the subsequent carrier tracking and symbol synchronization loops, shortening the lock time of the entire link and improving the overall timeliness of the system from acquisition to data demodulation.

[0215] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the scope of protection of the present invention.

Claims

1. A high-efficiency acquisition method for a low signal-to-noise ratio direct sequence spread spectrum channel, characterized in that: include: Perform matched filtering and signal extraction on the analog-to-digital converted signal to obtain the extracted signal; performing selective phase shifting and vector accumulation on at least two groups of signal frame segments in the extracted signal to fuse them into an accumulated signal; Perform frequency domain transformation and analysis on the accumulated signal to obtain spectrum data; Search the spectrum data and identify candidate peaks that meet a preset threshold, and extract their candidate peak information; The signal arrival position and frequency offset are calculated and output based on the candidate peak information.

2. The high-efficiency acquisition method for a low signal-to-noise ratio direct sequence spread spectrum channel according to claim 1, wherein: Calculate and output the signal arrival position and frequency offset based on the candidate peak information, including: For the candidate peak information, combined with the accumulated signal, the peak timing phase fingerprint verification is applied to obtain the real signal peak information; Based on the actual signal peak information, the signal arrival position and frequency deviation are calculated and output.

3. The method according to claim 2, characterized in that Combined with the accumulated signal, peak timing phase fingerprint verification is applied to obtain the true signal peak information, including: Decompose the accumulated signal into multiple sliding windows with time overlap, perform frequency domain transformation on the data in each sliding window, and construct a window spectrum sequence; In the window spectrum sequence, for each candidate peak in the candidate peak information, its phase value at the corresponding frequency position in each window spectrum is extracted to form a phase evolution sequence that characterizes the change of the candidate peak phase over time, and the real signal peak information is extracted based on this.

4. The method according to claim 3, characterized in that Extract true signal peak information based on the phase evolution sequence, including: The phase evolution sequence corresponding to each candidate peak is mathematically modeled, and a set of model parameters that characterize the dynamic characteristics of the sequence is obtained by fitting a quadratic polynomial model using the least squares method. Based on the model parameters, the phase curvature or phase continuity score of the phase evolution sequence is quantified, and based on the quantization result, it is determined whether the corresponding candidate peak is a true signal peak, and the peak information is extracted.

5. The method according to claim 1, wherein Performs selective phase shifting and vector accumulation, including: According to the pre-stored target phase shift angle, a complex phase shift coefficient of a predetermined accuracy is calculated by searching or interpolating in a pre-constructed non-uniform resolution lookup table having different angular resolutions in different phase intervals; Complex multiplication operations are performed on signal frame segments using complex phase shift coefficients to obtain accurately phase-shifted signal frame segments, and subsequent vector accumulation is performed.

6. The method according to claim 5, characterized in that The method further includes a phase shift error diffusion mechanism, calculating a complex phase shift coefficient according to a target phase shift angle, and performing operations using the coefficient, including: Read the historical phase shift error stored in the previous phase shift operation, and compensate the current target phase shift angle based on the historical phase shift error to obtain the compensated phase shift angle; Determining a complex phase shift coefficient of predetermined accuracy in a non-uniform resolution lookup table based on the compensated phase shift angle; After completing the phase shift operation on the signal frame segment using the complex phase shift coefficient, the difference between the actual phase shift angle represented by it and the target phase shift angle is calculated, and the current phase shift error is generated and stored as the historical phase shift error for the next phase shift operation.

7. The method according to claim 5, characterized in that The non-uniform resolution lookup table is updated dynamically, and its update method includes: Periodically counting the frequency distribution of target phase shift angles used in past phase shift operations to identify frequently used angle intervals; Improve the angular resolution of the high-frequency angle range and update the non-uniform resolution lookup table.

8. The method according to claim 1, characterized in that Selective phase shifting and vector accumulation are performed, and a structured search strategy for two sets of signal frame segments is included, specifically: Performing direct vector accumulation on the two groups of signal frame segments to obtain a first accumulated signal, and performing subsequent frequency domain transformation and analysis and peak identification on the first accumulated signal; On the premise that no candidate peak meeting the preset threshold is identified based on the first accumulated signal, a π phase shift is applied to one group of signal frame fragments, and vector accumulation is performed with another group of signal frame fragments to obtain a secondary accumulated signal, and frequency domain transformation and analysis as well as peak recognition are performed on the secondary accumulated signal.

9. The method according to claim 8, characterized in that The structured search strategy further includes: If two attempts of the structured search strategy for two groups of signal frame segments fail to identify a candidate peak that meets a preset threshold, the number of accumulated signal frame segments is increased to four groups; The four groups of signal frame fragments are sequentially subjected to phase shifting and vector accumulation using different preset phase combinations. The accumulated signal generated by each combination is used to perform frequency domain transformation and analysis as well as peak recognition until the candidate peak is successfully identified.

10. The method according to claim 1, characterized in that Perform frequency domain transformation and analysis on the accumulated signal to obtain spectrum data, including: Perform frequency domain transformation and analysis on the accumulated signal. Specifically, perform point-by-point sliding window sampling on the accumulated signal and perform correlation operation with the local sequence to obtain the correlated signal. Dividing the correlated signal into a predetermined number of groups according to a pre-stored chip rate representing the system symbol rate, and summing the data in each group to generate a grouped summed sequence; Perform fast Fourier transform on the grouped summed sequence to obtain spectrum data.

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