Low signal-to-noise ratio large doppler underwater acoustic pseudo-random signal capturing method and system

Abstract

The application belongs to the technical field of underwater acoustic communication synchronization head capture, and discloses a low signal-to-noise ratio large Doppler underwater acoustic pseudo-random signal capture method, system and equipment, partial reconstruction is realized through an overlapping average mode, the number of chips involved in overlapping average is set as an overlap factor, PN chips in a specified tail area are subjected to overlapping average and are replaced by new PN chips, the PN code in the head area is unchanged, and a new PN code is obtained; after the local PN signal is subjected to partial reconstruction, multiple Doppler nodes are taken at a certain frequency offset step interval, baseband waveform interpolation is carried out according to a Doppler factor, multiple Doppler deformation PN waveform signals are formed, and then non-correlation accumulation is carried out to complete local waveform compression; when the FFT output spectrum peak value exceeds a peak-to-average ratio threshold value, it is considered that time domain coarse synchronization is completed. The application provides a solution for time domain capture and frequency domain synchronization of a PN synchronization signal in low signal-to-noise ratio large Doppler underwater acoustic communication.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic communication synchronization head acquisition technology, and particularly relates to a method, system and device for acquiring low signal-to-noise ratio Doppler underwater acoustic pseudo-random signals. Background Technology

[0002] Currently, synchronization head signals in underwater acoustic communication mainly fall into two categories: frequency-modulated (FM) synchronization signals and spread-spectrum pseudo-random (PN) synchronization signals. FM synchronization signals, especially hyperbolic FM synchronization signals, are insensitive to Doppler and are suitable for Doppler scenarios of underwater mobile platforms such as UUVs. However, their time-frequency characteristics are significant, resulting in poor concealment in military applications and adverse noise impacts on marine life. Spread-spectrum pseudo-random synchronization signals have low spectral energy density and are widely applicable in military and bio-friendly scenarios. When used as a synchronization preamble, PN signals are extremely sensitive to Doppler, enabling accurate Doppler estimation while simultaneously acquiring the signal in the time domain, providing crucial reference for accurate windowing and Doppler compensation. To address the severe attenuation of correlation gain in PN signals under Doppler conditions, PN synchronization signal acquisition is necessary to achieve communication synchronization in both the time and frequency domains.

[0003] It should be noted that due to its superior anti-interference capability, PN synchronization signals have long been widely used in low Earth orbit (LEO) satellite communication systems. Currently, underwater acoustic spread spectrum signal acquisition technology heavily borrows synchronization algorithms from deep space communication. However, due to the low frequency and slow speed of sound underwater, the Doppler effect is more severe than in radio frequency wireless communication; the Doppler factor in underwater acoustic communication is 10 times that of radio frequency communication. 3 The above points indicate that existing mainstream algorithms are not entirely applicable in the acquisition of spread spectrum synchronization signals for Doppler mobile underwater acoustic communication.

[0004] Currently, underwater acoustic PN signal acquisition technology basically follows LEO spread spectrum signal acquisition technology, including serial search method (ambiguity function method) of time-frequency two-dimensional search, parallel search method of circular correlation pseudo-random code phase, and parallel search method of partial correlation-fast Fourier transform (PMF-FFT) frequency domain. In recent years, the newly emerging time-frequency domain compression acquisition algorithm, folding (XFAST) or double folding acquisition algorithm are all improvements or integrations of the above three basic acquisition algorithms. The earliest proposed ambiguity function method was based on a two-dimensional time-frequency search. It searches for the ambiguity function using a certain step size in both the time and frequency domains. This algorithm has a large computational cost and can cause communication decoding lag in the Doppler context. The circular correlation pseudo-random code phase parallel search method achieves circular convolution correlation operation through frequency domain multiplication and long-term signal parallel search through zero-padding FFT. However, due to the serial search based on a certain step size in the frequency domain, the computational load is large in the Doppler context. The PMF-FFT frequency domain parallel search method obtains partial correlation values ​​by windowing time domain slices, performs FFT on the correlation results, and considers synchronous acquisition as achieved when the peak value of the FFT spectrum exceeds a threshold. It can achieve frequency domain parallel search and is suitable for application scenarios with a large Doppler frequency offset range.

[0005] The traditional PMF-FFT algorithm: After bandpass filtering and down-conversion, the received signal is windowed and divided into M groups. These groups are then aligned with the local baseband PN signal and fed into an X-stage partially matched filter (PMF). N is the length of the PN code to be acquired, and the sampling factor (i.e., the number of sampling points per chip) is k, so Nk = Mx. The M PMF outputs are then subjected to FFT compensation. When the output FFT amplitude exceeds a set threshold, the currently received signal is considered a PN code synchronization signal with a phase error of 1 / 2T. c Meanwhile, the frequency corresponding to the maximum amplitude of the FFT is the estimated Doppler frequency offset, and the frequency offset estimation error is 1 / MXT. c First, the spread spectrum synchronization signal is modeled. Without loss of generality, this invention uses BPSK modulation. Let the carrier frequency f... c The transmitted signal amplitude is B, the pulse shaping filter is g, N is the length of the PN code to be captured, and the duration of a single chip is T. c The chip sequence corresponding to the spread spectrum pseudo-random code (PN code) is c = [c1c2…c N Then, the passband signal transmitted by the transmitter corresponding to the PN code can be expressed as:

[0006]

[0007] The underwater acoustic channel impulse response function (CIR) can be expressed as:

[0008]

[0009] Here, L represents the number of intrinsic acoustic rays received by the receiving node, and al With τ 0l Let represent the amplitude and relative time delay of the l-th intrinsic acoustic ray to the receiving node, respectively. Thus, the received signal after passing through the underwater acoustic multipath channel can be expressed as:

[0010]

[0011] in, For convolution, n(t) represents additive noise. The received signal is mixed with the local carrier oscillator and then low-pass filtered to complete down-conversion, i.e., carrier stripping, yielding the baseband signal. However, in the Doppler background, the carrier generates redundant frequency offset, which modulates the down-converted signal, causing a phase jump within the PN code. Without loss of generality, setting the initial phase to 0, the down-conversion output can be expressed as follows:

[0012]

[0013] Where β is the Doppler factor, s[(1+β)t] is the signal waveform compression extension term, and exp(j2πβf) c t) represents the carrier phase jump effect term. Since signal waveform compression spread is a slowly varying term in the weak Doppler background, traditional algorithms mainly focus on the effect of carrier phase jump. However, when the PN code is long and the underwater Doppler factor β is large, the time-domain compression spread is no longer an approximate negligible term.

[0014] After discretizing the signal in equation (5) according to the sampling rate, it is divided into M segments by the local baseband PN signal x. The X sampling points of each segment are aligned, multiplied, and summed to obtain M partial correlation values:

[0015]

[0016] Traditional algorithms assume that, assuming β is small, β in the waveform deformation term of equation (5) is approximately zero. Based on the properties of PN signals, when entering… Figure 1 When the received signal of the delay line is synchronized with the local baseband PN signal in the time domain, the amplitude-frequency response of the PMF-FFT output (ignoring noise) is:

[0017]

[0018] At this point, the amplitude-frequency response output after taking its maximum value is a regular scallop structure modulated by a partially correlated sinc function. In reality, underwater acoustic propagation frequencies cannot reach such high frequencies; this is only used as a control group with high carrier frequency and low Doppler factor. It can be seen that the scallop structure is obvious within the frequency shift range shown on the horizontal axis of the figure, the peak line shows a small change in amplitude, extending the Doppler tolerance and achieving the effect of parallel search in the frequency domain. In satellite communication, the carrier frequency can reach 10... 3MHz, the assumptions from equations (6) to (7) are reasonable, that is, in the context of common applications (10 3 The changes in the PMF-FFT spectrum peak lines are relatively small at the Hz level.

[0019] The traditional PMF-FFT algorithm is designed for satellite communications in the radio field, where the Doppler factor is relatively small. However, when porting it to underwater acoustic communication PN synchronization head acquisition, the Doppler factor in underwater acoustic communication is only 10 times that of radio frequency communication. 3 The above causes severe waveform distortion of the PN signal under underwater Doppler background, resulting in chip alignment misalignment even under PN time-domain synchronization. This makes the traditional PMF-FFT algorithm unsuitable for Doppler underwater acoustic PN signal acquisition, requiring corresponding improvements.

[0020] Without loss of generality, the following simulations and experiments all use the following parameter settings: the number of PN code chips is 2047, the sampling factor k after downsampling is 4, the number of groups M is set to 512, each group has 4 chips (the last group has 3 chips), the carrier frequency fc = 7 kHz, and the chip width Tc = 0.5 ms. The amplitude-frequency response diagrams of the traditional PMF-FFT method under different carrier backgrounds are shown. The carrier frequency of 280 kHz serves as the control group (without physical significance in underwater acoustic communication). The carrier frequencies of 28 kHz, 14 kHz, and 7 kHz correspond to the high, medium, and low frequency bands commonly used in underwater acoustic communication, respectively. To achieve relatively low propagation loss and long-distance propagation, spread spectrum communication often uses the medium and low frequency bands. The following simulations and sea trials use a 7 kHz carrier. It can be seen that in the commonly used frequency bands of underwater acoustic communication, the PMF-FFT output amplitude decays rapidly with the increase of Doppler frequency offset, and the lower the carrier frequency, the more severe the attenuation.

[0021] Here, common windowing and zero-padding techniques are used to improve the amplitude-frequency response of PMF-FFT under a 7kHz carrier. Specifically, a Hanning window is used for windowing, and 2x M-point zero-padding is employed. It can be seen that windowing and zero-padding only reduce the scallop loss effect; however, the trend of the amplitude rapidly decreasing with Doppler is not curbed. In summary, PMF-FFT has applicability issues in the acquisition of underwater acoustic PN synchronization signals, requiring applicability modifications. The cause of this phenomenon is attributed to code alignment misalignment resulting from waveform distortion of the PN signal under Doppler conditions. In underwater acoustic communication, the carrier frequency is low, and the sound propagation speed is slow. Assuming the seawater acoustic propagation speed is 1500m / s, a relative movement within 3 knots can generate 10... -3 The Doppler factor, for a PN code with 2047 chips, will cause a misalignment of more than 2 chips, making the assumption of ignoring waveform deformation in equations (6) to (7) no longer valid.

[0022] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0023] (1) Spectrum efficiency problem: In some cases, the traditional PMF-FFT algorithm is not efficient enough. If the use of spectrum is not optimized, it will lead to energy waste and reduced communication efficiency.

[0024] (2) Impact of environmental factors: Underwater acoustic communication is affected by various environmental factors (such as temperature, salinity, water depth, etc.), which can affect the performance of the PMF-FFT algorithm. The current algorithm does not have enough robustness to cope with these changes.

[0025] (3) Low signal-to-noise ratio: In underwater environments, the signal-to-noise ratio is usually low due to noise and signal propagation loss. This affects the performance and accuracy of the PMF-FFT algorithm.

[0026] (4) Algorithm complexity: The traditional PMF-FFT algorithm is too complex in some cases and requires a lot of computing resources. This will limit practical applications, especially in scenarios with limited resources or where fast processing is required.

[0027] Therefore, the key technical issues that urgently need to be addressed include how to improve the adaptability and efficiency of the PMF-FFT algorithm in Doppler underwater acoustic PN signal acquisition, how to improve its robustness to environmental changes, and how to reduce the complexity of the algorithm to adapt to more practical application scenarios. Summary of the Invention

[0028] To address the problems existing in the prior art, the present invention provides a method, system and device for capturing low signal-to-noise ratio Doppler underwater acoustic pseudo-random signals.

[0029] This invention is implemented as follows: a method for acquiring low signal-to-noise ratio (SNR) Doppler underwater acoustic pseudo-random signals. The method, based on a partial reconstruction-waveform compression algorithm, considers coarse temporal synchronization complete when the peak value of the FFT output spectrum exceeds a peak-to-average ratio (PAR) threshold. To address the Doppler estimation ambiguity problem after waveform compression, a secondary resident serial search is proposed to achieve Doppler estimation. Specifically, interpolated waveforms at frequency offsets of -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz are used as local PN signals and introduced into the traditional PMF-FFT. When the peak value of the FFT spectrum exceeds the threshold, the corresponding frequency offset value is the Doppler estimate.

[0030] Furthermore, including:

[0031] Partial reconstruction is achieved by overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor. The PN chips in the specified tail region are overlapped and averaged, and replaced with new PN chips. The PN code in the head region remains unchanged, and a new PN code is obtained.

[0032] After partially reconstructing the local PN signal, multiple Doppler nodes are taken at a certain frequency offset step size interval, and baseband waveform interpolation is performed according to the Doppler factor to form multiple Doppler deformation PN waveform signals. Then, uncorrelated accumulation is performed to complete the local waveform compression.

[0033] The partial reconstruction of the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method is achieved through overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor (O), where k·O is the overlapping averaging length. The PN chips in the specified tail region are overlapped and averaged according to equation (8) and replaced with new PN chips. The PN chips in the head region remain unchanged, resulting in a new PN code.

[0034]

[0035] Where R is the starting chip position of the PN tail reconstruction. The new PN code is reduced by (0-1) / 2 chips compared to the original code, and zeros are padded at the corresponding chip positions. The reconstructed PN code is repeatedly sampled k times and filtered to obtain a new baseband PN signal. It is divided into M groups and partially correlated with the received signal to complete the subsequent PMF-FFT process.

[0036] Furthermore, when the overlap factor is 3, the reconstruction can achieve full chip effective correlation within one chip of Doppler compression deformation when the PN sequence head is aligned with the spread spectrum code reconstruction with an overlap factor of 3. The reconstruction range is half of the tail of the PN signal.

[0037] Furthermore, in the low signal-to-noise ratio (SNR) Doppler underwater acoustic pseudo-random signal acquisition method, the partial reconstruction Doppler tolerance half-span is 5Hz under a -15dB SNR signal-to-noise ratio condition. The waveform compression Doppler deformation interpolation step size is set to 10Hz. Doppler deformation interpolation is performed at -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz. The deformation is then incoherently accumulated to obtain the compressed waveform, which is then fed into the local PN signal delay line and subjected to subsequent PMF-FFT operations with the down-converted data of the received signal. The acquisition system based on the partial reconstruction-waveform compression algorithm considers coarse synchronization in the time domain complete when the peak value of the FFT output spectrum exceeds the peak-to-average ratio (PAPR) threshold.

[0038] Furthermore, to address the issue of Doppler estimation ambiguity after waveform compression, a secondary resident serial search is proposed to achieve Doppler estimation. This involves using interpolated waveforms at frequency offsets of -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz as local PN signals and introducing them into the traditional PMF-FFT. When the peak value of the FFT spectrum exceeds the threshold, the corresponding frequency offset value is the Doppler estimate.

[0039] Another object of the present invention is to provide a low signal-to-noise ratio (SNR) Doppler underwater acoustic pseudo-random signal acquisition system based on the aforementioned low SNR Doppler underwater acoustic pseudo-random signal acquisition method, the low SNR Doppler underwater acoustic pseudo-random signal acquisition system comprising:

[0040] The partial reconstruction module is used to achieve partial reconstruction by overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor. The PN chips in the specified tail region are overlapped and averaged, and replaced with new PN chips. The PN code in the head region remains unchanged, and a new PN code is obtained.

[0041] The waveform compression module is used to partially reconstruct the local PN signal, take multiple Doppler nodes at a certain frequency offset step size interval, and perform baseband waveform interpolation according to the Doppler factor to form multiple Doppler deformed PN waveform signals. Then, it performs uncorrelated accumulation to complete the local waveform compression.

[0042] The Doppler estimation module is used to solve the problem of Doppler estimation ambiguity after waveform compression by proposing a secondary resident serial search to achieve Doppler estimation.

[0043] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0044] First, this invention proposes to reconstruct the local baseband PN signal to reduce the impact of Doppler deformation on the PMF correlation process. Simultaneously, considering the cumulative effect of deformation on the PN signal acquisition process, a partial reconstruction method targeting the tail of the PN signal is proposed to ensure the strength of the PMF output signal, thus expanding the applicability of PMF-FFT in underwater Doppler environments. Furthermore, the local PN baseband signal is subjected to multi-Doppler step-size waveform interpolation to form multiple compressed and expanded basic waveforms. These are then compressed through incoherent superposition, thereby significantly increasing the frequency domain parallel acquisition bandwidth of PMF-FFT in underwater Doppler environments.

[0045] This invention addresses the severe gain attenuation problem caused by waveform deformation in traditional PMF-FFT algorithms for underwater acoustic pseudo-random signal acquisition by mobile nodes in near-shore Doppler and low signal-to-noise ratio (SNR) environments. It proposes an improved PMF-FFT algorithm based on partial reconstruction and waveform compression, which is more suitable for underwater acoustic applications with low SNR and Doppler backgrounds. Experimental results show that the proposed PMF-FFT pseudo-random acquisition method significantly improves the acquisition performance of traditional PMF-FFT methods and has strong practicality.

[0046] Secondly, addressing the severe output amplitude attenuation caused by Doppler deformation in the traditional PMF-FFT pseudo-random signal frequency domain parallel acquisition algorithm for underwater acoustic communication synchronization head acquisition applications, a two-step solution of partial reconstruction and waveform compression is proposed: First, based on the traditional PMF-FFT algorithm, the local spread-spectrum pseudo-random (PN) signal is partially reconstructed to extend the Doppler tolerance of the PN acquisition system. Simultaneously, only partial PN code reconstruction is performed, without full code reconstruction, ensuring applicability to harsh underwater communication environments under low signal-to-noise ratio conditions. Second, based on the partial reconstruction Doppler tolerance analysis, a reasonable step size for the local PN signal deformation interpolation Doppler frequency offset is set. Interpolation deformation and non-coherent accumulation operations are performed on the local PN signal at the corresponding frequency offset points before proceeding to subsequent PMF-FFT calculations. Third, regarding Doppler estimation, a secondary resident serial search method is proposed to address the problem of fuzzy Doppler estimation after waveform compression, achieving Doppler estimation and providing a solution for time-domain acquisition and frequency-domain synchronization of PN synchronization signals in low signal-to-noise ratio, high-Doppler underwater acoustic communication.

[0047] Third, the technical solution of this invention fills a technical gap in the industry at home and abroad: This invention is the first to realize the adaptive improvement of the PMF-FFT spread spectrum synchronization head acquisition algorithm in the synchronization head acquisition of underwater acoustic communication in the Doppler background, so that the PMF-FFT is truly applicable to the underwater acoustic communication head acquisition process between underwater mobile platforms.

[0048] Does the technical solution of this invention overcome technical bias? Historically, underwater acoustic pseudo-random signal (PN) acquisition has drawn on the PMF-FFT algorithm used in deep space communication, assuming that PMF-FFT can effectively acquire underwater acoustic PN signals. However, simulations and experiments show that the algorithm's adaptability decreases sharply in underwater Doppler environments. This invention improves the applicability of the PMF-FFT algorithm in underwater acoustic communication, enabling PMF-FFT to truly achieve acquisition efficiency in spread spectrum communication head acquisition for underwater mobile platforms.

[0049] Fourth, the significant technological advancements in each step of the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method are as follows:

[0050] 1. Partial Reconstruction Step: In this step, the PN code chip in the specified tail region is replaced with a new PN code chip by overlapping averaging, while the PN code in the head region remains unchanged. This achieves partial reconstruction of the PN code, effectively reducing the impact of the Doppler factor on the PN code and further improving the signal acquisition performance in the context of Doppler.

[0051] 2. Waveform Compression Step: In this step, multiple Doppler nodes are used for baseband waveform interpolation to form multiple Doppler-deformed PN waveform signals. Then, uncorrelated accumulation is performed to complete local waveform compression. This reduces the impact of the Doppler factor on signal acquisition performance and improves the algorithm's robustness.

[0052] 3. Secondary Resident Serial Search Step: After waveform compression, Doppler estimation may become blurred. A secondary resident serial search step can achieve more accurate Doppler estimation, thereby improving signal acquisition performance.

[0053] 4. Tail reconstruction and overlap factor selection steps: In this step, by selecting an appropriate overlap factor and reconstructing the tail of the PN signal, the signal acquisition performance in the Doppler environment can be further improved.

[0054] 5. Interpolation step size selection step: In this step, by selecting an appropriate interpolation step size, a more accurate Doppler deformation interpolation waveform can be obtained, thereby further improving signal acquisition performance.

[0055] 6. Threshold setting step: In this step, by setting an appropriate threshold, when the peak value of the FFT spectrum exceeds the threshold, the Doppler value can be estimated more accurately, thereby improving the accuracy of signal acquisition.

[0056] In summary, this method effectively improves the acquisition performance of underwater acoustic pseudo-random signals in low signal-to-noise ratio and Doppler environments through steps such as partial reconstruction of the PN code, waveform compression, and accurate Doppler estimation. Attached Figure Description

[0057] Figure 1 The present invention provides a method, system, and device for acquiring low signal-to-noise ratio Doppler underwater acoustic pseudo-random signals.

[0058] Figure 2 This is a schematic diagram of the waveform deformation caused by Doppler and its effect on PMF-FFT provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of local PN signal reconstruction provided in an embodiment of the present invention (O=3);

[0060] Figure 4 This is a flowchart of the PMF-FFT capture method based on partial correlation-waveform compression provided in the embodiments of the present invention;

[0061] Figure 5 This is the PMF-FFT amplitude-frequency response diagram based on partial reconstruction-waveform compression provided in this embodiment of the invention;

[0062] Figure 6 This is a map showing the location of the sea trial provided in an embodiment of the present invention;

[0063] Figure 7 This is a comparison chart of the detection accuracy of the experimental PN signal after adding noise, provided in an embodiment of the present invention.

[0064] Figure 8 This is a comparison chart of the secondary stationary Doppler estimation results and the GNSS observation conversion results provided in the embodiments of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] like Figure 1 As shown in the figure, the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method provided in this embodiment of the invention includes the following steps:

[0067] S101: Partial reconstruction is achieved by overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor. The PN chips in the specified tail region are overlapped and averaged, and replaced with new PN chips. The PN code in the head region remains unchanged, and a new PN code is obtained.

[0068] S102: After partially reconstructing the local PN signal, take multiple Doppler nodes at a certain frequency offset step size interval, and perform baseband waveform interpolation according to the Doppler factor to form multiple Doppler deformation PN waveform signals. Then perform uncorrelated accumulation to complete the local waveform compression.

[0069] S103: When the capture system based on the partial reconstruction-waveform compression algorithm exceeds the peak-to-average power ratio threshold of the FFT output spectrum, it is considered to have completed coarse synchronization in the time domain.

[0070] S104: To address the problem of Doppler estimation ambiguity after waveform compression, a secondary dwelling serial search is proposed to achieve Doppler estimation.

[0071] The following are the specific implementation plans for each step:

[0072] Step S101:

[0073] Partial reconstruction is achieved through overlapping averaging. Specifically, the PN chips in the specified tail region are first averaged, typically by calculating the average of consecutive, overlapping chip blocks. The number of chips involved in the overlapping average is set as the overlap factor. This average is then used to replace the original tail chips, forming a new PN code. The PN chips in the head region remain unchanged, resulting in a new PN code.

[0074] Step S102:

[0075] After partially reconstructing the local PN signal, the next step of processing is performed. Multiple Doppler nodes are selected at certain frequency offset step intervals. Based on the Doppler factor of each node, baseband waveform interpolation is performed to form multiple Doppler-deformed PN waveform signals. Then, uncorrelated accumulation is performed to complete the compression of the local waveform.

[0076] Step S103:

[0077] When the peak value of the FFT output spectrum exceeds a preset peak-to-average power ratio (PAPR) threshold, we consider coarse synchronization in the time domain to be complete. This is achieved using a capture system employing a partial reconstruction-waveform compression algorithm. Once this condition is met, we can proceed to the next step of processing.

[0078] Step S104:

[0079] To address the problem of Doppler estimation ambiguity after waveform compression, we propose a two-stage dwell-sequential search method. Specifically, this method involves a sequential search within the Doppler frequency offset range to find the optimal Doppler frequency offset value, thereby achieving Doppler estimation.

[0080] The above implementation schemes cover a variety of key tasks in signal processing, including signal reconstruction, frequency offset processing, interpolation, noncorrelation accumulation, synchronization, and search. All of these require careful handling and adaptation to actual environmental and equipment conditions in practice.

[0081] Example 1:

[0082] The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method provided in this embodiment of the invention includes the following steps:

[0083] 1. Partial Restructuring

[0084] Partial reconstruction is achieved through overlap averaging. The number of chips involved in the overlap averaging (usually an odd number) is set as the overlap factor (O), where k·O is the overlap averaging length. The PN chips in the specified tail region are overlapped and averaged according to equation (8) and replaced with new PN chips. The PN code in the head region remains unchanged, resulting in a new PN code:

[0085]

[0086] Where R is the starting chip position of the PN tail reconstruction, the new PN code is (0-1) / 2 chips smaller than the original, and zeros are padded at the corresponding chip positions. The reconstructed PN code is repeatedly sampled k times and filtered to obtain a new baseband PN signal, which is divided into M groups and partially correlated with the received signal to complete the subsequent PMF-FFT process.

[0087] The reconstruction diagram when the overlap factor is 3 is shown below. Figure 3As shown, when using a spreading code with an overlap factor of 3 for reconstruction, the reconstructed code can achieve effective correlation of the entire code within one chip of Doppler compression deformation when the PN sequence head is aligned (time-domain synchronization), thus improving the resistance to Doppler deformation. However, the PMF correlation gain attenuation deepens with the increase of the overlap factor during the overlap averaging process, thereby affecting the amplitude of the reconstructed PMF-FFT. Therefore, in order to reasonably determine the reconstruction range, reconstruction factor, and other parameters, this invention, through a series of simulation analyses, finally determined that the reconstruction range is half of the tail of the PN signal, and the overlap factor is 3.

[0088] 2. Waveform compression

[0089] The flowchart of the PMF-FFT capture method based on waveform compression and partial reconstruction proposed in this invention is as follows: Figure 4 As shown in the figure, this diagram illustrates waveform compression methods. (For example, regarding...) Figure 2 To address the chip misalignment problem during the alignment of the local waveform with the received signal that has undergone compression and expansion deformation due to Doppler influence, this invention partially reconstructs the local PN signal, takes multiple Doppler nodes at a certain frequency offset step size, and performs baseband waveform interpolation based on the Doppler factor to form multiple Doppler-deformed PN waveform signals. Then, it performs uncorrelated accumulation to complete the local waveform compression.

[0090] Through simulation analysis, under the parameter settings of this invention, the partial reconstruction Doppler tolerance half-span is 5Hz under a signal-to-noise ratio of -15dB. Therefore, the waveform compression Doppler deformation interpolation step size is set to 10Hz, and Doppler deformation interpolation is performed at -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz. The deformation is then incoherently accumulated to obtain the compressed waveform, which is then fed into the local PN signal delay line and subjected to subsequent PMF-FFT operations with the down-converted data of the received signal. Based on the above method, the FFT normalized amplitude-frequency response of the compressed waveform obtained in this invention is as follows: Figure 5 It can be seen that by waveform compression, the PMF-FFT Doppler based on partial correlation-waveform compression is extended to -25 to 25 Hz. At a sound speed of 1500 m / s, the applicable relative speed range is -10 to 10 knots (negative values ​​indicate moving away in the opposite direction), which can basically meet the control and communication needs of existing underwater unmanned platforms.

[0091] 3. Secondary residence serial Doppler estimation

[0092] To address the issue of Doppler estimation ambiguity after waveform compression, a secondary dwelling serial search method is proposed to achieve Doppler estimation. This provides a solution for frequency domain synchronization of PN synchronization signals in low signal-to-noise ratio, high-Doppler underwater acoustic communication. The specific method is as follows: Figure 4As shown, the acquisition system based on the partial reconstruction-waveform compression algorithm considers coarse synchronization in the time domain to be complete when the peak value of the FFT output spectrum exceeds the peak-to-average ratio threshold. At this time, the interpolated waveforms of each frequency offset at -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz are used as local PN signals and introduced into the traditional PMF-FFT. When the peak value of the FFT spectrum exceeds the threshold, the corresponding frequency offset value is the Doppler estimate.

[0093] The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition system provided in this embodiment of the invention includes:

[0094] The partial reconstruction module is used to achieve partial reconstruction by overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor. The PN chips in the specified tail region are overlapped and averaged, and replaced with new PN chips. The PN code in the head region remains unchanged, and a new PN code is obtained.

[0095] The waveform compression module is used to partially reconstruct the local PN signal, take multiple Doppler nodes at a certain frequency offset step size interval, and perform baseband waveform interpolation according to the Doppler factor to form multiple Doppler deformed PN waveform signals. Then, it performs uncorrelated accumulation to complete the local waveform compression.

[0096] The Doppler estimation module addresses the problem of Doppler estimation ambiguity after waveform compression by proposing a secondary resident serial search to achieve Doppler estimation. The acquisition system based on the partial reconstruction-waveform compression algorithm considers the completion of coarse synchronization in the time domain when the peak value of the FFT output spectrum exceeds the peak-to-average ratio threshold.

[0097] The embodiments of the present invention have achieved some positive results during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0098] Sea trials and verification

[0099] A PN synchronization signal acquisition test was conducted on March 7, 2023, in Laoshan Bay, China. The test location is as follows: Figure 6 As shown in the diagram, the color-changing curve represents the movement path of the signal transmitting ship, and the color scale indicates the radial speed, calculated based on the triangular relationship between the ship's speed and the positions of the transmitting and receiving ships in the shipborne BeiDou system. The ship moves away from the signal receiving ship, and its trajectory involves initial acceleration followed by deceleration, with a maximum relative speed exceeding 5 knots. The transmission and reception distance increased from 4.6 km to 5.0 km. The test sea area had a water depth of 15-20 meters. Except for the waters near islands and reefs, the terrain was generally flat, and the seabed was muddy.

[0100] The experiment transmitted 30 frames of PN synchronization signals, each frame containing 6 groups of PN signals for acquisition verification. The signal-to-noise ratio (SNR) conditions were poor for the first 12 frames, with an average SNR of -4.7 dB, while the SNR conditions were better for the last 18 frames, with an average SNR of 2.1 dB. The traditional PMF-FFT method, the PMF-FFT method based solely on partial reconstruction, and the PMF-FFT method based on partial reconstruction and waveform compression were used to acquire the PN signals, yielding acquisition probabilities of 0.8556, 1.0000, and 1.0000, respectively.

[0101] It can be seen that, due to the improvement of the Doppler tolerance capability of the algorithm, the algorithm proposed in this invention has significantly improved the detection capability in the background of Doppler compared with the traditional algorithm. The PMF-FFT algorithm based on partial reconstruction-waveform compression achieves the detection of all PN signals. Since the background Doppler in the experiment is weak, the PMF-FFT method based on partial reconstruction alone has also achieved 100% detection effect, as can be seen from the direct experimental results.

[0102] To examine the detection performance under different signal-to-noise ratio (SNR) conditions, the sea trial sampling data was noise-added (with the experimental SNR set to 0 dB). The detection accuracy of different algorithms under different SNR conditions is shown below. Figure 7 As shown in the figure, C-PMF-FFT represents the traditional PMF-FFT algorithm, R-PMF-FFT represents the PMF-FFT algorithm based on partial reconstruction, and PMF-FFT based on partial correlation-waveform compression represents the PMF-FFT algorithm based on partial reconstruction-waveform compression. It can be seen that the PMF-FFT algorithm based on partial reconstruction-waveform compression proposed in this invention can still achieve 100% effective detection of PN signals under -9dB conditions.

[0103] Based on the second-stage stationary Doppler serial estimation process, the Doppler estimates of the improved PMF-FFT method are obtained. Assuming the sound velocity of seawater is 1466 m / s, the radial velocity estimate is obtained, as follows: Figure 8 As shown, the radial velocity control group was calculated based on the GNSS moving speed of the signal transmitting ship using position triangulation. It can be seen that the PMF-FFT Doppler estimation results based on partial correlation-waveform compression are basically consistent with the changing trends of GNSS observations, indicating that the Doppler estimation results are effective.

[0104] In conclusion, the results of the sea trials have confirmed the effectiveness of this method.

[0105] Figure 2The opposing movement of the transceiver platforms causes the PN code to compress and deform by one chip width. It can be seen that even with time-domain alignment of the PN synchronization signal (time-domain synchronization), the Doppler-deformed PN chip misaligns with the local baseband PN chip, reducing the effective correlation region that can be aligned, causing PMF output attenuation, and consequently affecting the PMF-FFT result gain. Furthermore, due to the cumulative effect of Doppler-induced deformation on chip misalignment, the effective correlation region caused by chip misalignment decreases sequentially from front to back, until the last chip is completely misaligned, no longer producing beneficial PMF output.

[0106] The following are four specific implementation examples and their corresponding solutions:

[0107] Example 1:

[0108] In underwater acoustic communication systems, the PN code chips in the tail region are first overlapped and averaged using an overlap averaging method, replacing them with new PN code chips. The PN code in the head region remains unchanged, resulting in a new PN code. This embodiment can be implemented using a programming language (such as Python or C++), utilizing array or list operations for overlap averaging and replacement operations.

[0109] Example 2:

[0110] After partially reconstructing the local PN signal, multiple Doppler nodes are selected at certain frequency offset step intervals, and baseband waveform interpolation is performed based on the Doppler factor to form multiple Doppler-deformed PN waveform signals. Then, uncorrelated accumulation is performed to complete local waveform compression. This embodiment can utilize signal processing libraries (such as Python's NumPy and SciPy libraries or MATLAB's Signal Processing Toolbox) to select the frequency offset step, interpolate the baseband waveform, and compress the waveform.

[0111] Example 3:

[0112] To address the issue of blurred Doppler estimation after waveform compression, a secondary dwell-sequential search can be implemented to achieve Doppler estimation. This step can be accomplished using data analysis tools (such as Python's Pandas library or the R language) for data searching and processing.

[0113] Example 4:

[0114] Time-domain coarse synchronization is considered complete when the peak value of the FFT output spectrum exceeds the peak-to-average ratio (PAR) threshold. This step can be implemented using Fourier transform-related libraries, such as Python's NumPy library or MATLAB, by analyzing and comparing the FFT output spectrum to determine whether the preset PAR threshold has been reached.

[0115] All of these embodiments can be implemented through programming, requiring a certain level of signal processing and programming knowledge. Alternatively, these steps can be integrated into an algorithm or model to complete all the above steps in a single process.

[0116] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0117] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for acquiring low signal-to-noise ratio Doppler underwater acoustic pseudo-random signals, characterized in that, The capture method based on partial reconstruction-waveform compression algorithm considers coarse synchronization in the time domain to be complete when the peak value of the FFT output spectrum exceeds the peak-to-average ratio threshold. To address the Doppler estimation ambiguity problem after waveform compression, a secondary resident serial search is proposed to achieve Doppler estimation. Specifically, the interpolated waveforms at frequency offsets of -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz are used as local PN signals and introduced into the traditional PMF-FFT. When the peak value of the FFT spectrum exceeds the threshold, the corresponding frequency offset value is the Doppler estimate. This includes: achieving partial reconstruction through overlapping averaging, setting the number of chips involved in the overlapping averaging as the overlap factor, and specifying the tail... The PN chips in the region are overlapped and averaged to replace the new PN chips, while the PN code in the head region remains unchanged, resulting in a new PN code. After partially reconstructing the local PN signal, multiple Doppler nodes are taken at certain frequency offset step intervals, and baseband waveform interpolation is performed based on the Doppler factor to form multiple Doppler-deformed PN waveform signals. Then, uncorrelated accumulation is performed to complete the local waveform compression. To address the problem of Doppler estimation ambiguity after waveform compression, a secondary resident serial search is proposed to achieve Doppler estimation. The acquisition system based on the partial reconstruction-waveform compression algorithm considers the completion of coarse synchronization in the time domain when the peak value of the FFT output spectrum exceeds the peak-to-average ratio threshold.

2. The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method as described in claim 1, characterized in that, The partial reconstruction of the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method is achieved by overlapping averaging, with the number of chips involved in the overlapping averaging set as the overlap factor (O). That is, the overlap average length is obtained by averaging the overlap of the PN code chips in the specified tail region according to equation (8), replacing them with new PN code chips, while the PN code in the head region remains unchanged, resulting in a new PN code: Where R is the starting chip position for PN tail reconstruction, and the new PN code is reduced by [number] compared to the original code. Each chip is padded with zeros at its corresponding position. The reconstructed PN code is repeatedly sampled k times and filtered to obtain a new baseband PN signal. This signal is divided into M groups and partially correlated with the received signal, and then the subsequent PMF-FFT process is completed. When the overlap factor is 3, the reconstructed code can achieve full chip effective correlation within one chip of Doppler compression deformation when the PN sequence head is aligned. The reconstruction range is half of the tail of the PN signal, and the overlap factor is 3.

3. The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method as described in claim 1, characterized in that, The low signal-to-noise ratio (SNR) Doppler underwater acoustic pseudo-random signal acquisition method has a partial reconstruction Doppler tolerance half-span of 5Hz under a waveform compression SNR of -15dB. The waveform compression Doppler deformation interpolation step size is set to 10Hz. Doppler deformation interpolation is performed at -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz. Then, the deformation is incoherently accumulated to obtain the compressed waveform, which is then fed into the local PN signal delay line and subjected to subsequent PMF-FFT operations with the down-converted data of the received signal.

4. The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method as described in claim 1, characterized in that, The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method uses the interpolated waveforms of each frequency offset at -20Hz, -10Hz, 0Hz, 10Hz, and 20Hz as local PN signals and introduces them into PMF-FFT respectively. When the peak value of the FFT spectrum exceeds the threshold, the corresponding frequency offset value is the Doppler estimate.

5. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method according to any one of claims 1 to 4.

6. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method according to any one of claims 1 to 4.

7. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition method according to any one of claims 1 to 4.

8. A low signal-to-noise ratio (SNR) Doppler underwater acoustic pseudo-random signal acquisition system based on the low SNR Doppler underwater acoustic pseudo-random signal acquisition method according to any one of claims 1 to 4, characterized in that, The low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition system includes: The partial reconstruction module is used to achieve partial reconstruction by overlapping averaging. The number of chips involved in the overlapping averaging is set as the overlap factor. The PN chips in the specified tail region are overlapped and averaged, and replaced with new PN chips. The PN code in the head region remains unchanged, and a new PN code is obtained. The waveform compression module is used to partially reconstruct the local PN signal, take multiple Doppler nodes at a certain frequency offset step size interval, and perform baseband waveform interpolation according to the Doppler factor to form multiple Doppler deformed PN waveform signals. Then, it performs uncorrelated accumulation to complete the local waveform compression. The Doppler estimation module addresses the issue of Doppler estimation ambiguity after waveform compression by proposing a secondary resident serial search to achieve Doppler estimation. The acquisition system based on the partial reconstruction-waveform compression algorithm considers coarse synchronization in the time domain to be complete when the peak value of the FFT output spectrum exceeds the peak-to-average ratio threshold.

9. A low signal-to-noise ratio and Doppler background underwater acoustic application terminal, characterized in that, The low signal-to-noise ratio and Doppler background underwater acoustic application terminal uses the low signal-to-noise ratio Doppler underwater acoustic pseudo-random signal acquisition system as described in claim 8.

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