A TDOA refinement method, device and equipment of AIS signal and storage medium

Abstract

The application provides an AIS signal TDOA refining method, device, equipment and storage medium, two channel AIS signal data are acquired first, and the two channel AIS signal data are pretreated, then, based on the pretreated signal data, time domain feature extraction, frequency domain feature extraction and carrier phase extraction are performed in parallel, wherein the time domain feature extraction obtains time domain TDOA measurement values through FFT accelerated cross-correlation calculation and sub-sampling accuracy interpolation algorithm, the frequency domain feature extraction obtains frequency domain TDOA measurement values through cross-power spectrum phase analysis and weighted least square fitting, and the carrier phase extraction obtains carrier phase TDOA measurement values through baseband down-conversion processing and integer ambiguity automatic solution; finally, the time domain TDOA measurement values, the frequency domain TDOA measurement values and the carrier phase TDOA measurement values are subjected to consistency cross-validation through a multi-domain information fusion module, adaptive weight distribution is performed based on confidence evaluation, and an optimal TDOA measurement result is output. The problem that the traditional TDOA measurement progress is insufficient under a complex electromagnetic environment and cannot meet the demand of high-precision positioning of AIS signals is solved.

Description

Technical Field

[0001] This invention relates to the field of AIS, and in particular to a method, apparatus, device and storage medium for TDOA refinement of AIS signals. Background Technology

[0002] Automatic Identification Systems (AIS) are widely used in maritime navigation, traffic monitoring, and situational awareness. One of their core technologies is positioning based on the time difference of arrival (TDOA) of multiple receiving nodes. Traditional TDOA measurement methods are mostly based on single-domain features, such as time-domain cross-correlation, frequency-domain phase difference, or carrier phase. However, in complex electromagnetic environments, single-domain methods are easily affected by noise interference or ambiguity, resulting in insufficient positioning accuracy and stability.

[0003] For example, time-domain cross-correlation methods are highly dependent on sampling rate and peak detection accuracy, frequency-domain methods are susceptible to phase jump interference, and while carrier phase methods are highly accurate, they are difficult to solve integer ambiguity problems and rely on additional prior information.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] This invention discloses a method, apparatus, device, and storage medium for refining AIS signals using TDOA, aiming to solve the problem that traditional TDOA measurement is insufficient in complex electromagnetic environments and cannot meet the high-precision positioning requirements of AIS signals.

[0006] The first embodiment of the present invention provides a method for refining the TDOA of AIS signals, comprising: Acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels; Based on the preprocessed signal data, time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction are performed in parallel. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. The time-domain TDOA measurement value, frequency-domain TDOA measurement value, and carrier phase TDOA measurement value are cross-validated for consistency by a multi-domain information fusion module. Based on the confidence assessment, adaptive weight allocation is performed to output the optimal TDOA measurement result.

[0007] Preferably, the temporal feature extraction obtains the temporal TDOA measurement value through FFT-accelerated cross-correlation calculation and subsampling precision interpolation algorithm, specifically as follows: The cross-correlation function R of the two signals is calculated by accelerating the cross-correlation calculation using FFT. 12 [m], the expression for the cross-correlation function is: R 12 [m]=IFFT(S1(k)×S2 ∗ (k)) Where S1(k) and S2(k) are the FFT transformation results of the two signals, respectively; A rough TDOA is obtained through peak detection, and its expression is: τ_coarse=(max_idx-N / 2)×Ts, where max_idx is the peak position of the cross-correlation function, N is the FFT length, and Ts is the sampling period; Subsampling accuracy is achieved using parabolic interpolation, with the subsampling correction factor being: δ=0.5×(y) −1 -y1) / (y −1 −2y0+y1) Where y −1 y0 and y1 are the amplitudes of three sample points near the peak value. The time-domain TDOA measurement value is calculated based on the subsampling correction: τ_time=(max_idx+δ-N / 2)×Ts.

[0008] Preferably, the frequency domain feature extraction obtains the frequency domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting, specifically as follows: Calculate the cross power spectrum S of the two signals. 12 (ω)=S1(ω)×S2 * (ω), extract the phase spectrum φ(ω)=arg{S 12 (ω)}, where S1(ω) and S2(ω) are the frequency domain representations of the two signals, respectively; The phase spectrum is unwrapped to eliminate 2π jumps, resulting in a continuous phase spectrum φ_unwrap(ω). Using a weighted least squares fitting method, based on the linear relationship between phase spectrum and frequency φ_unwrap(ω)=-ω×τ+φ0, where ω is the angular frequency, τ is the time delay parameter, and φ0 is the initial phase offset, the frequency domain TDOA measurement value is solved: τ_freq=-[Σw(ω k )ω k φ_unwrap(ω k )×Σw(ω k )-Σw(ω k )ω k ×Σw(ω k )φ_unwrap(ω k )] / [Σw(ωk )ω k 2 ×Σw(ω k )-(Σw(ω k )ω k ) 2 ] Where w(ω) k ) is the weighting function determined based on the cross-power spectrum amplitude, ω k Let be the angular frequency of the k-th frequency point.

[0009] Preferably, the carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution, specifically as follows: via local carrier LO(t) = exp(-j2πf c t) Perform baseband down-conversion on the two signals to obtain baseband signals baseband1(t) and baseband2(t), where f c The AIS carrier frequency is 160MHz; Extract the instantaneous phases φ1(t)=arg{baseband1(t)} and φ2(t)=arg{baseband2(t)}, and calculate the phase difference Δφ(t)=φ2(t)-φ1(t), where φ1(t) and φ2(t) are the instantaneous phases of signal 1 and signal 2, respectively; The average phase difference is obtained by averaging in the time domain: Δφ_avg=arg{mean(exp(j×Δφ(t)))}, and the original carrier phase TDOA is obtained: τ_carrier_raw=Δφ_avg / (2πf c ); Automatic integer ambiguity resolution is employed, using coarse time-domain measurement results as constraints to determine the optimal ambiguity: n_optimal = argmin{|τ_carrier(n) - τ_coarse|}, where τ_carrier(n) = τ_carrier_raw + n / f c , where τ_carrier(n) is the carrier phase of the nth ambiguity, and τ_carrier_raw is the original carrier phase TDOA measurement value without ambiguity correction; The final carrier phase TDOA measurement value is: τ_carrier=τ_carrier_raw+n_optimal / f c .

[0010] Preferably, the step of performing consistency cross-validation on the time-domain TDOA measurement value, frequency-domain TDOA measurement value, and carrier phase TDOA measurement value through a multi-domain information fusion module, and performing adaptive weight allocation based on confidence assessment to output the optimal TDOA measurement result is as follows: Calculate the confidence scores `confidence_time`, `confidence_freq`, and `confidence_carrier` in the time domain, frequency domain, and carrier phase domain, respectively. The time-domain confidence score is calculated based on the correlation peak sharpness, signal-to-noise ratio estimation, and interpolation confidence. The frequency-domain confidence score is based on the goodness-of-fit R². 2 The carrier confidence is calculated based on phase coherence and ambiguity confidence, and the coherence coefficient is calculated accordingly. The pairwise consistency of the three-domain measurement results is calculated using consistency cross-validation: consistency_tf=exp(-|τ_time-τ_freq|×f s ) consistency_tp=exp(-|τ_time-τ_carrier|×f s ) consistency_fp=exp(-|τ_freq-τ_carrier|×f s ) Where, consistency_tf represents the consistency index between time-domain and frequency-domain measurement results, consistency_tp represents the consistency index between time-domain and carrier phase-domain measurement results, and consistency_fp represents the consistency index between frequency-domain and carrier phase-domain measurement results. s Indicates the sampling frequency; The fusion weights are calculated based on confidence and consistency: w_time=confidence_time·(consistency_tf+consistency_tp) / 2; w_freq=confidence_freq·(consistency_tf+consistency_fp) / 2; w_carrier=confidence_carrier·(consistency_tp+consistency_fp) / 2; The optimal TDOA measurement result is generated based on the fusion weights.

[0011] The second embodiment of the present invention provides a TDOA refinement device for AIS signals, comprising: A preprocessing unit is used to acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels. The feature extraction unit is used to perform time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction in parallel based on the preprocessed signal data. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. The result output unit is used to perform consistency cross-validation on the time-domain TDOA measurement value, frequency-domain TDOA measurement value, and carrier phase TDOA measurement value through the multi-domain information fusion module, perform adaptive weight allocation based on confidence assessment, and output the optimal TDOA measurement result.

[0012] The third embodiment of the present invention provides a TDOA refinement device for AIS signals, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the TDOA refinement method for AIS signals as described in any of the above embodiments.

[0013] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, which can be executed by a processor of the device in which the computer-readable storage medium is located, to implement the TDOA refinement method for an AIS signal as described in any one of the claims above.

[0014] This invention provides a method, apparatus, device, and storage medium for refining the TDOA (Transient Tolerance of Occurrence) of AIS signals. First, AIS signal data from two channels is acquired and preprocessed. Then, based on the preprocessed signal data, time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction are performed in parallel. Specifically, time-domain feature extraction uses FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithms to obtain time-domain TDOA measurements. Frequency-domain feature extraction uses cross-power spectrum phase analysis and weighted least squares fitting to obtain frequency-domain TDOA measurements. Carrier phase extraction uses baseband down-conversion processing and automatic integer ambiguity resolution to obtain carrier phase TDOA measurements. Finally, a multi-domain information fusion module performs consistency cross-validation on the time-domain TDOA measurements, frequency-domain TDOA measurements, and carrier phase TDOA measurements. Based on confidence level assessment, adaptive weight allocation is performed to output the optimal TDOA measurement result. This solves the problem of insufficient progress in traditional TDOA measurements under complex electromagnetic environments, which cannot meet the high-precision positioning requirements of AIS signals. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a TDOA refinement method for AIS signals provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a TDOA refinement device for AIS signals provided in the second embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] The terms "first" and "second" used in the embodiments are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein.

[0023] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] This invention discloses a method, apparatus, device, and storage medium for refining AIS signals using TDOA, aiming to solve the problem that traditional TDOA measurement is insufficient in complex electromagnetic environments and cannot meet the high-precision positioning requirements of AIS signals.

[0025] The first embodiment of the present invention provides a TDOA refinement method for AIS signals, which can be executed by a TDOA refinement device, specifically by one or more processors within the TDOA refinement device, to at least implement the following steps: S101, acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels; In this embodiment, the TDOA refinement device can be a terminal with data processing capabilities, such as a server, workstation, or desktop computer. The TDOA refinement device can be equipped with a corresponding operating system and application software, and the functions required in this embodiment can be realized through the combination of the operating system and application software.

[0026] In this embodiment, AIS signals from different antennas or receivers are simultaneously acquired using two independent analog-to-digital converters (ADCs). During data acquisition, a unified clock reference is used to strictly synchronize the two ADCs to avoid spurious delay errors caused by sampling clock deviations.

[0027] Furthermore, the amplitude and phase of the two signals are calibrated for consistency, and the gain difference and phase shift between the two channels are compensated using pre-stored calibration parameters. The system monitors the dynamic range and saturation state of the signal in real time, and automatically adjusts the gain or marks a saturation anomaly when the signal amplitude is detected to be close to the full scale of the ADC. Even further, the signal is bandpass filtered by a digital filter to remove interference signals outside the AIS band, and a ring buffer technique is used to manage the continuous data stream, ensuring data continuity during real-time processing.

[0028] S102, based on the preprocessed signal data, time-domain feature extraction, frequency-domain feature extraction and carrier phase extraction are performed in parallel. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. Specifically, in this embodiment, the preprocessed two AIS signals S1[n] and S2[n] are subjected to a Fast Fourier Transform (FFT) of length N=1024 points to obtain the frequency domain representations S1(k)=FFT(S1[n]) and S2(k)=FFT(S2[n]), where k is the frequency index. Utilizing the convolutional properties of FFT, the cross-power spectrum S1(k)×S2*(k) of the frequency domain representations of the two signals is calculated, and then an inverse Fast Fourier Transform is performed to obtain the cross-correlation function R. 12 [m]=IFFT(S1(k)×S2 ∗ (k)), this method will transform the traditional time-domain cross-correlation O(N) 2 The complexity is reduced to O(NlogN), significantly improving computational efficiency. The system searches for the peak position `max_idx` with the largest amplitude in the obtained cross-correlation function and calculates a coarse time difference using `τ_coarse=(max_idx-N / 2)×Ts`, where the offset of N / 2 is used to adjust the zero-delay position to the center of the array, and Ts is the sampling period. To overcome the limitation of sampling rate on measurement accuracy, the system uses a parabolic interpolation algorithm to perform sub-sampling precision processing near the cross-correlation peak, selecting the peak position and three adjacent sample points `y`. −1 =|R 12 [max_idx-1]|、y0=|R 12 [max_idx]|、y1=|R 12 [max_idx+1]|, the subsampling correction δ=0.5×(y) is determined by fitting a parabolic function and finding its extreme points. −1 -y1) / (y −1 The correction factor (−2y0+y1) reflects the offset of the true peak value relative to the sampling point. The final time-domain TDOA measurement value is calculated by τ_time=(max_idx+δ-N / 2)×Ts, achieving a high-precision time delay measurement that far exceeds the sampling accuracy, typically reaching a measurement accuracy of 0.1 nanoseconds.

[0029] In parallel, using the already calculated frequency domain representations of the two signals S1(ω) and S2(ω), the cross power spectrum S is calculated. 12 (ω)=S1(ω)×S2 *(ω) obtains the complex spectrum containing time delay information, where S2 * (ω) is the complex conjugate of S2(ω). The system then extracts the phase information of the cross-power spectrum φ(ω) = arg{S 12 The phase spectrum (ω) directly reflects the relationship between the phase difference between the two signals and the frequency. Due to the 2π periodic jump in the phase function, the system uses a phase unwrapping algorithm to process the original phase spectrum. By detecting the phase difference between adjacent frequency points and accumulating compensation by integer multiples of 2π, a continuous phase spectrum φ_unwrap(ω) is obtained. Based on the physical property that the phase is proportional to the frequency due to signal propagation delay, the unwrapped phase spectrum should satisfy the linear relationship φ_unwrap(ω)=-ω×τ+φ0, where τ is the time delay parameter to be determined, and φ0 is the initial phase offset. To improve fitting accuracy and suppress noise, the system uses a weighted least squares fitting method, with the weight function w(ω)... k Based on the amplitude of the cross power spectrum at each frequency point |S 12 (ω k )| 2 In the design, frequencies with stronger signal energy receive higher weights. By minimizing the sum of squared weighted errors, the system utilizes the analytical formula τ_freq=-[Σw(ω k )ω k φ_unwrap(ω k )×Σw(ω k )-Σw(ω k )ω k ×Σw(ω k )φ_unwrap(ω k )] / [Σw(ω k )ω k 2 ×Σw(ω k )-(Σw(ω k )ω k ) 2 The frequency domain TDOA measurement value can be directly solved, which can effectively utilize the phase information of the entire signal frequency band and provide a high-precision TDOA estimate independent of the time domain method when the phase unwrapping is successful.

[0030] In parallel, the system generates a local carrier signal LO(t) = exp(-j2πf) that is completely synchronized with the AIS signal carrier frequency fc = 160MHz. cThe local carrier is mixed with the two received signals to achieve baseband downconversion, resulting in baseband1(t) = S1(t) × LO(t) and baseband2(t) = S2(t) × LO(t). After low-pass filtering to remove high-frequency components generated by mixing, the system extracts the instantaneous phases φ1(t) = arg{baseband1(t)} and φ2(t) = arg{baseband2(t)} of the two baseband signals and calculates the instantaneous phase difference Δφ(t) = φ2(t) - φ1(t). To suppress the influence of noise on phase measurement, the system uses vector averaging to calculate the average phase difference Δφ_avg = arg{mean(exp(j×Δφ(t)))}. By converting the phase difference into a complex vector on the unit circle for averaging, the interference of phase jump on the arithmetic mean is effectively avoided. Based on the average phase difference, the system calculates the original carrier phase TDOA as τ_carrier_raw = Δφ_avg / (2πf c However, due to the integer ambiguity of 2π in phase measurements, the true carrier phase TDOA should be in the form τ_carrier(n) = τ_carrier_raw + n / fc, where n is the unknown integer ambiguity. The system uses the time-domain coarse measurement result τ_coarse as a constraint, automatically resolving the ambiguity by searching for the integer n that minimizes |τ_carrier(n) - τ_coarse|, i.e., n_optimal = argmin{|τ_carrier(n) - τ_coarse|}. This determines the correct integer number of cycles without manual intervention. The final carrier phase TDOA measurement value is obtained by using τ_carrier = τ_carrier_raw + n_optimal / fc. c This allows for ultra-high precision time delay measurement down to the sub-nanosecond level, with accuracy primarily limited by the stability of the carrier frequency and the level of phase noise.

[0031] S103, the time-domain TDOA measurement value, frequency-domain TDOA measurement value, and carrier phase TDOA measurement value are cross-validated for consistency through the multi-domain information fusion module, and adaptive weight allocation is performed based on confidence assessment to output the optimal TDOA measurement result.

[0032] The system first calculates the confidence index for each of the three domains. The time-domain confidence_time is calculated using the correlation peak sharpness = max_val / mean(|R). 12The confidence_time is calculated as a weighted combination of the signal-to-noise ratio estimation (SNR_time) and interpolation confidence (interp_confidence = 1 / (1+|δ|), where a1 = 0.4, a2 = 0.4, and a3 = 0.2. The frequency domain confidence_freq is based on the goodness-of-fit R... 2 =1 - combination of RSS / TSS and average coherence_avg: confidence_freq=b1×R 2 The formula is calculated as: +b2×coherence_avg+b3×min(1,SNR_freq / 15), where b1=0.5, b2=0.3, and b3=0.2. The carrier confidence_carrier is calculated using phase coherence = |mean(exp(j×Δφ(t)))| and ambiguity confidence = exp(-|τ_carrier-τ_coarse|×f c The confidence_carrier is obtained by combining γ1 × phase_coherence + γ2 × ambiguity_confidence, where γ1 = 0.6 and γ2 = 0.4. Consistency cross-validation is then performed, calculated... consistency_tf=exp(-|τ_time-τ_freq|×f s ) consistency_tp=exp(-|τ_time-τ_carrier|×f s ) consistency_fp=exp(-|τ_freq-τ_carrier|×f s ) The system obtains pairwise consistency indices for the three-domain measurement results, where an exponential decay function ensures that domain pairs with greater measurement differences receive lower consistency scores. Based on a dual evaluation of confidence and consistency, the system calculates fusion weights, specifically... First, normalize the confidence level: conf_sum=confidence_time+confidence_freq+confidence_carrier w_time_base=confidence_time / conf_sum w_freq_base=confidence_freq / conf_sum w_carrier_base=confidence_carrier / conf_sum Adjusting weights using consistency: w_time=w_time_base×(consistency_tf+consistency_tp) / 2 w_freq=w_freq_base×(consistency_tf+consistency_fp) / 2 w_carrier=w_carrier_base×(consistency_tp+consistency_fp) / 2 Weight renormalization: w_sum=w_time+w_freq+w_carrier w_time_final=w_time / w_sum w_freq_final=w_freq / w_sum w_carrier_final=w_carrier / w_sum The optimal TDOA measurement result is output by weighted average τ_final = w_time_final × τ_time + w_freq_final × τ_freq + w_carrier_final × τ_carrier. When overall_consistency = (consistency_tf + consistency_tp + consistency_fp) / 3 is less than 0.3, the system automatically switches to single-domain mode and uses only the measurement result with the highest confidence to avoid erroneous fusion.

[0033] In one possible implementation of the present invention: The system's quality monitoring and anomaly handling mechanism ensures the reliability of TDOA measurements through multi-level real-time detection and adaptive adjustment. It continuously monitors the signal quality indicators of each domain, and when the signal-to-noise ratio (SNR) is detected to be lower than the preset threshold `threshold_min`, it automatically reduces the confidence weight of the corresponding domain by introducing a penalty factor into the confidence calculation. The system implements a gradual weight decay using the formula `confidence_modified = confidence_original × exp(-(threshold_min - SNR) / threshold_min)`. Simultaneously, the system monitors the maximum correlation value `max_correlation` of the cross-correlation function. When this value falls below the threshold `threshold_corr`, the signal is marked as low-quality, triggering a quality warning mechanism. For outlier detection, the system employs a statistical detection method based on the 3σ criterion, calculating the median `median_tdoa` and standard deviation `σ_tdoa` of the three-domain TDOA measurements. When a measurement value in a certain domain satisfies |τ_i-median_tdoa|>3×σ_tdoa, it is marked as an outlier and removed from the fusion process; When the overall consistency index is: When overall_consistency = (consistency_tf + consistency_tp + consistency_fp) / 3 is less than 0.3, the system determines that there is a significant discrepancy in the multi-domain measurement results and automatically switches to single-domain working mode. Select: The domain corresponding to `confidence_max = max(confidence_time, confidence_freq, confidence_carrier)` is used as the unique output. The system calibration and compensation module performs real-time compensation for system errors between multiple channels using pre-stored calibration parameters, including amplitude calibration `s2_calibrated = (s2 / gain_ratio) × exp(-j × phase_offset)` and delay calibration `τ_compensated = τ_measured - system_delay`, where `gain_ratio` and `phase_offset` are obtained through periodic consistency tests. Temperature drift compensation uses a quadratic polynomial model `delay_drift(T) = a0 + a0 × (T - T0) + a2 × (T - T0)`. 2 The system predicts and compensates for system delay drift caused by temperature changes. The system calculates the compensation amount based on real-time temperature sensor data T_current and applies it to the final TDOA result to ensure stable measurement accuracy under temperature change environment.

[0034] In one possible implementation of the present invention: The cubic spline interpolation method selects 7 consecutive sampling points R near the cross-correlation peak max_idx. 12 [max_idx-3] to R 12[max_idx+3] constructs a piecewise cubic polynomial function S(x), which maintains the continuity of function values, first derivative, and second derivative at the connection points of each segment, forming a smooth interpolation curve. The system determines the location of extreme points in the continuous domain by solving for the first derivative dS / dx=0 of the cubic spline function. Numerical methods are used to search for points where the derivative is zero within a small interval near the peak, thus obtaining the precise time delay location beyond the limitations of discrete sampling points. When the signal quality is high and the peak shape is regular, the system uses the Sinc interpolation method based on the mathematical principle of an ideal low-pass filter, using the formula R_continuous(t)=∑ n R 12 The [n]×sinc((t-nTs) / Ts) method reconstructs discrete cross-correlated samples into a continuous-time function, where sinc(x)=sin(πx) / (πx) is the sinusoidal basis function, perfectly reconstructing the continuous form of the band-limited signal. The system performs numerical optimization on the reconstructed continuous function over a large time range, finding the precise time position corresponding to the global maximum through optimization algorithms such as gradient search or the golden section. The adaptive selection mechanism of the interpolation method dynamically determines the optimal strategy based on the signal quality assessment results. When the correlation peak sharpness > threshold_sharp and the signal-to-noise ratio (SNR) > threshold_snr, the Sinc interpolation method with the highest computational accuracy is preferentially selected. When the signal has slight distortion but the peak shape is still good, the robust cubic spline interpolation method is selected. In cases of poor signal quality or high noise, the system reverts to the parabolic interpolation method with the highest computational efficiency. This adaptive mechanism ensures optimal interpolation accuracy under various signal conditions.

[0035] Please see Figure 2 The second embodiment of the present invention provides a TDOA refinement device for AIS signals, comprising: Preprocessing unit 201 is used to acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels. Feature extraction unit 202 is used to perform time-domain feature extraction, frequency-domain feature extraction and carrier phase extraction in parallel based on the preprocessed signal data. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. The result output unit 203 is used to perform consistency cross-validation on the time-domain TDOA measurement value, frequency-domain TDOA measurement value, and carrier phase TDOA measurement value through the multi-domain information fusion module, perform adaptive weight allocation based on confidence assessment, and output the optimal TDOA measurement result.

[0036] The third embodiment of the present invention provides a TDOA refinement device for AIS signals, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the TDOA refinement method for AIS signals as described in any of the above embodiments.

[0037] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, which can be executed by a processor of the device in which the computer-readable storage medium is located, to implement the TDOA refinement method for an AIS signal as described in any one of the claims above.

[0038] This invention provides a method, apparatus, device, and storage medium for refining the TDOA (Transient Tolerance of Occurrence) of AIS signals. First, AIS signal data from two channels is acquired and preprocessed. Then, based on the preprocessed signal data, time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction are performed in parallel. Specifically, time-domain feature extraction uses FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithms to obtain time-domain TDOA measurements. Frequency-domain feature extraction uses cross-power spectrum phase analysis and weighted least squares fitting to obtain frequency-domain TDOA measurements. Carrier phase extraction uses baseband down-conversion processing and automatic integer ambiguity resolution to obtain carrier phase TDOA measurements. Finally, a multi-domain information fusion module performs consistency cross-validation on the time-domain TDOA measurements, frequency-domain TDOA measurements, and carrier phase TDOA measurements. Based on confidence level assessment, adaptive weight allocation is performed to output the optimal TDOA measurement result. This solves the problem of insufficient progress in traditional TDOA measurements under complex electromagnetic environments, which cannot meet the high-precision positioning requirements of AIS signals.

[0039] Exemplary examples show that the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the TDOA refinement device implementing an AIS signal. For example, the apparatus described in the second embodiment of the present invention.

[0040] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the AIS signal TDOA refinement method, connecting various parts of the entire AIS signal TDOA refinement method through various interfaces and lines.

[0041] The memory can be used to store the computer program and / or modules. The processor implements various functions of a TDOA refinement method for AIS signals by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0042] If the implemented module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0043] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0044] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for refining the TDOA of AIS signals, characterized in that, include: Acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels; Based on the preprocessed signal data, time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction are performed in parallel. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. The time-domain TDOA measurements, frequency-domain TDOA measurements, and carrier-phase TDOA measurements are cross-validated for consistency using a multi-domain information fusion module. Adaptive weight allocation is then performed based on confidence assessment to output the optimal TDOA measurement result. Specifically, the confidence scores `confidence_time`, `confidence_freq`, and `confidence_carrier` are calculated for the time, frequency, and carrier-phase domains, respectively. The time-domain confidence score is calculated based on correlation peak sharpness, signal-to-noise ratio estimation, and interpolation reliability, while the frequency-domain confidence score is based on the goodness-of-fit R0. 2 The carrier confidence is calculated based on phase coherence and ambiguity confidence, and the coherence coefficient is calculated accordingly. The pairwise consistency of the three-domain measurement results is calculated using consistency cross-validation: consistency_tf=exp(-|τ_time-τ_freq|×f s ) consistency_tp=exp(-|τ_time-τ_carrier|×f s ) consistency_fp=exp(-|τ_freq-τ_carrier|×f s ) Where, consistency_tf represents the consistency index between time-domain and frequency-domain measurement results, consistency_tp represents the consistency index between time-domain and carrier phase-domain measurement results, and consistency_fp represents the consistency index between frequency-domain and carrier phase-domain measurement results. s The sampling frequency is represented by τ_time, the time-domain TDOA measurement value is represented by τ_freq, the frequency-domain TDOA measurement value is represented by τ_carrier, and the carrier phase TDOA measurement value is represented by τ_carrier. The fusion weights are calculated based on confidence and consistency. w_time=confidence_time·(consistency_tf+consistency_tp) / 2; w_freq=confidence_freq·(consistency_tf+consistency_fp) / 2; w_carrier=confidence_carrier·(consistency_tp+consistency_fp) / 2; Where w_time, w_freq, and w_carrier are the fusion weights corresponding to the time domain, frequency domain, and carrier phase domain, respectively; The optimal TDOA measurement result is generated based on the fusion weights.

2. The method for refining the TDOA of AIS signals according to claim 1, characterized in that, The temporal feature extraction obtains temporal TDOA measurements through FFT-accelerated cross-correlation calculation and subsampling precision interpolation algorithms, specifically: The cross-correlation function R of the two signals is calculated by accelerating the cross-correlation calculation using FFT. 12 [m], the expression for the cross-correlation function is: R 12 [m]=IFFT(S1(k)×S2 * (k)) Where S1(k) and S2(k) are the FFT transformation results of the two signals, respectively; A rough TDOA is obtained through peak detection, and its expression is: τ_coarse=(max_idx-N / 2)×Ts, where max_idx is the peak position of the cross-correlation function, N is the FFT length, and Ts is the sampling period; Subsampling accuracy is achieved using parabolic interpolation, with the subsampling correction factor being: δ=0.5×(y 1 y1) / (y 1 2y0+y1) Where y 1. y0 and y1 are the amplitudes of three sample points near the peak. The time-domain TDOA measurement value is calculated based on the subsampling correction: τ_time=(max_idx+δ-N / 2)×Ts.

3. The method for refining the TDOA of AIS signals according to claim 1, characterized in that, The frequency domain feature extraction obtains the frequency domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting, specifically: Calculate the cross power spectrum S of the two signals. 12 (ω)=S1(ω)×S2 * (ω), extract the phase spectrum φ(ω)=arg{S 12 (ω)}, where S1(ω) and S2(ω) are the frequency domain representations of the two signals, respectively; The phase spectrum is unwrapped to eliminate 2π jumps, resulting in a continuous phase spectrum φ_unwrap(ω). Using a weighted least squares fitting method, based on the linear relationship between phase spectrum and frequency φ_unwrap(ω)=-ω×τ+φ0, where ω is the angular frequency, τ is the time delay parameter, and φ0 is the initial phase offset, the frequency domain TDOA measurement value is solved: τ_freq=-[Σw(ω k )oh k φ_unwrap(ω k )×Σw(ω k )-Σw(ω k )oh k ×Σw(ω k )φ_unwrap(ω k )] / [Σw(ω k )oh k 2 ×Σw(ω k )-(Σw(ω k )oh k ) 2 ] Where w(ω) k ) is the weighting function determined based on the cross-power spectrum amplitude, ω k Let be the angular frequency of the k-th frequency point.

4. The method for refining the TDOA of an AIS signal according to claim 1, characterized in that, The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution, specifically as follows: via local carrier LO(t) = exp(-j2πf c t) Perform baseband down-conversion on the two signals to obtain baseband signals baseband1(t) and baseband2(t), where f c The AIS carrier frequency is 160MHz; Extract the instantaneous phases φ1(t)=arg{baseband1(t)} and φ2(t)=arg{baseband2(t)}, and calculate the phase difference Δφ(t)=φ2(t)-φ1(t), where φ1(t) and φ2(t) are the instantaneous phases of signal 1 and signal 2, respectively; The average phase difference is obtained by averaging in the time domain: Δφ_avg=arg{mean(exp(j×Δφ(t)))}, and the original carrier phase TDOA is obtained: τ_carrier_raw=Δφ_avg / (2πf c ); Automatic integer ambiguity resolution is employed, using coarse time-domain measurement results as constraints to determine the optimal ambiguity: n_optimal = argmin{|τ_carrier(n) - τ_coarse|}, where τ_carrier(n) = τ_carrier_raw + n / f c , where τ_carrier(n) is the carrier phase of the nth ambiguity, and τ_carrier_raw is the original carrier phase TDOA measurement value without ambiguity correction; The final carrier phase TDOA measurement value is: τ_carrier=τ_carrier_raw+n_optimal / f c 。 5. A TDOA refinement device for AIS signals, characterized in that, include: A preprocessing unit is used to acquire AIS signal data from two channels and preprocess the AIS signal data from the two channels. The feature extraction unit is used to perform time-domain feature extraction, frequency-domain feature extraction, and carrier phase extraction in parallel based on the preprocessed signal data. The time-domain feature extraction obtains the time-domain TDOA measurement value through FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency-domain feature extraction obtains the frequency-domain TDOA measurement value through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic integer ambiguity resolution. The result output unit is used to perform consistency cross-validation on the time-domain TDOA measurement values, frequency-domain TDOA measurement values, and carrier-phase TDOA measurement values ​​through a multi-domain information fusion module, perform adaptive weight allocation based on confidence evaluation, and output the optimal TDOA measurement result. Specifically, it is used to calculate the confidence scores confidence_time, confidence_freq, and confidence_carrier in the time domain, frequency domain, and carrier-phase domain, respectively. The time-domain confidence score is calculated based on the correlation peak sharpness, signal-to-noise ratio estimation, and interpolation confidence, while the frequency-domain confidence score is calculated based on the goodness-of-fit R². 2 The carrier confidence is calculated based on phase coherence and ambiguity confidence, and the coherence coefficient is calculated accordingly. The pairwise consistency of the three-domain measurement results is calculated using consistency cross-validation: consistency_tf=exp(-|τ_time-τ_freq|×f s ) consistency_tp=exp(-|τ_time-τ_carrier|×f s ) consistency_fp=exp(-|τ_freq-τ_carrier|×f s ) Where, consistency_tf represents the consistency index between time-domain and frequency-domain measurement results, consistency_tp represents the consistency index between time-domain and carrier phase-domain measurement results, and consistency_fp represents the consistency index between frequency-domain and carrier phase-domain measurement results. s The sampling frequency is represented by τ_time, the time-domain TDOA measurement value is represented by τ_freq, the frequency-domain TDOA measurement value is represented by τ_carrier, and the carrier phase TDOA measurement value is represented by τ_carrier. The fusion weights are calculated based on confidence and consistency. w_time=confidence_time·(consistency_tf+consistency_tp) / 2; w_freq=confidence_freq·(consistency_tf+consistency_fp) / 2; w_carrier=confidence_carrier·(consistency_tp+consistency_fp) / 2; Where w_time, w_freq, and w_carrier are the fusion weights corresponding to the time domain, frequency domain, and carrier phase domain, respectively; The optimal TDOA measurement result is generated based on the fusion weights.

6. A TDOA refinement device for AIS signals, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that can be executed by the processor to implement a TDOA refinement method for AIS signals as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The device contains a computer program that can be executed by a processor of the device in which the computer-readable storage medium is located, to implement the TDOA refinement method for an AIS signal as described in any one of claims 1 to 4.

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