TDOA (Time Difference Of Arrival) refining method, device and equipment for AIS (Automatic Identification System) signal and storage medium

Abstract

The invention provides a TDOA (Time Difference Of Arrival) refinement method, device and equipment for an AIS (Automatic Identification System) signal and a storage medium, and the method comprises the steps: firstly obtaining two channels of AIS signal data, carrying out the preprocessing of the two channels of AIS signal data, and then carrying out the parallel execution of time domain feature extraction, frequency domain feature extraction and carrier phase extraction based on the preprocessed signal data, in the time domain feature extraction, a time domain TDOA measurement value is obtained through FFT acceleration cross-correlation calculation and a sub-sampling precision interpolation algorithm, in the frequency domain feature extraction, a frequency domain TDOA measurement value is obtained through cross-power spectrum phase analysis and weighted least square fitting, and in the carrier phase extraction, a carrier phase TDOA measurement value is obtained through baseband down-conversion processing and integer ambiguity automatic calculation; and finally, performing consistency cross validation on the time domain TDOA measurement value, the frequency domain TDOA measurement value and the carrier phase TDOA measurement value through a multi-domain information fusion module, performing adaptive weight distribution based on confidence evaluation, and outputting an optimal TDOA measurement result. The problems that the traditional TDOA measurement progress is insufficient in a complex electromagnetic environment, and the requirement of AIS signal high-precision positioning cannot be met are solved.

Description

Technical Field

[0001] The present invention relates to the field of AIS, and in particular to a TDOA refinement method, device, equipment and storage medium for AIS signals. Background Art

[0002] The Automatic Identification System (AIS) is widely used in maritime navigation, traffic control, and situational awareness. One of its core features is positioning based on the time difference of observation (TDOA) between multiple receiving nodes. Traditional TDOA measurement methods rely 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 susceptible to noise interference and ambiguity, resulting in insufficient positioning accuracy and stability.

[0003] For example, the time domain cross-correlation method is highly dependent on the sampling rate and peak detection accuracy, the frequency domain method is susceptible to phase jump interference, and the carrier phase method, although highly accurate, is difficult to solve the whole cycle ambiguity problem and relies on additional prior information.

[0004] In view of this, this application is filed. Summary of the Invention

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

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

[0007] Preferably, the time domain feature extraction obtains the time domain TDOA measurement value by FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm, specifically: Accelerate the cross-correlation calculation by FFT and calculate the cross-correlation function R of the two signals 12 [m], the expression of the cross-correlation function is: R 12 [m]=IFFT(S1(k)×S2 ∗ (k)) Among them, S1(k) and S2(k) are the FFT transformation results of the two signals respectively; The coarse TDOA is obtained by 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; The parabolic interpolation method is used to achieve sub-sampling accuracy, and the sub-sampling correction is: 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 sub-sampling correction amount: τ_time=(max_idx+δ-N / 2)×Ts.

[0008] Preferably, the frequency domain feature extraction obtains the frequency domain TDOA measurement value by 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 and obtain a continuous phase spectrum φ_unwrap(ω); The weighted least squares fitting method is used to solve the frequency domain TDOA measurement value 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: 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 a weight function determined based on the cross-power spectrum amplitude, ω k is the angular frequency of the kth 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: Through the 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 time domain averaging: Δφ_avg=arg{mean(exp(j×Δφ(t)))}, and the original carrier phase TDOA is obtained: τ_carrier_raw=Δφ_avg / (2πf c ); The integer ambiguity is automatically resolved, and the time domain coarse measurement results are used 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 raw carrier phase TDOA measurement without ambiguity correction. The final carrier phase TDOA measurement is: τ_carrier=τ_carrier_raw+n_optimal / f c .

[0010] Preferably, the multi-domain information fusion module performs consistency cross-validation on the time domain TDOA measurement value, the frequency domain TDOA measurement value, and the carrier phase TDOA measurement value, performs adaptive weight allocation based on confidence evaluation, and outputs the optimal TDOA measurement result, specifically: Calculate the confidence_time, confidence_freq and confidence_carrier in the time domain, frequency domain and carrier phase domain respectively. The time domain confidence is based on the correlation peak sharpness, signal-to-noise ratio estimation and interpolation confidence calculation, and the frequency domain confidence is based on the goodness of fit R 2 and coherence coefficient calculation, carrier confidence is calculated based on phase coherence and ambiguity confidence; The pairwise consistency of the three-domain measurement results is calculated through 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 ) Among them, consistency_tf represents the consistency index of the measurement results in the time domain and frequency domain, consistency_tp represents the consistency index of the measurement results in the time domain and carrier phase domain, consistency_fp represents the consistency index of the measurement results in the frequency domain and carrier phase domain, f s Indicates the sampling frequency; Calculate fusion weights 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; An optimal TDOA measurement result is generated based on the fusion weights.

[0011] A second embodiment of the present invention provides a TDOA refinement device for an AIS signal, comprising: A preprocessing unit is used to obtain two-channel AIS signal data and preprocess the two-channel AIS signal data; A feature extraction unit is configured to perform time domain feature extraction, frequency domain feature extraction, and carrier phase extraction in parallel based on the preprocessed signal data, wherein the time domain feature extraction obtains a time domain TDOA measurement value by using FFT accelerated cross-correlation calculation and a sub-sampling precision interpolation algorithm, the frequency domain feature extraction obtains a frequency domain TDOA measurement value by using cross-power spectrum phase analysis and weighted least squares fitting, and the carrier phase extraction obtains a carrier phase TDOA measurement value by using 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 a multi-domain information fusion module, perform adaptive weight allocation based on confidence assessment, and output the optimal TDOA measurement result.

[0012] A third embodiment of the present invention provides a TDOA refinement device for an AIS signal, 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 a TDOA refinement method for an AIS signal as described in any one of the above.

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

[0014] The present invention provides a TDOA refinement method, device, equipment, and storage medium for AIS signals. The method first obtains two-channel AIS signal data and preprocesses the two-channel AIS signal data. Then, 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 time domain TDOA measurements through FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency domain feature extraction obtains frequency domain TDOA measurements through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains carrier phase TDOA measurements through baseband down-conversion processing and automatic integer ambiguity resolution. Finally, a multi-domain information fusion module is used to perform consistency cross-validation on the time domain TDOA measurements, frequency domain TDOA measurements, and carrier phase TDOA measurements. Adaptive weight allocation is performed based on confidence assessment to output the optimal TDOA measurement result. This method solves the problem that traditional TDOA measurements are insufficient in complex electromagnetic environments and cannot meet the needs of high-precision positioning of AIS signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 1 is a flow chart of a TDOA refinement method for an AIS signal provided by the first embodiment of the present invention; Figure 2 1 is a module diagram of a TDOA refinement device for AIS signals provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0019] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0021] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0022] The "first" and "second" mentioned in the embodiments are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. 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 an order other than that illustrated or described herein.

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

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

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

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

[0027] Furthermore, the two signals are calibrated for amplitude and phase consistency, using pre-stored calibration parameters to compensate for gain differences and phase offsets between the two channels. The system monitors the signal's dynamic range and saturation in real time, automatically adjusting gain or flagging saturation anomalies when the signal amplitude approaches the ADC's full scale. Furthermore, a digital filter performs bandpass filtering on the signal to remove interference signals outside the AIS frequency band. A ring buffer is used to manage the continuous data stream, ensuring data continuity during real-time processing.

[0028] S102, based on the preprocessed signal data, performing time domain feature extraction, frequency domain feature extraction, and carrier phase extraction in parallel, wherein the time domain feature extraction obtains a time domain TDOA measurement value by using FFT accelerated cross-correlation calculation and a sub-sampling precision interpolation algorithm, the frequency domain feature extraction obtains a frequency domain TDOA measurement value by using cross-power spectrum phase analysis and weighted least squares fitting, and the carrier phase extraction obtains a carrier phase TDOA measurement value by using baseband down-conversion processing and automatic integer ambiguity resolution; Specifically, in this embodiment, the two pre-processed AIS signals S1[n] and S2[n] are subjected to a fast Fourier transform of length N = 1024 points, respectively, to obtain frequency domain representations S1(k) = FFT(S1[n]) and S2(k) = FFT(S2[n]), where k is the frequency index. Utilizing the convolution property 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 reduces the O(N 2 ) complexity is reduced to O(NlogN), which greatly improves the computational efficiency. The system searches for the peak position max_idx with the largest amplitude in the obtained cross-correlation function, and calculates the rough time difference through 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. In order to break through 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, and selects the peak position and the three adjacent sample points y before and after it. −1 =|R 12 [max_idx-1]|、y0=|R 12 [max_idx]|、y1=|R 12 [max_idx+1]|, by fitting the parabolic function and finding its extreme point to determine the subsampling correction δ=0.5×(y −1 −y1) / (y −1 = −2y0+y1). This correction reflects the degree of deviation of the true peak from the sampling point. The final time-domain TDOA measurement is calculated as τ_time = (max_idx + δ-N / 2) × Ts. This achieves high-precision delay measurement far exceeding the sampling accuracy, typically achieving 0.1 nanosecond measurement accuracy.

[0029] In parallel, the two signals S1(ω) and S2(ω) are represented in the frequency domain by calculating the cross power spectrum S 12 (ω)=S1(ω)×S2 *(ω) obtains the complex spectrum containing the 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 (ω)}, the phase spectrum directly reflects the relationship between the phase difference between the two signals and the frequency. Since the phase function has a periodic jump of 2π, 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 for 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 the 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. In order to improve the fitting accuracy and suppress the influence of noise, the system adopts a weighted least squares fitting method. The weight function w(ω k ) According to the amplitude of the cross power spectrum of each frequency point |S 12 (ω k )| 2 The design is done so that the frequency point with stronger signal energy gets a higher weight. By minimizing the sum of squares of weighted errors, the system uses 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 ] directly solves the frequency domain TDOA measurement value, which can effectively utilize the phase information of the entire signal frequency band and provide high-precision TDOA estimation 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 ct), the local carrier performs complex mixing operation with the two received signals to realize baseband down-conversion processing to obtain baseband1(t)=S1(t)×LO(t) and baseband2(t)=S2(t)×LO(t). After low-pass filtering to remove the high-frequency components generated by the 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). In order 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 and averaging it, 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 ), but due to the 2π integer cycle ambiguity in the phase measurement, the true carrier phase TDOA should be in the form of 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 condition and automatically resolves the ambiguity by searching for the integer n that minimizes |τ_carrier(n)-τ_coarse|, that is, n_optimal=argmin{|τ_carrier(n)-τ_coarse|}, and the correct number of integer cycles can be determined without manual intervention. The final carrier phase TDOA measurement value is obtained by τ_carrier=τ_carrier_raw+n_optimal / f c Obtained, to achieve sub-nanosecond ultra-high precision delay measurement, the accuracy is mainly limited by the stability of the carrier frequency and the phase noise level.

[0031] S103, 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, performing adaptive weight allocation based on confidence evaluation, and outputting an optimal TDOA measurement result.

[0032] The system first calculates the confidence index of the three domains respectively. The time domain confidence index confidence_time is calculated by the correlation peak sharpness=max_val / mean(|R 12[m]|), signal-to-noise ratio estimation SNR_time and interpolation confidence interp_confidence = 1 / (1+|δ|) weighted combination confidence_time = a1×min(1,sharpness / 5)+a2×min(1,SNR_time / 20)+a3×interp_confidence, where a1=0.4, a2=0.4, a3=0.2. The frequency domain confidence confidence_freq is based on the goodness of fit R 2 =1-RSS / TSS and average coherence coherence_avg combination: confidence_freq=b1×R 2 +b2×coherence_avg+b3×min(1,SNR_freq / 15), where b1=0.5, b2=0.3, and b3=0.2. The carrier confidence confidence_carrier is calculated by phase coherence phase_coherence=|mean(exp(j×Δφ(t)))| and ambiguity confidence ambiguity_confidence=exp(-|τ_carrier-τ_coarse|×f c ) is obtained by combining confidence_carrier=γ1×phase_coherence+γ2×ambiguity_confidence, where γ1=0.6 and γ2=0.4. Then, consistency cross validation is performed by calculating 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 pairwise consistency index of the three-domain measurement results is obtained, where the exponential decay function ensures that the domain pairs with greater measurement differences receive lower consistency scores. Based on the dual evaluation of confidence and consistency, the system calculates the fusion weight. Specifically, Normalize the confidence first: 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 Use consistency to modify the weights: 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 averaging 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 lower than 0.3, the system automatically switches to single-domain mode and only uses the measurement result with the highest confidence to avoid incorrect fusion.

[0033] In one possible implementation of the present invention: The system's quality monitoring and exception handling mechanisms ensure the reliability of TDOA measurements through multi-level real-time detection and adaptive adjustment. It continuously monitors the signal quality indicators of each domain. When the signal-to-noise ratio (SNR) is detected to be below the preset threshold (threshold_min), the confidence weight of the corresponding domain is automatically reduced by introducing a penalty factor in the confidence calculation: confidence_modified = confidence_original × exp(-(threshold_min - SNR) / threshold_min) to achieve progressive weight decay. The system also 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 and a quality warning mechanism is triggered. For outlier detection, the system uses 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 the measurement value of a 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: When overall_consistency = (consistency_tf + consistency_tp + consistency_fp) / 3 is lower than 0.3, the system determines that there is a significant difference 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 sole output. The system calibration and compensation module uses pre-stored calibration parameters to compensate for system errors across multiple channels in real time. This includes 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 regular consistency testing. Temperature drift compensation uses the 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 the real-time temperature sensor data T_current and applies it to the final TDOA result, ensuring stable measurement accuracy in a temperature-varying environment.

[0034] In one possible implementation of the present invention: The cubic spline interpolation method selects 7 consecutive sample 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 the function value, first-order derivative, and second-order derivative at the connection points of each segment, forming a smooth interpolation curve. The system determines the position of the extreme point in the continuous domain by solving the first-order derivative dS / dx=0 of the cubic spline function, and uses numerical methods to search for the point where the derivative is zero in a small interval near the peak, thereby obtaining an accurate time delay position beyond the limit 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 the ideal low-pass filter, through the formula R_continuous(t)=∑ n R 12 [n]×sinc((t-nTs) / Ts) reconstructs discrete cross-correlation samples into a continuous-time function, where sinc(x)=sin(πx) / (πx) is a sinusoidal basis function, perfectly reconstructing the continuous form of the band-limited signal. The system numerically optimizes the reconstructed continuous function over a large time range, finding the precise time location corresponding to the global maximum through optimization algorithms such as gradient search or the golden section. An adaptive interpolation method dynamically determines the optimal strategy based on signal quality assessment results. When the correlation peak sharpness exceeds threshold_sharp and the signal-to-noise ratio (SNR) exceeds threshold_snr, the sinc interpolation method with the highest computational accuracy is prioritized. When the signal exhibits slight distortion but maintains a good peak shape, the robust cubic spline interpolation method is selected. In cases of poor signal quality or high noise, the system falls back to the parabolic interpolation method with the highest computational efficiency. This adaptive mechanism ensures optimal interpolation accuracy under various signal conditions.

[0035] See also Figure 2 A second embodiment of the present invention provides a TDOA refinement device for an AIS signal, comprising: The pre-processing unit 201 is used to obtain the AIS signal data of the two channels and pre-process the AIS signal data of the two channels; A feature extraction unit 202 is configured to perform time domain feature extraction, frequency domain feature extraction, and carrier phase extraction in parallel based on the preprocessed signal data, wherein the time domain feature extraction obtains a time domain TDOA measurement value by using FFT accelerated cross-correlation calculation and a sub-sampling precision interpolation algorithm, the frequency domain feature extraction obtains a frequency domain TDOA measurement value by using cross-power spectrum phase analysis and weighted least squares fitting, and the carrier phase extraction obtains a carrier phase TDOA measurement value by using 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 a multi-domain information fusion module, perform adaptive weight allocation based on confidence evaluation, and output the optimal TDOA measurement result.

[0036] A third embodiment of the present invention provides a TDOA refinement device for an AIS signal, 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 a TDOA refinement method for an AIS signal as described in any one of the above.

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

[0038] The present invention provides a TDOA refinement method, device, equipment, and storage medium for AIS signals. The method first obtains two-channel AIS signal data and preprocesses the two-channel AIS signal data. Then, 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 time domain TDOA measurements through FFT-accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm. The frequency domain feature extraction obtains frequency domain TDOA measurements through cross-power spectrum phase analysis and weighted least squares fitting. The carrier phase extraction obtains carrier phase TDOA measurements through baseband down-conversion processing and automatic integer ambiguity resolution. Finally, a multi-domain information fusion module is used to perform consistency cross-validation on the time domain TDOA measurements, frequency domain TDOA measurements, and carrier phase TDOA measurements. Adaptive weight allocation is performed based on confidence assessment to output the optimal TDOA measurement result. This method solves the problem that traditional TDOA measurements are insufficient in complex electromagnetic environments and cannot meet the needs of high-precision positioning of AIS signals.

[0039] For example, the computer programs described in the third and fourth embodiments of the present invention may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the device for implementing TDOA refinement of AIS signals. For example, the apparatus described in the second embodiment of the present invention may be used.

[0040] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor serves as the control center of the TDOA refinement method for an AIS signal, and utilizes various interfaces and circuits to connect the various components of the TDOA refinement method for an AIS signal.

[0041] The memory can be used to store the computer programs and / or modules. The processor implements various functions of a TDOA refinement method for AIS signals by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or a text conversion function); the data storage area may store data generated based on the use of the mobile phone (such as audio data and text message data). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0042] If the implementation 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, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0043] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive 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 changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A TDOA refinement method for AIS signals, characterized in that: include: Acquire two-channel AIS signal data and preprocess the two-channel AIS signal data; Based on the preprocessed 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 the time domain TDOA measurement value by FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm, the frequency domain feature extraction obtains the frequency domain TDOA measurement value by cross-power spectrum phase analysis and weighted least squares fitting, and the carrier phase extraction obtains the carrier phase TDOA measurement value by baseband down-conversion processing and automatic resolution of integer ambiguity; The time domain TDOA measurement value, frequency domain TDOA measurement value, and carrier phase TDOA measurement value are cross-validated for consistency through a multi-domain information fusion module, and adaptive weight allocation is performed based on confidence assessment to output the optimal TDOA measurement result.

2. The TDOA refinement method of an AIS signal according to claim 1, characterized in that: The time domain feature extraction obtains the time domain TDOA measurement value by FFT accelerated cross-correlation calculation and sub-sampling precision interpolation algorithm, specifically: Accelerate the cross-correlation calculation by FFT and calculate the cross-correlation function R of the two signals 12 [m], the expression of the cross-correlation function is: R 12 [m]=IFFT(S1(k)×S2 ∗ (k)) Among them, S1(k) and S2(k) are the FFT transformation results of the two signals respectively; The coarse TDOA is obtained by 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; The parabolic interpolation method is used to achieve sub-sampling accuracy, and the sub-sampling correction is: 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 sub-sampling correction amount: τ_time=(max_idx+δ-N / 2)×Ts.

3. The TDOA refinement method of an AIS signal 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 and obtain a continuous phase spectrum φ_unwrap(ω); The weighted least squares fitting method is used to solve the frequency domain TDOA measurement value 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: 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 a weight function determined based on the cross-power spectrum amplitude, ω k is the angular frequency of the kth frequency point.

4. The TDOA refinement method for AIS signals according to claim 1, wherein: The carrier phase extraction obtains the carrier phase TDOA measurement value through baseband down-conversion processing and automatic resolution of integer ambiguity, specifically: Through the 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 time domain averaging: Δφ_avg=arg{mean(exp(j×Δφ(t)))}, and the original carrier phase TDOA is obtained: τ_carrier_raw=Δφ_avg / (2πf c ); The integer ambiguity is automatically resolved, and the time domain coarse measurement results are used 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 raw carrier phase TDOA measurement without ambiguity correction. The final carrier phase TDOA measurement is: τ_carrier=τ_carrier_raw+n_optimal / f c 。 5. The TDOA refinement method of an AIS signal according to claim 1, characterized in that: The multi-domain information fusion module performs consistency cross-validation on the time domain TDOA measurement value, the frequency domain TDOA measurement value, and the carrier phase TDOA measurement value, performs adaptive weight allocation based on confidence evaluation, and outputs the optimal TDOA measurement result, specifically: Calculate the confidence_time, confidence_freq and confidence_carrier in the time domain, frequency domain and carrier phase domain respectively. The time domain confidence is based on the correlation peak sharpness, signal-to-noise ratio estimation and interpolation confidence calculation, and the frequency domain confidence is based on the goodness of fit R 2 and coherence coefficient calculation, carrier confidence is calculated based on phase coherence and ambiguity confidence; The pairwise consistency of the three-domain measurement results is calculated through 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 ) Among them, consistency_tf represents the consistency index of the measurement results in the time domain and frequency domain, consistency_tp represents the consistency index of the measurement results in the time domain and carrier phase domain, consistency_fp represents the consistency index of the measurement results in the frequency domain and carrier phase domain, f s Indicates the sampling frequency; Calculate fusion weights 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; An optimal TDOA measurement result is generated based on the fusion weights.

6. A TDOA refinement device for AIS signals, characterized in that: include: A preprocessing unit is used to obtain two-channel AIS signal data and preprocess the two-channel AIS signal data; A feature extraction unit is configured to perform time domain feature extraction, frequency domain feature extraction, and carrier phase extraction in parallel based on the preprocessed signal data, wherein the time domain feature extraction obtains a time domain TDOA measurement value by using FFT accelerated cross-correlation calculation and a sub-sampling precision interpolation algorithm, the frequency domain feature extraction obtains a frequency domain TDOA measurement value by using cross-power spectrum phase analysis and weighted least squares fitting, and the carrier phase extraction obtains a carrier phase TDOA measurement value by using 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 a multi-domain information fusion module, perform adaptive weight allocation based on confidence assessment, and output the optimal TDOA measurement result.

7. A TDOA refinement device for AIS signals, characterized in that: The method comprises 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 of an AIS signal according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement the TDOA refinement method of an AIS signal as described in any one of claims 1 to 5.

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