5G signal arrival time robust tracking method and system based on challenge environment

Abstract

The invention belongs to the field of TOA tracking, and discloses a 5G signal arrival time robust tracking method and system based on a challenge environment, and the method comprises the steps: carrying out the synchronization and pilot frequency information extraction of a 5G downlink broadcast channel (PBCH); cSI measurement and denoising are carried out; and estimating the time of arrival. According to the invention, based on a dual noise reduction mechanism of CSI, time domain smoothing and frequency domain processing are combined, so that the quality of low-signal-to-noise-ratio signals is remarkably improved. An SAGE algorithm and delay phase-locked loop collaborative architecture is introduced, accurate separation of multipath components and dynamic phase-locked optimization are achieved, complex environment interference is effectively inhibited, and TOA tracking precision is improved by more than 30%. According to the invention, a reliable solution is provided for high-precision positioning in a complex environment.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of TOA tracking technology, and particularly relates to a robust 5G signal arrival time tracking method and system based on challenging environments. Background Technology

[0002] Current location services based on 5G technology face significant technical bottlenecks in practical deployments, especially in complex and dynamic environments, where existing positioning methods struggle to meet the dual requirements of high accuracy and high reliability. Fingerprint positioning and geometric positioning, as mainstream technologies, each have core flaws that restrict their large-scale application. These specific problems are as follows:

[0003] Fingerprint positioning technology achieves location matching by constructing a signal feature database. While it exhibits centimeter-level accuracy in controlled environments, its core drawbacks lie in insufficient environmental adaptability and excessively high maintenance costs. This technology relies on the long-term stability of characteristic parameters such as Channel State Information (CSI) or Reference Signal Received Power (RSRP). However, in real-world scenarios, environmental changes such as base station parameter adjustments and building modifications cause continuous drift in signal characteristics. Studies show that the effective period of a fingerprint database typically does not exceed thirty days; beyond this period, positioning errors increase by two to three times. Although some solutions attempt to reduce labor costs through automated data collection, in densely populated base station areas, the dynamic rate of signal feature changes far exceeds the database update capability, resulting in persistently high annual maintenance costs. Furthermore, while the Reference Signal Received Power (RSRP) serves as a simplified characteristic parameter to reduce computational complexity, it is extremely sensitive to fluctuations in base station transmit power. Experimental data shows that even minor adjustments to base station power can significantly increase the positioning failure rate. More critically, existing fingerprint positioning research largely focuses on static indoor environments, lacking robust designs for dynamic interference in road scenarios, hindering the practical application of the technology.

[0004] Geometric positioning technology calculates location based on physical parameters such as time of arrival (TOA) and angle of arrival (DOA), theoretically offering greater environmental versatility. However, it faces severe challenges in real-world road scenarios. Building obstruction and vegetation cover in dense urban areas cause severe multipath effects, leading to the superposition of direct and reflected signals at the receiver. Traditional delay-locked loop (DLL) technology struggles to accurately separate multipath components, and the delay estimation error increases exponentially with the number of multipath paths. Secondly, measurement failures are prominent in low signal-to-noise ratio (SNR) environments. In road scenarios, factors such as vehicle movement and temporary obstacles cause instantaneous signal attenuation, potentially causing the SNR to plummet below -10 dB. In such cases, carrier phase (CP) measurements are affected by noise interference, resulting in cycle slips and a significantly increased probability of phase-locked loop loss. While existing synchronization algorithms achieve sub-nanosecond delay accuracy in high SNR laboratory environments, their performance degrades significantly in real-world low SNR scenarios. It is worth noting that most geometric positioning studies are designed only for line-of-sight (LOS) environments, while non-line-of-sight (NLOS) situations are common in real-world road scenarios. Existing methods have large positioning errors in such environments and cannot meet the needs of high-precision services.

[0005] Therefore, developing robust TOA tracking methods for challenging environments has become an urgent need to overcome existing technological bottlenecks. In the current road environment, complex factors such as the non-uniform signal attenuation zone formed by densely distributed buildings and the transient multipath interference caused by dynamic traffic flow necessitate an innovative solution that can adapt to environmental changes in real time, suppress multipath interference, and ensure measurement accuracy under low signal-to-noise ratio conditions.

[0006] Based on the above analysis, the urgent technical problems that need to be solved in the existing technology are:

[0007] Existing geometric positioning technologies based on commercial 5G New Radio (NR) signals have significant limitations in real-world, complex environments. Traditional methods rely excessively on ideal propagation conditions and cannot effectively address signal attenuation and multipath interference in real-world road scenarios. DLLs (Depth-of-Loss) struggle to accurately separate signal components in multipath environments, leading to a severe decrease in delay estimation accuracy. Furthermore, synchronization algorithms exhibit drastic performance degradation under low signal-to-noise ratio (SNR) conditions, significantly increasing the probability of phase-locked loop loss. GNSS-derived delay discriminators are not adapted to the characteristics of 5G NR signals, further exacerbating delay tracking errors. The existing technology verification system lacks adaptability testing for scenarios such as dynamic occlusion and transient interference, resulting in insufficient reliability in actual deployments. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a robust 5G signal arrival time tracking method and system based on challenging environments.

[0009] This invention is implemented as follows: a robust 5G signal arrival time tracking method based on challenging environments, characterized in that the robust 5G signal arrival time tracking method based on challenging environments specifically includes:

[0010] S1: 5G Downlink Broadcast Channel (PBCH) Synchronization and Pilot Information Extraction;

[0011] S2: CSI measurement and noise reduction;

[0012] S3: Arrival time estimate.

[0013] Furthermore, the synchronization signal block (SSB) of the PBCH in S1 mainly consists of three parts: the primary synchronization signal (PSS), the secondary synchronization signal (SSS), and the demodulation reference signal (DMRS). PSS synchronization utilizes the local PSS and the received signal. The correlation between them, each local PSS sequence is linked to a different NR cell identifier II ( ), This correlation can be expressed as:

[0014]

[0015] In the formula for conjugate, for The corresponding local PSS sequence, For PSS subcarrier indicators, For related delays, This is the estimated starting position of the PSS;

[0016] SSS synchronization is similar to PSS, with different local sequences corresponding to unique NR cell identifiers I( ), The local SSS sequence is associated with the received signal, described as follows:

[0017]

[0018] in For SSS subcarrier indicators, For the correlation delay of the subcarrier, for Local SSS sequences, The estimated starting position of the SSS;

[0019] Synchronization using PSS Synchronized with SSS The CID can be determined as follows:

[0020]

[0021] in The range is from 0 to 335, resulting in 336 possible values. The range is from 0 to 2, resulting in 3 different values. DMRS starts from a point... Start by extracting from PBCH at intervals of 4.

[0022] Further, step S2 includes:

[0023] (1) The DMRS CSI is measured using least squares (LS), expressed as:

[0024]

[0025] in This indicates the received DMRS. Indicates conjugate transpose;

[0026] (2) Construct a raised cosine filter to remove high-frequency noise components from the CIR, obtaining a denoised CIR. Then, use FFT to transform the denoised CIR back to the frequency domain. The core content of the raised cosine filter can be expressed as:

[0027] ,

[0028] Where parameters Controlling the passband bandwidth of the raised cosine filter;

[0029] (3) Further denoising is achieved by using convolutional averaging in the frequency domain to enhance denoising near the CIR peak:

[0030]

[0031] Furthermore, step S3 includes:

[0032] (1) Multipath resolution based on SAGE: The SAGE algorithm maximizes the likelihood function using an iterative method. The likelihood function based on CSI can be expressed as:

[0033]

[0034] in Representing a multipath parameter set, the SAGE algorithm extends the Expectation-Maximization (EM) algorithm by only reestimating the parameters in each iteration. A subset of the path, with parameters optimized sequentially for each path.

[0035] (2) Robust tracing based on DLLs:

[0036] Based on TOA discrimination, an improved representation of the symbol delay discriminator from CSI is proposed:

[0037]

[0038]

[0039] Noise is reduced by a second-order loop filter, and high-frequency components are removed from the discriminator output. Finally, the loop filter output is sent to a numerically controlled oscillator (NCO) to calculate the current symbol delay.

[0040] Another objective of this invention is to provide a robust 5G signal arrival time tracking system for challenging environments, the system specifically comprising:

[0041] The signal synchronization module is used for 5G downlink broadcast channel synchronization, cell search, and pilot extraction.

[0042] The CSI measurement and denoising module is used to solve the CSI by least squares, perform initial denoising with raised cosine filter CSI, and then perform further denoising with frequency domain averaged CSI.

[0043] The TOA estimation module is used for SAGE multipath resolution and robust DLL arrival time tracking.

[0044] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0045] This invention utilizes a dual noise reduction mechanism based on Channel State Information (CSI), combining time-domain smoothing and frequency-domain processing to significantly improve the quality of low signal-to-noise ratio signals. By introducing a spatially alternating generalized expectation-maximization (SAGE) algorithm and a delay-locked loop (PLL) collaborative architecture, it achieves accurate multipath component separation and dynamic PLL optimization, effectively suppressing interference in complex environments and improving TOA tracking accuracy by over 30%. This invention provides a reliable solution for high-precision positioning in complex environments.

[0046] This innovative technical solution utilizes existing 5G NR base stations to achieve high-precision outdoor positioning services. By eliminating the need for dedicated base stations and fingerprint database maintenance, it significantly reduces system construction and operation costs. In complex urban environments with low signal-to-noise ratios and non-line-of-sight propagation scenarios, it significantly improves the reliability and continuity of positioning services, successfully expanding into emerging application areas such as intelligent traffic monitoring, emergency search and rescue positioning, and UAV 3D navigation, creating sustainable value-added service revenue for operators.

[0047] This invention is the first in the world to construct a two-stage channel information denoising system, improving signal quality in multipath environments through joint time-frequency domain processing. It innovatively integrates the spatial alternation expectation-maximization algorithm with delay-locked loop technology, forming a tracking architecture with dynamic multipath signal resolution capabilities. The phase detector array, specifically optimized for the signal characteristics of the 5G NR standard, effectively solves the problem of timing accuracy degradation in commercial network equipment under complex channel conditions.

[0048] This solution successfully overcomes long-standing technical bottlenecks in the industry, such as unstable phase-locked loops with low signal-to-noise ratio and large timing estimation errors under dense multipath interference. Through the synergistic effect of multipath component iterative stripping algorithm and adaptive loop control technology, it achieves reliable delay measurement capability even in extremely harsh channel environments, breaking through the applicability limitations of traditional geometric positioning methods in commercial 5G networks.

[0049] This invention overturns the traditional understanding that "5G positioning must rely on fingerprint databases or dedicated hardware" from both theoretical and practical perspectives. Through the deep integration of refined channel processing and intelligent tracking algorithms, it demonstrates the feasibility of achieving high-precision geometric positioning based on standard base stations. Its technical approach effectively eliminates the strong dependence on fingerprint collection and maintenance, providing a new technical paradigm for the development of integrated communication and positioning network architectures. Attached Figure Description

[0050] Figure 1 This is a flowchart of a robust 5G signal arrival time tracking method based on challenging environments provided in an embodiment of the present invention;

[0051] Figure 2 This is a block diagram of a robust 5G signal arrival time tracking system based on challenging environments, provided in an embodiment of the present invention.

[0052] Figure 3 This is a field test road section diagram provided in an embodiment of the present invention;

[0053] Figure 4 This is a graph showing the tracking results of Time of Arrival (TOA) provided in an embodiment of the present invention;

[0054] Figure 5 This is a graph of the cumulative distribution function of the TOA estimation error provided in an embodiment of the present invention;

[0055] Figure 6 This is a detailed flowchart of the robust 5G signal arrival time tracking method based on challenging environments provided in this embodiment of the invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] In the context of current 5G communication system deployments facing challenging environments with high dynamics and severe obstruction, Time of Arrival (TOA) estimation technology plays a crucial role in accurate positioning, time synchronization, and wireless sensing. However, traditional TOA extraction methods generally rely on strong signal assumptions and idealized channel conditions. In scenarios with severe multipath interference, significant channel frequency selectivity, and low pilot signal-to-noise ratio, they often suffer from large estimation biases, poor stability, and insufficient resolution, failing to meet the terminal positioning and networking requirements in environments such as urban canyons, underground spaces, and non-line-of-sight environments. Therefore, improving the robustness and accuracy of TOA estimation has become a critical issue that urgently needs to be addressed for the industrial deployment of 5G positioning systems.

[0058] The robust tracking method proposed in this invention introduces a multi-level processing link in its overall architecture. By jointly enhancing synchronization, denoising channel estimation, and resolving paths, it eliminates the propagation effect of crosstalk errors between modules. Its core mechanism utilizes the primary synchronization (PSS) and secondary synchronization (SSS) information in the synchronization signal block to determine the candidate cell identifier and starting position through a maximum correlation strategy. This allows for the identification of a unique cell, and the structural parameters of the demodulation reference signal (DMRS) are derived from the CID, ensuring the accuracy of pilot extraction in the frequency domain. This processing logic effectively avoids estimation drift caused by DMRS sampling point offset under non-ideal multipath conditions, constructing a synchronization initialization process that is robust and adaptable to challenging channels.

[0059] In the channel estimation stage, the least squares (LS) method is used to initially estimate the demodulated reference signal, and then a raised cosine window function is constructed to perform time-domain filtering on the channel impulse response (CIR). This filter window is jointly set based on the pilot subcarrier and noise reduction control parameters. By dynamically suppressing high-frequency components in the time domain, it effectively mitigates disturbances caused by spectral leakage and sideband ringing, recovering a clear main path energy structure. Simultaneously, through frequency-domain convolutional averaging, the signal-to-noise ratio in the region surrounding the main path delay is further improved, providing a highly stable input for subsequent path parameter estimation.

[0060] To address the ambiguity in TOA estimation caused by multipath characteristics, this invention introduces a SAGE path separation algorithm extended from the expectation-maximization (EM) concept. By independently updating the delay and power of each path during the iteration process, a discernible delay distribution sequence is constructed in the multipath energy overlap region, thereby improving the extraction capability of weak paths. This algorithm updates only local parameters in each iteration, avoiding overall convergence instability, and possesses good numerical stability and computational controllability, making it particularly suitable for sparse multipath channel modeling and fitting.

[0061] To improve the accuracy of the final symbol-level delay extraction, an improved symbol delay discriminator (DLL) was constructed. This structure utilizes the CSI frequency domain response to construct the cross-correlation output of the lead and lag channels, and introduces a normalized error energy difference to construct a robust discrimination index. This is then combined with a second-order phase-locked loop filter to remove non-stationary interference terms. By controlling the coupling between the discriminator output and the feedback path of the local loop, the numerically controlled oscillator (NCO) can precisely adjust the tracking delay at the sub-symbol level, overcoming the adaptability bottleneck of traditional time-domain difference methods under modulation type and waveform variations.

[0062] This method establishes a three-layer nested structure for challenging scenarios, effectively improving the robustness of TOA extraction while maintaining estimation resolution. Compared with traditional single-link estimation methods, it exhibits stronger anti-interference capabilities and stable convergence characteristics in typical complex environments such as non-line-of-sight, strong occlusion, and severe multipath dispersion, providing an industrially usable core supporting technology path for 5G enhanced positioning and high-precision synchronization systems.

[0063] like Figure 1 As shown, this embodiment of the invention provides a robust 5G signal arrival time tracking method based on challenging environments. The method specifically includes:

[0064] S1: 5G New Radio (NR) Downlink Broadcast Channel (PBCH) Synchronization and Pilot Information Extraction;

[0065] S2: Channel State Information (CSI) Measurement and Denoising;

[0066] S3: Time of Arrival (TOA) Estimation.

[0067] The synchronization signal block (SSB) of the PBCH in S1 mainly consists of three parts: the primary synchronization signal (PSS), the secondary synchronization signal (SSS), and the demodulation reference signal (DMRS). PSS synchronization utilizes the local PSS and the received signal. The correlation between them. Each local PSS sequence is linked to a different NR cell identifier II ( ), This correlation can be expressed as:

[0068]

[0069] In the formula for conjugate, for The corresponding local PSS sequence, For PSS subcarrier indicators, For related delays, This is the estimated starting position of the PSS. SSS synchronization is similar to PSS. Different local sequences correspond to unique NR cell identifiers I( ), This invention associates the local SSS sequence with the received signal, as described in the following description:

[0070]

[0071] in For SSS subcarrier indicators, For the correlation delay of the subcarrier, for Local SSS sequences, This represents the estimated starting position of the SSS.

[0072] Synchronization using PSS Synchronized with SSS The cell ID (CID) can be determined as follows:

[0073]

[0074] in The range is from 0 to 335, resulting in 336 possible values. The range is from 0 to 2, resulting in 3 distinct values. This combination ensures that each cell in the network has a unique CID. DMRS starts from point v and extracts from PBCH at intervals of 4. The parameter v is entirely determined by the CID and is expressed as the CID modulo 4.

[0075] The S2, CSI, contains rich channel information and is a fundamental source of key parameters for wireless signal localization. Therefore, obtaining accurate CSI is a prerequisite for ensuring TOA estimation performance. This invention first uses least squares (LS) to measure the demodulation reference signal (DMRS) CSI. Subsequently, to recover the DMRS CSI in severely interfered environments, this invention proposes a dual denoising method including initial denoising and further denoising. In the frequency domain, the local DMRS is represented as a signal with dimension... of ,in This indicates the number of DMRS subcarriers. The CSI measurement value is obtained using the LS method and is expressed as:

[0076]

[0077] in This indicates the received DMRS. This represents the conjugate transpose. Wireless channels are frequently subjected to severe noise interference. To improve the accuracy of DMRS CSI, this invention first constructs a raised cosine filter to remove high-frequency noise components from the channel impulse response (CIR). The core of this filter can be expressed as:

[0078] ,

[0079] Where parameters The passband bandwidth of the raised cosine filter is controlled. This raised cosine filter can effectively reduce high-frequency noise and spectral leakage in the time-domain signal, thus obtaining a denoised CIR. Then, a Fast Fourier Transform (FFT) is used to transform the denoised CIR back to the frequency domain. By constructing a raised cosine function to filter the CIR, high-frequency components are eliminated. However, this method is insufficient to address the effects of severe interference. Therefore, this invention proposes further denoising by using convolutional averaging in the frequency domain to achieve enhanced denoising near the CIR peak:

[0080]

[0081] This method improves the stability of channel estimation and reduces noise by performing convolution and normalization on the channel estimates of adjacent subcarriers in the frequency domain. Unlike traditional low-pass filtering, frequency-domain averaging can smooth the channel response and adjust the boundary region according to the number of available samples, ensuring consistent estimation quality across the entire frequency band. This method maintains accurate estimation in both edge and center regions, thus achieving balanced and accurate channel estimation.

[0082] The proposed TOA estimation method described in S3 is specifically designed to provide accurate and reliable TOA measurements in highly disruptive environments. This method comprises two main parts: multipath resolution based on Expectation-Maximization of Interaction Space (SAGE) and robust tracking based on a DLL. Multipath resolution plays a crucial role in initializing the DLL and enables relocking when the DLL loses its lock. In turn, the feedback structure of the DLL guarantees high-precision TOA tracking.

[0083] The SAGE algorithm accurately estimates multipath parameters by maximizing the likelihood function using an iterative method, while remaining robust in low signal-to-noise ratio environments. It effectively handles closely spaced or highly correlated multipath components, making it well-suited for complex and dynamic conditions. The CSI-based likelihood function can be expressed as:

[0084]

[0085] in This represents the set of parameters for the multipath. The SAGE algorithm extends the Expectation-Maximization (EM) algorithm. In each iteration, it only re-estimates... It is a subset of the set, therefore, the parameters of each path are optimized sequentially.

[0086] Initially developed for Global Positioning System (GPS) receivers, the DLL focuses on accurately processing the code phase of the received signal. It utilizes an early-late discriminator, a loop filter, and an oscillator to achieve high-precision code phase-locking. With technological advancements, the application of DLLs has gradually expanded into the field of wireless communications. In this context, DLLs are primarily used for symbol synchronization, ensuring accurate demodulation of the received signal. Some researchers have extended the use of DLLs to Orthogonal Frequency Division Multiplexing (OFDM) modulation, using it to estimate the symbol delay in the frequency domain, also known as the Time of Arrival (TOA). TOA discrimination is performed by associating the received OFDM symbol with the early and late offsets of a local reference OFDM symbol. Based on this, an improved representation of the symbol delay discriminator from CSI has been proposed:

[0087]

[0088]

[0089] To ensure system stability, a second-order loop filter was implemented, effectively reducing noise and removing high-frequency components from the discriminator output. While a first-order filter might not adequately suppress high-frequency noise, a third-order filter could introduce unnecessary complexity and pose a risk to stability. Therefore, the second-order filter achieves an ideal balance between performance and complexity. Finally, the loop filter output is sent to a numerically controlled oscillator (NCO) to calculate the current symbol delay.

[0090] like Figure 2 As shown in the figure, an embodiment of the present invention provides a robust 5G signal arrival time tracking system based on challenging environments, specifically including:

[0091] The signal synchronization module is used for 5G downlink broadcast channel synchronization, cell search, and pilot extraction.

[0092] The CSI measurement and denoising module is used to solve the CSI by least squares, perform initial denoising with raised cosine filter CSI, and then perform further denoising with frequency domain averaged CSI.

[0093] The TOA estimation module is used for SAGE multipath resolution and robust DLL arrival time tracking.

[0094] This invention can directly utilize commercial 5G NR signals for outdoor positioning, making it particularly suitable for complex environments with low signal-to-noise ratios and severe multipath interference, such as urban roads and forest areas. Its applications include 5G-based mobile terminal positioning services (such as personal travel navigation and social positioning); vehicle positioning and roadside coordination in intelligent transportation and vehicle-to-everything (V2X) networks; real-time tracking of rescue personnel and critical equipment in urban public safety and emergency management; formation navigation and path planning for drones and outdoor inspection robots; and location awareness services such as asset tracking and shared mobility tool positioning for the Internet of Things (IoT). The above systems do not require additional dedicated base stations; they can leverage existing 5G base station networks to achieve wide-coverage and flexibly deployed outdoor positioning capabilities.

[0095] like Figure 3 As shown, this embodiment selected the roads surrounding the playground of the School of Information Science at Wuhan University as the field test section. This section is densely covered by trees and includes several non-line-of-sight (NLOS) sections, fully reflecting the characteristics of a complex environment. Based on the collected measured data, the time of arrival (TOA) of the system was tracked, and the visualization results are shown below. Figure 4 To verify the performance advantages of the proposed solution, a comparative experiment was designed with the Multiple Signal Classification (MUSIC) algorithm, and the cumulative distribution functions of the TOA estimation errors of the two algorithms were plotted on [the graph]. Figure 5 Experimental results show that, compared with the traditional MUSIC algorithm, the proposed method significantly improves both the stability and accuracy of TOA estimation, with the TOA estimation accuracy increasing from 11.92 m to 4.55 m.

[0096] Figure 6 This invention provides a detailed flowchart of a robust 5G signal arrival time tracking method for challenging environments.

[0097] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0098] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A robust 5G signal arrival time tracking method for challenging environments, characterized in that, Includes the following steps: Step 1: Synchronization and pilot information extraction for the downlink broadcast channel; Step 2: Measure and denoise the channel state information based on the extracted pilot signals; Step 3: Combine the multipath discrimination algorithm and the symbol delay discriminator to achieve arrival time estimation.

2. A method for extracting 5G synchronization information, characterized in that, Includes the following steps: The synchronization start position and cell identifier number 2 are extracted by the correlation between the primary synchronization signal and the received signal. The synchronization start position and cell identifier 1 are extracted by the correlation between the subsynchronization signal and the received signal. Cell identification code is generated by combining cell identifiers 1 and 2, and the extraction location of pilot signal is determined accordingly.

3. A method for 5G pilot CSI estimation and denoising, characterized in that, Includes the following steps: Channel state information is calculated using the least squares method; Construct a raised cosine filter to remove high-frequency interference from the channel response in the time domain; The denoised channel response is converted into a frequency domain signal, and the signal-to-noise ratio is enhanced by frequency domain convolution averaging.

4. A multipath arrival time estimation method based on the SAGE algorithm, characterized in that, Includes the following steps: The arrival time of each path is iteratively optimized; Maximize the likelihood function of CSI observation data to extract multipath parameters; The path set and corresponding delay are obtained through multiple rounds of expected maximum and parameter update operations.

5. A symbol-level TOA tracing method based on a DLL structure, characterized in that, Includes the following steps: Construct the cross-correlation function between leading and lagging paths; Construct the discriminator output based on the cross-correlation strength; The output is filtered into a second-order loop filter to remove high-frequency interference, and the final time delay is calculated.

6. A 5G signal arrival time tracking system, characterized in that, include: The synchronization processing module is used to acquire synchronization information of the downlink broadcast channel and extract pilot signals; The CSI estimation module is used to perform channel state estimation and frequency domain denoising on the pilot signal; The TOA estimation module is used to perform multipath extraction and time delay determination based on the denoised CSI.

7. A 5G channel estimation and filtering module system structure, characterized in that, include: The LS estimation submodule is used to construct a least-squares solution based on the pilot signals and estimate the CSI. The time-domain filtering submodule is used to smooth the channel impulse response using a raised cosine function; The frequency domain enhancement submodule is used to perform weighted averaging of frequency domain CSI to improve latency resolution.

8. An information processing terminal, characterized in that, This is used to implement the functions of the synchronization processing module, CSI estimation module, and TOA estimation module in the 5G signal arrival time tracking system as described in claim 6.

9. A computer-readable storage medium having a computer program stored thereon, the program, when executed by a processor, causing the processor to perform the robust 5G signal arrival time tracking method as described in claim 1.

10. A computing device comprising a processor and a memory, wherein the memory stores program code that, when executed by the processor, causes the computing device to perform the robust 5G signal arrival time tracking method as described in claim 1.

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