Satellite navigation interference wide-area monitoring method and system based on ubiquitous perception

Abstract

The application discloses a satellite navigation interference wide-area monitoring method and system based on ubiquitous sensing, and the method comprises the following steps: ubiquitous sensing: relying on global satellite navigation system terminals in existing urban infrastructure as sensing nodes, constructing a wide-area distributed monitoring network through optimization networking, and collecting multi-source heterogeneous satellite navigation monitoring data; interference monitoring: based on multi-source heterogeneous satellite navigation terminal data, detecting, identifying and positioning interference signals, and determining the type and position of the interference source; situation generation: based on the type, position and other monitoring information of the interference source, combining a geographic information system, generating and displaying a multi-dimensional real-time threat situation, and triggering an early warning when the threat level reaches a preset threshold. The application is applied to the field of interference identification and positioning, can realize low-cost and efficient wide-area coverage, rapid detection, accurate identification, accurate positioning and real-time early warning of urban-level satellite navigation interference, and improves the overall guarantee capability of urban space-time safety.

Description

Technical Field

[0001] This invention relates to the field of interference identification and localization technology, specifically a wide-area monitoring method and system for satellite navigation interference based on ubiquitous sensing. Background Technology

[0002] Spatiotemporal information is the cornerstone of critical infrastructure, directly impacting the safe and stable operation of core systems such as communications, power, and transportation. Global Navigation Satellite Systems (GNSS), as the most crucial source of spatiotemporal information, provide precise positioning and timing services. However, due to the extremely low power of its signals transmitted from satellites to the ground, it is highly susceptible to malicious or unintentional interference, posing a significant security risk of being denied, weakened, or even manipulated, thus carrying a major threat to critical infrastructure. In recent years, security incidents caused by GNSS signal interference to critical infrastructure have occurred frequently worldwide, resulting in substantial economic losses and social impact.

[0003] To address spatiotemporal security threats, the core task is to achieve rapid detection, accurate assessment, and timely early warning of these threats. Currently, research and product development related to interference monitoring technologies have been conducted both domestically and internationally. Internationally, in addition to general radio interference monitoring systems, specialized interference monitoring technologies for GNSS have been developed. Representative solutions include GPS interference source monitoring and location system networks and airborne GPS interference source location systems. The GPS interference source monitoring and location system network consists of 10 radio frequency interference sources, 33 portable positioning systems, over 200 handheld positioning systems, and 21 airborne devices, utilizing almost all existing interference source location technologies. The airborne GPS interference source location system includes a four-element antenna array, an interference monitoring / direction-finding receiver, a data logger, and a GPS / INS (inertial navigation system) navigation device. It employs a short baseline interferometer direction-finding system, using a high-sensitivity, fast-scanning receiver to monitor interference sources and accurately determine directions. Another commercial solution involves deploying groups of sensors connected to a central server to detect and geolocate GPS interference in critical areas such as airports and ports. This system can verify and locate threat types and present them on a visual map. Another type of GNSS interference detection and analysis system can continuously monitor specific areas, automatically detect, classify, and locate intentional interference, and can be flexibly configured according to security level requirements, upgrading from single-station detection to multi-station network positioning.

[0004] With the large-scale application of the BeiDou-3 system, China is increasingly emphasizing the safe application of satellite navigation. The monitoring subsystems built by relevant research institutions can detect, analyze, and comprehensively evaluate satellite signals and the electromagnetic environment. In addition, relevant departments have also launched a real-time GNSS interference early warning system for civil aviation. Spectrum monitoring technology platforms provided by several technology companies can also detect GNSS signal interference. However, the above research and applications are still in their initial stages. While interference monitoring can be achieved, it is difficult to achieve efficient, accurate, and networked coverage of interference monitoring, identification, and positioning over large urban areas.

[0005] The existing technology has the following main technical disadvantages: 1. Insufficient wide-area and large-scale monitoring capabilities: Existing solutions are mostly limited to monitoring key local nodes such as airports and ports, lacking mature and efficient city-level wide-area coverage technology solutions, and are unable to achieve comprehensive perception of satellite navigation interference over a large area. 2. High cost of professional system deployment: The construction and deployment of professional GNSS interference monitoring systems are expensive, making it difficult to popularize them on a large scale in cities, which limits the large-scale application of the technology and makes it impossible to form an economically feasible full-area monitoring network. 3. Weak real-time early warning and proactive defense capabilities: Existing technologies mainly focus on passive monitoring and handling after interference occurs, lacking the ability to proactively perceive interference signals, predict trends, and provide early warnings. This makes it difficult to identify and avoid risks in time before interference causes actual harm. 4. The problem of data silos is prominent: monitoring data from different scenarios and industries are scattered, with inconsistent standards and a lack of interactive sharing mechanisms. The data is highly privatized, making it impossible to achieve cross-domain network collaborative analysis and form a data synergy to support the overall situation assessment. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, this invention provides a wide-area monitoring method and system for satellite navigation interference based on ubiquitous sensing. It utilizes GNSS terminals in existing communication, power, and transportation infrastructure as sensing nodes, optimizes network topology to achieve wide-area collection of multi-source heterogeneous data, and uses classical or artificial intelligence algorithms to detect, identify, and locate interference threats. Combined with a geographic information system, it generates a multi-dimensional real-time threat situation, forming a full-process, closed-loop ubiquitous sensing system for urban spatiotemporal security threats. This enables wide-area coverage, rapid detection, accurate identification, precise location, and real-time early warning of city-level satellite navigation interference in a low-cost and efficient manner, thereby enhancing the overall urban spatiotemporal security protection capability.

[0007] To achieve the above objectives, the present invention provides a wide-area monitoring method for satellite navigation interference based on ubiquitous sensing, comprising the following steps: Ubiquitous sensing: Relying on the global satellite navigation system terminals in the city's existing infrastructure as sensing nodes, a wide-area distributed monitoring network is constructed through optimized networking to collect multi-source heterogeneous satellite navigation monitoring data; Interference monitoring: Based on multi-source heterogeneous satellite navigation terminal data, interference signals are detected, identified, and located to determine the type and location of the interference source; Situation generation: Based on monitoring information such as the type and location of interference sources, combined with geographic information systems, a multi-dimensional real-time threat situation is generated and displayed, and an early warning is triggered when the threat level reaches a preset threshold.

[0008] To achieve the above objectives, the present invention also provides a wide-area satellite navigation interference monitoring system based on ubiquitous sensing, which uses the above-described method for wide-area monitoring of satellite navigation interference. The wide-area satellite navigation interference monitoring system includes: The sensing node networking module is used to schedule and manage various types of global satellite navigation system terminals deployed in urban infrastructure to form a ubiquitous sensing network, and optimize the networking to form a hierarchical distributed monitoring network to collect multi-source heterogeneous satellite navigation monitoring data. The data processing module is communicatively connected to the sensing node networking module, and is used to receive the multi-source heterogeneous satellite navigation monitoring data, and to detect, identify and locate satellite navigation interference, and determine the type and location of the interference source. The threat situation generation module is communicatively connected to the data processing module. It is used to generate and display a multi-dimensional real-time threat situation based on the location and type of the interference source and in conjunction with the geographic information system, and to trigger an early warning when the threat level reaches a preset threshold.

[0009] Compared with the prior art, the present invention has the following beneficial technical effects: 1. This invention utilizes existing urban infrastructure (such as communication, power, and transportation) to construct a sensing network using positioning and timing terminals, eliminating the need for additional dedicated data acquisition equipment and significantly reducing system construction and maintenance costs. By optimizing the networking strategy, it achieves low-cost, wide-area coverage monitoring of spatiotemporal security threats across the entire city, solving the pain points of limited coverage and expensive deployment associated with traditional solutions. 2. This invention employs an interference detection, identification, and localization method based on multi-source heterogeneous data. It integrates a single-node dual-detector joint detection algorithm with a wide-area network deep residual neural network and multi-attribute decision-making method to perform refined classification and identification of interference signals. At the same time, through improved time difference measurement and joint localization algorithm, it achieves accurate localization of interference sources, effectively solving the technical problems of difficult multi-source data fusion, ambiguous interference identification, and low localization accuracy. 3. This invention integrates interference heat map generation, interference impact quantitative analysis, and geographic information system visualization. It can comprehensively and in real time present the spatial distribution, intensity level, temporal evolution of interference threats and their impact on critical infrastructure. Furthermore, it can quantitatively classify threat levels and automatically issue early warnings, providing intuitive and quantitative digital decision support for global situation assessment, prediction, and joint emergency response. 4. This invention constructs a closed-loop sensing system that integrates ubiquitous sensing, interference monitoring, and situation generation. It achieves integrated processing from data acquisition, interference detection, identification, and location to situation assessment and early warning. It breaks through the bottlenecks of disordered sensing nodes, insufficient data processing capabilities, and delayed situation generation in traditional technologies, and comprehensively improves the ability to quickly detect, accurately assess, and promptly handle urban spatiotemporal security threats. 5. The technical solution of this invention is deeply adapted to the large-scale security application of global satellite navigation systems such as BeiDou, providing security for critical infrastructure. At the same time, it organically integrates cutting-edge technologies from multiple fields such as the Internet of Things, big data, artificial intelligence, and geographic information, promoting cross-innovation and achievement transformation of related technologies, and helping to promote the technological upgrading and ecosystem construction of the entire city's spatiotemporal security industry. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0011] Figure 1 This is a flowchart of the wide-area monitoring method for satellite navigation interference based on ubiquitous sensing in an embodiment of the present invention; Figure 2 This is a block diagram of a wide-area satellite navigation interference monitoring system based on ubiquitous sensing, as described in an embodiment of the present invention.

[0012] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0014] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0015] Example 1 This embodiment discloses a wide-area monitoring method for satellite navigation interference based on ubiquitous sensing. It utilizes GNSS terminals in existing communication, power, and transportation infrastructure as sensing nodes, optimizes network topology to achieve wide-area collection of multi-source heterogeneous data, and uses classical or artificial intelligence algorithms to detect, identify, and locate interference threats. Combined with a geographic information system, it generates a multi-dimensional real-time threat situation, forming a full-process, closed-loop ubiquitous sensing system for urban spatiotemporal security threats. This achieves wide-area coverage, rapid detection, accurate identification, precise location, and real-time early warning of city-level satellite navigation interference in a low-cost and efficient manner, thereby improving the overall spatiotemporal security protection capability of the city. (Reference) Figure 1 The specific steps included in this embodiment are as follows: Ubiquitous sensing: Relying on the global satellite navigation system terminals in the city's existing infrastructure as sensing nodes, a wide-area distributed monitoring network is constructed through optimized networking to collect multi-source heterogeneous satellite navigation monitoring data; Interference monitoring: Based on multi-source heterogeneous satellite navigation terminal data, interference signals are detected, identified, and located to determine the type and location of the interference source; Situation generation: Based on monitoring information such as the type and location of interference sources, combined with geographic information systems, a multi-dimensional real-time threat situation is generated and displayed, and an early warning is triggered when the threat level reaches a preset threshold.

[0016] In this embodiment, the ubiquitous sensing process specifically includes: Establish a node resource database containing several sensing nodes based on satellite navigation terminals in the city's existing infrastructure; Based on the node resource database, satellite navigation monitoring data is collected, and the data is aggregated and transmitted to the data center by relying on the public communication network or the dedicated communication network inside the node; The data collected by the sensing nodes are preprocessed, redundancy is removed, and the data is classified and arranged to optimize the node and network structure and flexibly build a wide-area hierarchical distributed monitoring network.

[0017] In establishing the node resource database, the distribution, models, data acquisition capabilities, and communication interfaces of GNSS terminals in existing urban infrastructure such as communication base stations, power terminals, and transportation equipment are comprehensively reviewed first. GNSS terminals with data acquisition and transmission capabilities are then selected as effective sensing nodes. In this embodiment, the sensing nodes included in the node resource database are mainly divided into static nodes and dynamic nodes. Static nodes include satellite navigation terminals associated with key infrastructure such as power systems and communication base stations, while dynamic nodes include satellite navigation terminals associated with mobile devices such as buses, private cars, and personal mobile phones. In specific applications, the sensing nodes in the node resource database can be further divided into the following four categories: Class A nodes: Equipped with UBX-M8N receivers, they collect satellite navigation monitoring data through UBX-NAV-PVT and UBX-NAV-SAT protocols. Their positioning accuracy is generally low, making them suitable for urban facilities with low positioning requirements, such as ordinary streetlights, charging piles, and other municipal IoT terminals. Class B nodes: Employ dual UBX-F9P receivers, support differential positioning, and collect satellite navigation monitoring data through UBX-NAV-PVT, UBX-NAV-SAT, and UBX-RXM-RAWX protocols. They possess high positioning accuracy and are suitable for scenarios with extremely high positioning accuracy requirements, such as autonomous driving. Class C nodes are equipped with a single UBX-F9P receiver and collect satellite navigation monitoring data through the UBX-NAV-PVT and UBX-NAV-SAT protocols. They have high positioning accuracy and are suitable for urban facilities with medium accuracy requirements, such as urban sanitation vehicles and municipal engineering vehicles. Category D nodes: These are APP nodes that rely on the built-in navigation chip in mobile phones to collect satellite navigation monitoring data. They have good versatility and wide deployment capabilities, and can cover urban scenarios across the entire region.

[0018] After completing the construction of the node resource library, networking strategies can be designed based on big data sharing. In this embodiment, the radio environment map system is divided into a global radio environment map (Global REM) and a local radio environment map (Local REM). The local REM prioritizes storing dynamic parameters such as radio propagation loss, signal strength, and node location to achieve rapid data updates and local response. After multiple local REMs process sensing data by region, the best data is aggregated into the global REM, distributing data processing pressure and improving wide-area situational awareness capabilities. The specific construction process of the local REM and global REM in this embodiment is as follows: The construction process of the local REM is as follows: First, local management domains are defined according to urban administrative divisions and geographical zones. Each management domain corresponds to a local REM. The management domain is defined by the coverage area of ​​a single aggregation layer node (such as a municipal facility node or a smart street light pole), which typically covers a street or business district (with a radius of about 0.5-2 kilometers). Within the management domain, each sensing node collects dynamic parameters such as radio propagation loss, signal strength, node location, and carrier-to-noise ratio in real time. These parameters are aggregated in time windows (such as 1 second) to generate a snapshot of the local electromagnetic environment in the area. The local REM is stored using a key-value pair structure. The key is the node identifier and timestamp, and the value is the electromagnetic parameter vector currently collected by the node, enabling rapid data updates and local query responses.

[0019] The construction process of the global REM is as follows: Each local REM uploads its aggregated key data—including regional signal strength statistics, interference detection results, node operating status, etc.—to the global REM at a set interval (e.g., every 5 seconds). The global REM, covering the entire city, merges and integrates the data uploaded from multiple local REMs, establishes a global electromagnetic environment index according to spatial grids (e.g., 500m × 500m), and stores the signal characteristic statistics and interference markers of each grid. The global REM estimates and fills in the grids without deployed nodes using spatial interpolation methods to form a complete city-level electromagnetic environment situation map. A hierarchical architecture of "local rapid response and global unified aggregation" is formed between the local REMs and the global REM: local REMs prioritize storing dynamic parameters to achieve sub-second rapid response; the global REM integrates data from multiple local REMs to support wide-area situation analysis and cross-regional collaboration.

[0020] After completing the construction of the global REM and local REM, a wide-area hierarchical distributed monitoring network can be built. The specific implementation process is as follows: Step 101: Clean, denoise and standardize the satellite navigation monitoring data such as signal strength, frequency and phase collected by each sensing node, and extract the intrinsic features of the satellite navigation monitoring data to form a signal feature vector, thereby enhancing the interpretability of the interference detection model. The intrinsic features of the satellite navigation monitoring data include carrier-to-noise ratio change rate, power spectral entropy, pseudorange residual statistics, Doppler frequency offset variance, etc. Step 102: Integrate satellite navigation monitoring data from different sensing nodes and of different types to provide a comprehensive perspective on interference detection. The integration process specifically involves: First, the data collected by different types of sensing nodes are parsed and standardized in format. For example, data collected by Class A nodes via UBX-NAV-PVT and UBX-NAV-SAT protocols, pseudorange and carrier phase data collected by Class B nodes via UBX-RXM-RAWX protocol, and data in their respective protocol formats by Class C and Class D nodes are uniformly converted into a standard data structure containing timestamps, node identifiers, location information, and signal feature vectors. Then, according to the time alignment principle and using a globally unified clock as a reference, the asynchronously collected data from different sensing nodes are timestamped to ensure that multi-source data at the same time can be jointly analyzed. Finally, according to the spatial alignment principle, the location information of each sensing node is transformed and registered to construct a spatiotemporally aligned multi-source heterogeneous fusion dataset. Step 103: Based on statistical methods or machine learning methods such as random forests and support vector machines, a preliminary judgment is made as to whether there are interference signals in the satellite navigation signal data collected by the sensing nodes, and valid sensing nodes without interference signals are selected. The specific process is as follows: For each sensing node, the extracted signal feature vector is initially classified using a preset statistical threshold or a trained random forest / support vector machine classification model. If the signal energy exceeds the normal threshold or the classification model outputs an interference category, the sensing node is determined to have interference signals and is marked as a suspected interference sensing node; otherwise, it is marked as a valid sensing node without interference and included in the subsequent network screening pool. For example, taking the carrier-to-noise ratio (C / N0) as an example, when the C / N ratio of a sensing node is lower than 30 dB-Hz or the power spectral entropy is lower than the normal baseline value, it is determined that the node may be subject to suppression interference. Step 104: Utilize the agent to learn the optimal strategy to redundantly remove effective sensing nodes with similar data features and overlapping monitoring ranges, thereby reducing network load. Specifically, the redundancy removal involves: First, the data feature similarity between any two effective sensing nodes is calculated. The Pearson correlation coefficient is used to measure the linear correlation between the signal feature vectors of the two nodes within the sliding time window. When the Pearson correlation coefficient ρ > 0.85, the data features of the two nodes are considered highly similar. Second, the monitoring range overlap rate of the two effective sensing nodes is calculated. A coverage circle is constructed with the positions of the two effective sensing nodes as the center and their respective effective monitoring radii. The ratio of the intersection area to the union area of ​​the two coverage circles is calculated as the overlap rate R. When R > 0.6, the monitoring range is considered highly overlapping. When two effective sensing nodes simultaneously satisfy the data feature similarity ρ > 0.85 and the monitoring range overlap rate R > 0.6, the two effective sensing nodes are considered redundant. After determining redundancy, nodes are selected for elimination according to node priority. The priority rule is: Class B nodes (high-precision differential) > Class C nodes (medium-precision) > Class A nodes (general type) > Class D nodes (APP mobile nodes). Among nodes of the same type, those with small monitoring radii and low data quality are eliminated first, while nodes with high priority, strong monitoring capabilities, and excellent data quality are retained. Step 105: Based on the location, monitoring capabilities, and data characteristics of the sensing nodes, all remaining valid sensing nodes are divided into core nodes, backbone nodes, and ordinary nodes to construct a wide-area hierarchical distributed monitoring network. The specific process includes: The selection criteria for core nodes are: nodes with high-precision positioning and timing capabilities, stable and high-speed communication links, and coverage of key urban areas. Priority will be given to selecting from Class B nodes. Typical carriers are GNSS terminals attached to 5G base stations and GNSS terminals of power base stations, which are responsible for the functions of data aggregation, forwarding and collaborative scheduling across the entire region. The selection criteria for backbone nodes are: nodes with medium or higher positioning accuracy, stable communication capabilities, and coverage areas with dense populations or near key facilities. They are mainly selected from Class C nodes and some Class A nodes. Typical carriers are municipal facility nodes such as smart street light poles and traffic monitoring poles, which undertake data aggregation and edge computing tasks within the region. Ordinary nodes are all other valid sensing nodes, including Class A general nodes and Class D mobile APP nodes, which are responsible for basic data collection and reporting; The construction process of the wide-area hierarchical distributed monitoring network is as follows: The core node forms the top layer, interconnected with the 5G-A backbone network via dedicated fiber optic lines to build a low-latency, high-bandwidth backbone link, connecting to the city's smart management platform. The backbone nodes form the middle layer, forming a star-shaped connection with the core node. Each backbone node covers a street or business district, collecting data from ordinary nodes within the area, performing local preprocessing (data cleaning, feature extraction, redundancy filtering), and uploading only critical information and abnormal data to the core layer. Ordinary nodes form the bottom layer, flexibly connecting to either backbone or core nodes—densely populated urban areas connect to the nearest backbone node, while remote suburban areas directly connect to the core node. Mobile nodes (such as Class D nodes on buses) dynamically switch access nodes based on their routes and signal quality. Data interoperability between layers is achieved through standard communication protocols, forming a three-layer distributed architecture: "core layer global scheduling—aggregation layer regional collaboration—sensing layer data acquisition."

[0021] As a preferred embodiment, the ubiquitous sensing in this embodiment also includes dynamic adjustment and evaluation of the wide-area hierarchical distributed monitoring network. That is, based on the real-time monitoring node working status and network coverage, when node failure or coverage blind spot occurs, the wide-area hierarchical distributed monitoring network is dynamically adjusted to ensure network stability. Specifically, the system monitors the heartbeat signals and data reporting status of each node in real time. When a node fails to respond for three consecutive reporting cycles, it is determined to be faulty, triggering a network reconstruction process. First, within the area of ​​the faulty node, a node with the closest coverage and monitoring capabilities to the faulty node is selected from backup nodes at the same level or nodes at adjacent levels as a replacement. This replacement node is then elevated to the level of the faulty node, inheriting its data forwarding and collaborative scheduling responsibilities. Second, if there are no available replacement nodes in the faulty area, resulting in a coverage blind spot, mobile nodes (such as monitoring terminals carried on buses) are temporarily dispatched to extend the data collection time and reporting frequency when passing through the area, forming mobile blind spot coverage. At the same time, the system updates the node status information in the global REM and local REM, marking the faulty node as offline and the replacement node as active, ensuring the real-time and accuracy of network topology information. Once the faulty node recovers, the system assesses the current network status. If the replacement node has stably assumed its responsibilities, the recovered node will be downgraded to a standby node. If the monitoring capabilities of the recovered node are superior to those of the replacement node, its original hierarchical role will be restored, and the replacement node will revert to its original hierarchical level.

[0022] In this embodiment, interference monitoring mainly includes three parts: single-node interference detection, wide-area network-wide interference identification, and interference source localization. Single-node interference detection refers to processing data collected by sensing nodes using energy discrimination, information comparison, and artificial intelligence techniques to detect the presence or absence of interference. Wide-area network-wide interference identification involves aggregating multi-source heterogeneous data from all sensing nodes in the network, extracting interference features, and identifying and classifying suppression interference, deception interference (localization), and deception interference (time synchronization) based on classical algorithms or artificial intelligence methods. Interference source localization involves jointly employing traditional time-of-arrival (TOA) positioning, cross-positioning, and artificial intelligence techniques to calculate and output the location coordinates of the interference source.

[0023] In this embodiment, single-node interference detection and wide-area network-wide interference identification constitute a collaborative working mechanism of "rapid front-end screening - precise back-end analysis": For each sensing node in the network, single-node interference detection is continuously performed as the first line of defense to achieve rapid preliminary screening of interference signals; when a single node detects a suspected interference signal, it reports the detection result and original feature data to the central node, triggering the wide-area network-wide interference identification process. At this time, multi-source heterogeneous data from multiple nodes across the network are collected for joint analysis. Through a neural network model combined with a multi-attribute decision method, the interference type is refined for classification and confirmation; for nodes whose single-node detection result is no interference, wide-area network-wide identification is not triggered, and only status data is reported according to the regular cycle. In short, single-node detection runs continuously for all nodes and is responsible for quickly detecting anomalies; wide-area network-wide identification is initiated centrally for areas where single-node detection results are abnormal and is responsible for accurate identification and confirmation.

[0024] In this embodiment, the single-node interference detection specifically employs a spoofing signal joint detection method that combines phase difference detection and carrier Doppler variance detection to perform interference detection on satellite signals collected by a single sensing node. The specific implementation process is as follows: Step 201: Real-time acquisition of carrier phase data and Doppler frequency offset data of GNSS signals from a single sensing node; Step 202: Based on the collected carrier phase data and Doppler frequency offset data, preliminary interference detection is performed using a phase difference detector and a Doppler variance detector, respectively, and the respective decision results are output. Step 203: Calculate the first cumulative average of the differences between the M most recent decisions of the phase difference detector and the global decision, and the second cumulative average of the differences between the M most recent decisions of the Doppler variance detector and the global decision. The global decision refers to the consensus reached by all sensing nodes in the network regarding the satellite signal at the current moment. Specifically, it is the majority vote of the decisions made by all nodes that detected the satellite signal within the most recent time window. That is, if more than half of the nodes' decisions are "spoofing interference exists," the global decision is "spoofing interference exists," otherwise it is "no spoofing interference." The global decision represents the network's collective understanding of the signal's state and is used to measure the degree of deviation between the local decisions of individual detectors and the overall consensus. Step 204: Determine whether the first cumulative average and the second cumulative average exceed a preset threshold. If both the first and second cumulative average values ​​exceed the preset threshold, it is determined that both the phase difference detector and the Doppler variance detector show significant inconsistency with the global decision. This indicates that both detectors detected abnormal features within the time window and their decision directions consistently deviate from the global consensus. In this case, the decision result corresponding to the one with the larger cumulative average value among the two detectors is taken as the final output, i.e., the detector conclusion with a greater degree of deviation and higher detection confidence is selected. If the decision results of the two detectors are consistent, the consistent decision result is directly output. If either the first cumulative average or the second cumulative average exceeds a preset threshold, then the detector corresponding to the threshold is selected for detection, and its decision result is output. If neither the first cumulative average nor the second cumulative average exceeds a preset threshold, the final detection conclusion is output by combining the decision results of the phase difference detector and the Doppler variance detector. The joint process is as follows: when the cumulative average of the two detectors does not exceed the preset threshold, a weighted voting method is used to fuse the decision results of the two detectors. The weights are determined by the historical detection accuracy of each detector. Let the historical detection accuracy of the phase difference detector be P1, and the historical detection accuracy of the Doppler variance detector be P2, with normalized weights w1=P1 / (P1+P2) and w2=P2 / (P1+P2) respectively. The decision results of the two detectors at the current moment are weighted and voted. If the weighted voting value exceeds 0.5, it is determined that there is deception interference; otherwise, it is determined that there is no deception interference. This joint process integrates the complementary information of the two detectors: the phase difference detector is sensitive to signal phase abrupt changes and is good at detecting generative deception interference; the Doppler variance detector is sensitive to signal frequency shifts and is good at detecting relay-type deception interference. The combination of the two can effectively cover different types of deception interference.

[0025] For wide area network (WAN) data, the role of a single node is limited and cannot accurately and quickly identify multiple interference sources. Therefore, it is necessary to mine and analyze the heterogeneous data of the entire network and use multi-attribute decision-making methods to detect interference from multiple sources and of multiple types. Therefore, this embodiment uses a neural network method to detect and identify GNSS interference. The specific implementation process is as follows: Step 301, Data Collection: Collect pre-processed multi-source heterogeneous data from all nodes in the network, including signal strength, frequency, phase changes, etc. For example, in the experiment, data acquisition was carried out under interference-free environment, data acquisition under suppression interference, and data acquisition under deception interference, as shown in Tables 1 to 3. Step 302, Feature Extraction: Extract key features representing different types of interference from the collected data, such as the signal power threshold for suppressing interference and the phase change characteristics of spoofing interference. Step 303, Model Training and Recognition: An interference recognition model is constructed based on a deep residual neural network or confederation learning approach. The model is trained using labeled interference sample data. The model analyzes wide-area data and, combined with a multi-attribute decision method, achieves accurate identification of different types of interference, such as suppression interference and deception interference. The process of combining the multi-attribute decision method is as follows: First, the interference type probability vector output by the neural network model is used as the basic probability assignment for each attribute. The decision attribute set is defined to include five attributes: interference signal power characteristics, signal spectrum characteristics, carrier phase consistency characteristics, Doppler frequency offset characteristics, and spatial distribution characteristics. Second, the weight of each attribute is determined using the analytic hierarchy process (AHP). The interference signal power characteristics and signal spectrum characteristics have higher weights (0.25 each), followed by the carrier phase consistency characteristics and Doppler frequency offset characteristics (0.20 each), and the spatial distribution characteristics have the lowest weight (0.10). Then, the TOPSIS multi-attribute decision method is used to comprehensively rank the candidate interference types output by the neural network model. The relative closeness of each candidate type to the positive and negative ideal solutions is calculated, and the interference type with the highest closeness is selected as the final recognition result. For example, when a neural network model outputs "suppression interference probability 0.65, deception interference probability 0.30, no interference probability 0.05" for a certain detection, after a multi-attribute decision method comprehensively considers the scores of the five attributes, if the overall closeness of suppression interference is 0.82 and deception interference is 0.45, then it is finally determined to be suppression interference. The multi-attribute decision method here plays a role in performing secondary verification and attribute-level fusion of the neural network output, effectively reducing the risk of misjudgment by a single model. Step 304, Reverse Result Verification: Cross-validate the recognition result with the single-node detection result, eliminate misjudged data, improve the recognition accuracy, compare the two states to obtain the interference recognition accuracy, and the recognition accuracy is 92.38%.

[0026] Table 1 Data under interference-free environment

[0027] Table 2 Data under suppression interference

[0028] Table 3 Data under deception interference

[0029] In this embodiment, the interference source localization is achieved using a TDOA-based localization method, and its specific implementation process is as follows: Step 401: Select the core node in the wide-area hierarchical distributed monitoring network as the master receiver and the remaining nodes as auxiliary receivers. Use the adaptive filtering recursive least squares algorithm combined with the generalized cross-correlation method to estimate the cross-correlation of the interference signals received by the master receiver and auxiliary receivers, and determine the TDOA time difference measurement value between each auxiliary receiver and the master receiver. Step 402: Substitute the TDOA time difference measurement value into the hyperbolic positioning equation, use the Chan algorithm to calculate the initial coordinates of the interference source, and use the initial coordinates as input values ​​to the Taylor algorithm for iterative calculation to optimize the positioning accuracy and output the two-dimensional or three-dimensional coordinates of the interference source. Let the coordinates of the interference source be (x, y) (2D positioning) or (x, y, z) (3D positioning). There are M receiving nodes in the monitoring network. The known coordinates of the i-th node are (x, y) and (y, z). i ,y i ,z i The core node (the first node) is selected as the reference station, and the remaining M-1 nodes are used as auxiliary receivers.

[0030] In the specific implementation process, the process of establishing the hyperbolic positioning equation for TDOA is as follows: The distance from the interference source to the i-th node is:

[0031] The distance from the interference source to the reference node (the first node) is:

[0032] The TDOA time difference measurement value between the i-th auxiliary receiver and the main receiver obtained in step 401 is τ. i ,1, the corresponding distance difference is:

[0033] Where c is the speed of light, thus establishing M-1 hyperbolic positioning equations:

[0034] In practical implementation, the process of solving for the initial coordinates based on the Chan algorithm is as follows: Chan's algorithm transforms a nonlinear equation into a pseudolinear equation by introducing an intermediate variable. Let R1² = (x - x1)² + (y - y1)² + (z - z1)², squaring both sides of the hyperbola equation and rearranging, we get:

[0035] Rewrite the above equation in matrix form for i=2,3,...,M:

[0036] in: Let be the vector to be determined; Ga is a coefficient matrix of (M-1)×4, with the (i-1)th row being: ; h is a constant vector of (M-1)×1, and the (i-1)th element is: .

[0037] In the first step, we assume that R1 is independent of the first three elements of θ, and obtain an initial estimate of θ using weighted least squares:

[0038] Where Q is the covariance matrix of the TDOA measurement error. Substituting R1 into the constraint conditions A second weighted least squares correction is applied to the first three components to obtain the final solution of the Chan algorithm. Used as the initial coordinates of the interference source.

[0039] In practice, the iterative optimization process based on the Taylor algorithm is as follows: Output using the Chan algorithm Using the initial values, a first-order Taylor expansion of the hyperbola positioning equation is performed at the current estimated point, linearizing the nonlinear equation as follows:

[0040] in Substituting the calculated value of the hyperbola equation into the current estimated point, the partial derivative is:

[0041] Rewrite the M-1 linearized equations in matrix form:

[0042] Where G is an (M-1)×3 Jacobian matrix, and the (i-1)th row is... ; Δε is the residual vector, and the (i-1)th element is ; This is the coordinate correction amount.

[0043] Iterative update of coordinate estimation:

[0044] Repeatedly calculate the Jacobian matrix, the correction, and update the coordinates until the correction satisfies the convergence criteria. (ε is the preset convergence threshold, such as 1 meter), outputting the final coordinates of the interference source. , , ).

[0045] In practical applications, if there are multiple interference sources, the number and type of interference signals can be identified through signal correlation diagrams, and the TDOA value of each interference source can be calculated and located.

[0046] It is worth noting that in specific applications, the method of TDOA-based positioning is not limited to the above-mentioned method for locating interference sources. Traditional time difference of arrival positioning, cross positioning and artificial intelligence techniques can also be used to calculate and output the location coordinates of the interference source. This embodiment will not elaborate on these techniques.

[0047] In this embodiment, situation generation is a crucial step in achieving visualized presentation, quantitative assessment, and timely early warning of urban satellite navigation interference identification and positioning. Its core revolves around depicting the spatial distribution of interference, quantitatively analyzing its impact, displaying multi-dimensional situation data, and triggering early warnings. By integrating multi-dimensional data such as geographic information, electromagnetic propagation characteristics, and positioning and timing errors, a threat situation generation system with real-time performance, accuracy, and comprehensiveness is constructed. This achieves a closed-loop connection from interference detection and positioning to situation assessment and early warning, providing intuitive and quantitative decision-making basis for emergency response. In the specific implementation process, situation generation mainly includes: Based on the Gaussian regression process algorithm, and combined with the location coordinates of the interference source, digital elevation and elevation gradient data, an interference heatmap is generated. Based on the equivalent carrier-to-noise ratio calculation model, the impact of interference on positioning and timing errors is evaluated, and the threat level is classified according to the magnitude of the error. By overlaying interference heatmaps, threat levels, and geographic information systems, a multi-dimensional situational visualization is displayed, and an early warning is automatically triggered when the threat level reaches medium or higher.

[0048] The specific implementation process for generating the interference heatmap in this embodiment is as follows: Step 501: Collect interference identification results, interference source positioning coordinates, regional digital elevation data, elevation gradient data, and multi-source heterogeneous spectrum information; Step 502: Define the observation point set X as a four-dimensional vector composed of plane two-dimensional spatial position (x,y), digital elevation H, and elevation gradient Grad_H, to reflect the influence of the geographical environment on electromagnetic propagation; Step 503: Considering the influence of terrain changes on signal power, an asymmetric kernel function is used to calculate the covariance matrix between different observation points and the points to be inferred. The more drastic the terrain change (the larger the elevation gradient and elevation difference), the greater the power difference weight; Step 504: Based on the idea of main path iteration, use sparse monitoring data to realize the refined construction of the interference electromagnetic radiation field map through the Gaussian regression process algorithm, and generate a high-precision interference heat map to intuitively present the interference intensity and distribution range. The specific process is as follows: First, denote the known observation point set as X={x1,x2,...,x n}, each observation point x i is a four-dimensional vector including plane position, digital elevation, and elevation gradient, and the corresponding interference signal power observation value is denoted as y={y1,y2,...,y n}, the set of points to be inferred is denoted as ; Next, construct a Gaussian process prior, assuming that the interference signal power follows a Gaussian process distribution in space, that is, f(x)~GP(μ(x),k(x,x')), where μ(x) is the mean function which takes the local weighted average of the observed values, k(x,x') is the covariance function defined by the asymmetric kernel function, and the form is k(x i ,x j )=σ²·exp(-||x i -x j ||²_A / (2l²)), where ||·||_A is the asymmetric distance metric considering the terrain influence, σ² is the signal variance, and l is the length scale parameter; Then, perform sparse approximation based on the main path iteration idea, select p inducing points from n observation points (p<<n), approximate the complete Gaussian process posterior through the variational inference method, reduce the computational complexity from O(n³) to O(np²), and enable the algorithm to process city-level large-scale monitoring data; Subsequently, calculate the posterior predictive distribution of the points to be inferred, the posterior mean , the posterior variance , where K(·,·) is the covariance matrix, σ n ² is the observation noise variance, and the posterior mean is the optimal estimated value of the interference signal power at the point to be inferred; Finally, the posterior mean of all points to be inferred is rendered on the GIS map as contour lines or color bands to generate an interference heatmap. The posterior variance is overlaid as an uncertainty measure, providing decision-makers with an intuitive presentation of the spatial distribution of interference intensity and its confidence level.

[0049] In this embodiment, the specific implementation process of threat level classification is as follows: Step 601: Calculate the equivalent carrier-to-noise ratio based on the equivalent carrier-to-noise ratio calculation model, as follows:

[0050] in, The equivalent carrier-to-noise ratio of a GNSS receiver under interference conditions. The carrier-to-noise ratio under interference-free conditions. This is the ratio of internal interference to received signal power. For pseudocode rate, The anti-interference quality factor (related to the ranging signal type and the interference signal type); Step 602, perform positioning and timing error assessment: calculate the standard deviation of the three-dimensional positioning error using the user ranging error (URE). and standard deviation of timing error ,in For position accuracy factor, For time precision factor, This includes measurement errors caused by satellite orbit, clock bias, atmospheric delay, receiver error, and multipath. Step 603: Based on the interference intensity, the attenuation degree of the equivalent carrier-to-noise ratio, and the magnitude of the positioning and timing error, the threat level is divided into four levels: minor, moderate, severe, and extreme, providing a quantitative basis for situation assessment. Specifically, this is based on the equivalent carrier-to-noise ratio (C / N0) calculated in step 601. e Based on the positioning and timing errors calculated in step 602 and the number and range of interference sources, the threat level is divided into four levels, including: Minor threat: The equivalent carrier-to-noise ratio attenuation is small, the receiver can still track the signal normally, the positioning and timing error is within an acceptable range, the number of disturbed nodes is small and the coverage area is extremely small, and the interference does not have a substantial impact on the positioning and timing function of critical infrastructure. Medium threat: The equivalent carrier-to-noise ratio is significantly attenuated, the signal quality is significantly degraded, the positioning accuracy shows a perceptible deviation, the timing error exceeds the normal accuracy but has not yet caused service interruption, the number of disturbed nodes is large and covers a certain area, and has caused local signal quality degradation but has not yet caused service interruption. Serious threats: The equivalent carrier-to-noise ratio is significantly attenuated, the signal quality is severely deteriorated, the positioning accuracy is greatly reduced, some receivers experience short-term loss of lock, the timing error exceeds the tolerance of critical infrastructure, there are many disturbed nodes and the coverage area is wide, or critical infrastructure enters the disturbed area, which has led to a significant decrease in positioning accuracy or short-term loss of lock in critical areas. Extreme threats: Severe attenuation of the equivalent carrier-to-noise ratio leads to complete loss of receiver lock, or detection of deliberate spoofing attack characteristics, resulting in unavailable positioning services, severely degraded timing accuracy, and paralysis of large-scale regional navigation services.

[0051] In the above classification, the attenuation of the equivalent carrier-to-noise ratio reflects the physical impact of interference intensity on signal quality (corresponding to step 601), the positioning and timing error quantifies the functional damage of interference to downstream applications (corresponding to step 602), and the number of disturbed nodes and the coverage area characterize the spatial impact range of the interference. These four indicators together constitute the basis for determining the threat level. When the levels corresponding to the various indicators are inconsistent, the highest level is taken as the comprehensive threat level of the area to ensure the conservatism and security of the early warning.

[0052] After generating the interference heatmap and classifying the threat level, the system overlays core monitoring information such as the interference heatmap, interference source location coordinates, and threat level with a Geographic Information System (GIS), and incorporates geographic data such as terrain, building, and critical infrastructure distribution. Simultaneously, a dedicated graphical user interface is used to display the real-time threat situation, dynamically presenting the temporal evolution trend, spatial distribution range, intensity level of the interference threat, and its actual impact on surrounding critical infrastructure. When the interference threat level reaches medium or higher, the system will automatically trigger an early warning mechanism, sending warning information including the interference type, specific location, impact range, and handling suggestions to relevant management departments via SMS, platform push notifications, and other means.

[0053] It is worth noting that, although this embodiment Figure 1 The steps are shown sequentially as indicated by the arrows, but they are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are performed; they can be executed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0054] Example 2 Based on the ubiquitous sensing-based wide-area satellite navigation interference monitoring method in Example 1, this example discloses a ubiquitous sensing-based wide-area satellite navigation interference monitoring system, referencing... Figure 2 The city's satellite navigation interference identification and positioning system includes a sensing node networking module, a data processing module, and a threat situation generation module, specifically: The sensing node networking module is used to schedule and manage various global satellite navigation system terminals deployed in urban infrastructure to form a ubiquitous sensing network. It also optimizes the networking to form a wide-area hierarchical distributed monitoring network to collect multi-source heterogeneous satellite navigation monitoring data. The data processing module is communicatively connected to the sensing node networking module, and is used to receive the multi-source heterogeneous satellite navigation monitoring, and to detect, identify and locate satellite navigation interference, and determine the type and location of the interference source; The threat situation generation module is communicatively connected to the data processing module. It is used to generate and display a multi-dimensional real-time threat situation based on the location and type of the interference source and in conjunction with the geographic information system, and to trigger an early warning when the threat level reaches a preset threshold.

[0055] In this embodiment, the specific working process and working principle of the sensing node networking module, data processing module, and threat situation generation module are the same as those in Embodiment 1, so they will not be described again in this embodiment. Each module can be implemented entirely or partially through software, hardware, or a combination thereof. Each unit module can be embedded in the processor of the computer device in hardware form or independent of it, or it can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above unit modules.

[0056] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A wide-area monitoring method for satellite navigation interference based on ubiquitous sensing, characterized in that, Includes the following steps: Ubiquitous sensing: Relying on the global satellite navigation system terminals in the city's existing infrastructure as sensing nodes, a wide-area distributed monitoring network is constructed through optimized networking to collect multi-source heterogeneous satellite navigation monitoring data; Interference monitoring: Based on multi-source heterogeneous satellite navigation terminal data, interference signals are detected, identified, and located to determine the type and location of the interference source; The detection of interference signals specifically employs a spoofing signal joint detection method that combines phase difference detection and carrier Doppler variance detection. This method is used to detect interference in satellite signals acquired by a single sensing node. The specific implementation process is as follows: Real-time acquisition of carrier phase data and Doppler frequency offset data of GNSS signals from a single sensing node; Based on the collected carrier phase data and Doppler frequency offset data, preliminary interference detection is performed using a phase difference detector and a Doppler variance detector, respectively, and the respective decision results are output. The first cumulative average of the differences between the most recent M decision results of the phase difference detector and the global decision result is calculated, as well as the second cumulative average of the differences between the most recent M decision results of the Doppler variance detector and the global decision result. The global decision result refers to the consistent decision conclusion of all sensing nodes in the network regarding the satellite signal at the current moment. Specifically, the majority vote result of the decision results of all nodes that detected the satellite signal within the most recent time window is taken as the global decision. That is, if more than half of the nodes' decision results are "spoofing interference exists", then the global decision result is "spoofing interference exists", otherwise it is "no spoofing interference". Determine whether the first cumulative average and the second cumulative average exceed the preset threshold: If both the first cumulative average and the second cumulative average exceed the preset threshold, the decision result corresponding to the larger cumulative average of the two detectors will be used as the final output. If the decision results of the two detectors are consistent, the consistent decision result will be output directly. If either the first cumulative average or the second cumulative average exceeds a preset threshold, then the detector corresponding to the threshold is selected for detection, and its decision result is output. If neither the first cumulative average nor the second cumulative average exceeds the preset threshold, the decision results of the two detectors are fused by a weighted voting method. The weights are determined by the historical detection accuracy of each detector. Let the historical detection accuracy of the phase difference detector be P1 and the historical detection accuracy of the Doppler variance detector be P2. The normalized weights are w1=P1 / (P1+P2) and w2=P2 / (P1+P2), respectively. The decision results of the two detectors at the current time are weighted and voted. If the weighted voting value exceeds 0.5, it is determined that there is deception interference; otherwise, it is determined that there is no deception interference. The identification of interference signals specifically involves: collecting multi-source heterogeneous data from all sensing nodes in the network, extracting interference features, and identifying and classifying suppression interference, location-based deception interference, and timing-based deception interference based on classical algorithms or artificial intelligence methods. The specific method for locating the interference signal is as follows: time difference measurement is performed by combining the adaptive filtering recursive least squares algorithm with the generalized cross-correlation method, and the coordinates of the interference source are calculated by jointly using the Chan algorithm and the Taylor algorithm. Situation generation: Based on the type and location of the interference source, combined with the geographic information system, generate and display a multi-dimensional real-time threat situation, and trigger an early warning when the threat level reaches a preset threshold.

2. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 1, characterized in that, The ubiquitous sensing process specifically includes: Establish a node resource database containing several sensing nodes based on satellite navigation terminals in the city's existing infrastructure; Based on the node resource database, satellite navigation monitoring data is collected, and the data is aggregated and transmitted to the data center via the public communication network or the node's internal dedicated communication network. The data collected by the sensing nodes are preprocessed, redundancy is removed, and the data is classified and arranged to optimize the node and network structure and flexibly build a wide-area hierarchical distributed monitoring network.

3. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 2, characterized in that, The types of sensing nodes mainly include: Static nodes: Satellite navigation terminals associated with critical infrastructure, including power systems and communication base stations; Dynamic nodes: Satellite navigation terminals attached to mobile devices, including buses, private cars, and personal mobile phones.

4. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 2 or 3, characterized in that, The ubiquitous sensing also includes dynamically adjusting and evaluating the wide-area hierarchical distributed monitoring network, which is used to dynamically adjust the wide-area hierarchical distributed monitoring network when sensing nodes fail or there are coverage blind spots, so as to ensure network stability.

5. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 2 or 3, characterized in that, The process of locating the interference source is as follows: The core node in the wide-area hierarchical distributed monitoring network is selected as the master receiver, and the remaining nodes are selected as auxiliary receivers. The adaptive filtering recursive least squares algorithm combined with the generalized cross-correlation method is used to estimate the cross-correlation of the interference signals received by the master receiver and auxiliary receivers, and to determine the TDOA time difference measurement value between each auxiliary receiver and the master receiver. Substitute the TDOA time difference measurement value into the hyperbolic positioning equation, use the Chan algorithm to calculate the initial coordinates of the interference source, and use the initial coordinates as the input value to the Taylor algorithm for iterative calculation to optimize the positioning accuracy and output the two-dimensional or three-dimensional coordinates of the interference source. If there are multiple interference sources, the number and type of interference signals are identified by the signal correlation diagram, and the TDOA value of each interference source is calculated and the location is completed.

6. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 1, 2, or 3, characterized in that, The situation generation specifically includes: Based on the Gaussian regression process algorithm, and combined with the location coordinates of the interference source, digital elevation and elevation gradient data, an interference heatmap is generated. Based on the equivalent carrier-to-noise ratio calculation model, the impact of interference on positioning and timing errors is evaluated, and the threat level is classified according to the magnitude of the error. The interference heatmap and the threat level are overlaid with the geographic information system to provide a multi-dimensional situational visualization, and an early warning is automatically triggered when the threat level reaches medium or above.

7. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 6, characterized in that, The specific process for generating the interference heatmap is as follows: Collect interference identification results, interference source location coordinates, regional digital elevation data, elevation gradient data, and multi-source heterogeneous spectrum information; The set of observation points is defined as a four-dimensional vector consisting of two-dimensional spatial location, digital elevation, and elevation gradient. The covariance matrix between different observation points and the point to be predicted is calculated using an asymmetric kernel function. Based on the main path iteration idea, the interference electromagnetic radiation field map is refined and constructed using sparse monitoring data through a Gaussian regression process algorithm, generating a high-precision interference heat map.

8. The wide-area monitoring method for satellite navigation interference based on ubiquitous sensing according to claim 6, characterized in that, The equivalent carrier-to-noise ratio calculation model is as follows: in, The equivalent carrier-to-noise ratio of a GNSS receiver under interference conditions. The carrier-to-noise ratio under interference-free conditions. This is the ratio of internal interference to received signal power. For pseudocode rate, This is the anti-interference quality factor.

9. A wide-area monitoring system for satellite navigation interference based on ubiquitous sensing, characterized in that, The satellite navigation interference wide-area monitoring system, comprising any one of claims 1 to 8, performs the method described in any one of claims 1 to 8 for such monitoring. The sensing node networking module is used to schedule and manage various global satellite navigation system terminals deployed in urban infrastructure to form a ubiquitous sensing network. It also optimizes the networking to form a wide-area hierarchical distributed monitoring network to collect multi-source heterogeneous satellite navigation monitoring data. The data processing module is communicatively connected to the sensing node networking module, and is used to receive the multi-source heterogeneous satellite navigation monitoring data, and to detect, identify and locate satellite navigation interference, and determine the type and location of the interference source. The threat situation generation module is communicatively connected to the data processing module. It is used to generate and display a multi-dimensional real-time threat situation based on the location and type of the interference source and in conjunction with the geographic information system, and to trigger an early warning when the threat level reaches a preset threshold.

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