Passive radar target positioning method and system based on external radiation source

Abstract

The application discloses a passive radar target positioning method and system based on external radiation sources, and relates to the technical field of radar positioning. The method comprises the following steps: screening external radiation source signals of a target monitoring area, and arranging a distributed receiving antenna array based on the signals; real-time receiving signals and transmitting the signals to a quality assessment unit; the quality assessment unit adaptively screens the signals, and outputs standard radiation source signals with radiation source type identification; characteristic parameters of the standard signals are extracted, a multivariate signal feature set is obtained, and multi-band signal fusion and target positioning analysis are carried out based on the feature set, so as to generate target position information. The application solves the technical problems of complex signal processing, insufficient positioning accuracy and limited dynamic monitoring capability of existing passive radar technology in a complex electromagnetic environment, achieves the technical effect of improving the positioning accuracy and dynamic monitoring capability of passive radar in a complex environment through multi-band signal fusion and adaptive signal processing.

Description

Technical Field

[0001] This invention relates to the field of radar positioning technology, specifically to a passive radar target positioning method and system based on an external radiation source. Background Technology

[0002] In the field of modern radar technology, passive radar detects and locates targets by receiving signals from external radiation sources (such as broadcast television, communication base stations, satellites, etc.). It has advantages such as strong concealment, low power consumption, and flexible deployment, and is widely used in security monitoring, traffic management and other scenarios.

[0003] However, passive radar technology also faces some challenges. First, the characteristics of external radiation source signals (such as frequency, power, modulation method, etc.) are diverse, and the propagation characteristics of the signals are greatly affected by the environment, increasing the complexity of signal processing. Second, passive radar relies on external signals, and the availability and stability of the signals are crucial to the system's performance. How to extract effective information from multiple external radiation source signals and perform signal fusion to improve positioning accuracy is also a key problem that passive radar technology needs to solve. Summary of the Invention

[0004] This application provides a passive radar target localization method and system based on an external radiation source, which solves the technical problems of existing passive radar technology in complex electromagnetic environments, such as complex signal processing, insufficient positioning accuracy, and limited dynamic monitoring capabilities.

[0005] The first aspect of this application provides a passive radar target localization method based on external radiation sources. The method includes: selecting and determining a set of external radiation source signals for a target monitoring area; deploying a receiving antenna array based on the external radiation source signal set, the receiving antenna array comprising multiple signal receiving points, and the multiple signal receiving points employing a distributed network structure; receiving external radiation source signals in real time based on the multiple signal receiving points and transmitting them to a quality assessment unit; the quality assessment unit performing adaptive signal data filtering based on a periodic external radiation source signal sequence, outputting a standard radiation source signal, the standard radiation source signal carrying a radiation source type identifier; extracting feature parameters from the standard radiation source signal to obtain a multi-dimensional signal feature set; performing multi-band signal fusion based on the multi-dimensional signal feature set, and performing target localization analysis based on the fused signal features to generate target location information.

[0006] A second aspect of this application provides a passive radar target positioning system based on external radiation sources. The system includes: an external radiation source determination module for filtering and determining a set of external radiation source signals for a target monitoring area; a receiving antenna array deployment module for deploying a receiving antenna array based on the external radiation source signal set, the receiving antenna array comprising multiple signal receiving points in a distributed network structure; a signal receiving module for receiving external radiation source signals in real time based on the multiple signal receiving points and transmitting them to a quality assessment unit; an adaptive data filtering module for the quality assessment unit to adaptively filter signal data according to a periodic sequence of external radiation source signals and output a standard radiation source signal, the standard radiation source signal carrying a radiation source type identifier; a feature parameter extraction module for extracting feature parameters from the standard radiation source signal to obtain a multi-dimensional signal feature set; and a target positioning analysis module for performing multi-band signal fusion based on the multi-dimensional signal feature set and performing target positioning analysis based on the fused signal features to generate target location information.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This application provides a passive radar target localization method and system based on external radiation sources, relating to the field of radar localization technology. By screening the external radiation source signal set of the target monitoring area, deploying a distributed receiving antenna array, receiving signals in real time and performing adaptive screening, outputting standard radiation source signals and extracting feature parameters, performing multi-band signal fusion and target localization analysis, and generating target location information, this method solves the technical problems of complex signal processing, insufficient positioning accuracy, and limited dynamic monitoring capabilities of existing passive radar technology in complex electromagnetic environments. It achieves the technical effect of improving the positioning accuracy and dynamic monitoring capabilities of passive radar in complex environments through multi-band signal fusion and adaptive signal processing. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic flowchart of a passive radar target localization method based on an external radiation source is provided for an embodiment of this application;

[0011] Figure 2 This is a schematic diagram of a passive radar target positioning system based on an external radiation source, provided as an embodiment of this application.

[0012] Explanation of reference numerals in the attached diagram: 11 External radiation source determination module, 12 Receiving antenna array deployment module, 13 Signal receiving module, 14 Adaptive data filtering module, 15 Feature parameter extraction module, 16 Target positioning analysis module. Detailed Implementation

[0013] This application provides a passive radar target localization method and system based on an external radiation source, which solves the technical problems of existing passive radar technology in complex electromagnetic environments, such as complex signal processing, insufficient positioning accuracy, and limited dynamic monitoring capabilities.

[0014] The technical solutions of the embodiments of this application 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 this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] Example 1, as Figure 1 As shown, this application provides a passive radar target localization method based on an external radiation source, the method comprising:

[0017] P10: For the target monitoring area, screen and determine the set of external radiation source signals.

[0018] Specifically, the first step is to screen and identify external radiation source signals for the target monitoring area. External radiation sources refer to external signal sources that can provide effective signals within the target area. These signal sources may come from different broadcasting, communication, or satellite systems, including but not limited to DTMB (Digital Terrestrial Television Broadcasting), DVB (Digital Video Broadcasting), FM broadcasting, high-power orbital internet satellite signals, and 5G communication signals.

[0019] First, defining the target monitoring area involves analyzing factors such as the spatial extent, geographical characteristics, climate conditions, and building obstruction of the target area. Detailed surveys and studies of the area can determine the effective coverage of external radiation source signals within it. The selection of this area directly affects the subsequent signal reception quality and the accuracy of target positioning.

[0020] Next, the external radiation source signal set is screened. This process requires evaluating the suitability of different signal sources based on several key factors, including signal coverage, signal strength, spectral stability, and the interference environment of the target area. For example, DTMB and DVB signals are commonly used in terrestrial digital television and broadcasting, offering wide coverage, but their signals may be attenuated by factors such as buildings and weather. FM broadcast signals, on the other hand, are widely used in radio broadcasting, offering good signal propagation stability and are suitable for locating targets at medium to short distances. High-power internet satellite signals and 5G signals, in particular, offer wide coverage, especially orbital satellite signals which can traverse large geographical areas and have strong penetrating power, effectively providing long-distance, long-term continuous signals. This has significant advantages in large-scale monitoring and tracking of high-speed moving targets.

[0021] In addition to considering the aforementioned technical parameters, signal selection also requires analysis of the signal source's frequency characteristics. Different signal sources may have varying impacts on target positioning accuracy. For example, low-frequency signals (such as FM radio) have strong penetration but narrow bandwidth, making them suitable for long-distance propagation; while high-frequency signals (such as 5G signals) have wider bandwidth, providing higher positioning accuracy, but their propagation range is relatively limited. Therefore, when selecting a signal source, it is necessary to weigh its suitability based on the needs of the monitoring area and the target's motion characteristics.

[0022] This screening process comprehensively considers signal propagation characteristics, signal strength, frequency characteristics, and interference factors within the target area to select a suitable set of external radiation source signals. This signal set will serve as input signals in subsequent steps, providing high-quality, stable signal data to support accurate target localization and tracking tasks.

[0023] P20: A receiving antenna array is deployed based on the external radiation source signal set. The receiving antenna array includes multiple signal receiving points, and the multiple signal receiving points adopt a distributed network structure.

[0024] Optionally, based on the aforementioned set of external radiation source signals, a receiving antenna array is planned and deployed. The receiving antenna array is an array of multiple antenna elements responsible for receiving signals reflected or scattered from external radiation sources within the target monitoring area. To ensure the comprehensiveness and high quality of the received signals, the spatial layout of the monitoring area and the propagation characteristics of different external radiation source signals must be fully considered when deploying the antenna array.

[0025] In practice, the layout of the receiving antenna array needs to be rationally planned based on the topography, electromagnetic environment, and propagation characteristics of external radiation source signals in the target monitoring area. In urban environments, buildings and terrain may obstruct and reflect signal propagation; therefore, the layout of receiving points should avoid signal-obstructing areas as much as possible and be optimized according to the direction and intensity distribution of signal propagation. For example, for medium-Earth orbit high-power internet satellite signals, receiving points should be preferentially placed in open areas to ensure the reception of weak signals from high altitudes; while for 5G signals, due to their higher frequency band and relatively smaller coverage area, the layout of receiving points should be more dense to ensure continuous signal reception.

[0026] Next, the receiving antenna array comprises multiple signal receiving points, and these receiving points employ a distributed network structure. In this distributed structure, the receiving points are no longer clustered in a single location as in a traditional centralized structure, but rather distributed across different locations within the target monitoring area via wireless or wired networks. This distributed deployment not only expands the signal reception coverage but also effectively reduces weak signal areas caused by terrain obstruction, building barriers, and other factors. The distributed structure also offers greater adaptability, enabling the reception of the same signal from different angles through multiple receiving points, thus enhancing the reliability of signal reception.

[0027] Furthermore, due to their different locations, each receiving point in the distributed network structure receives signals with varying time delays and frequency offsets. By comparing signals from different receiving points, the propagation path of the signal can be accurately estimated, thereby effectively deducing the precise location of the target. Since the signal received at each receiving point experiences different time delays, frequency variations, and multipath effects during propagation, the system can utilize this difference information for precise target localization analysis. Through this arrangement, the entire receiving network can work collaboratively to continuously and accurately locate and track the target as it moves.

[0028] In actual deployment, the receiving points of the antenna array are interconnected through a distributed network structure and achieve synchronous data transmission via network protocols. Each receiving point transmits the received signal to a centralized processing unit or collaborative processing system in real time, where the signal information received by each receiving point is analyzed through collaborative processing. This distributed receiving method can significantly improve the system's anti-interference capability, especially in the face of local signal attenuation or interference, other receiving points can still provide effective signal data, thereby ensuring the stability and reliability of the system.

[0029] P30: Based on the multiple signal receiving points, receive signals from external radiation sources in real time and transmit them to the quality assessment unit.

[0030] Specifically, signals from external radiation sources are received in real time from multiple signal receiving points, and the received signals are transmitted to the quality assessment unit. This process involves not only signal acquisition and transmission, but also preliminary control over signal integrity and availability, laying the foundation for subsequent signal processing and target location analysis.

[0031] First, multiple signal receiving points work collaboratively in a distributed network structure to receive signals from external radiation sources in real time. These receiving points are distributed throughout the target monitoring area, and their layout is carefully designed to ensure coverage of the entire monitoring area and effective reception of selected sets of external radiation sources, such as DTMB / DVB digital television signals, FM radio signals, medium-Earth orbit high-power internet satellite signals, and 5G signals. Each receiving point is equipped with high-performance antennas and receiving equipment capable of adapting to signals with different frequency bands and propagation characteristics, ensuring stable signal reception.

[0032] After acquiring signals from an external radiation source, the signal receiving point must immediately transmit the signals to the quality assessment unit. This transmission process requires high efficiency and low latency to ensure real-time signal transmission. Therefore, the signal receiving point and the quality assessment unit are typically connected via a high-speed data transmission network. This network can support the rapid transmission of large amounts of data and guarantee the integrity and accuracy of the signal during transmission. Various technologies may be employed during signal transmission, such as fiber optic communication and wireless communication; the specific choice depends on the environmental conditions of the monitoring area and cost-effectiveness analysis.

[0033] The quality assessment unit, as a key link in the signal processing flow, is responsible for performing a preliminary quality assessment on the received signals from external radiation sources. This is to screen out high-quality signals and eliminate low-quality signals caused by interference, noise, or transmission loss, thereby improving the efficiency and accuracy of subsequent signal processing and providing a reliable data source for subsequent signal data screening and feature extraction.

[0034] In practice, the real-time reception function of a signal receiving point needs to possess high stability and reliability. This requires not only excellent hardware performance from the signal receiving equipment but also optimization of the signal reception process through software algorithms. For example, the receiving equipment can use adaptive gain control technology to automatically adjust the receiving gain to adapt to signals of different intensities, thereby improving the dynamic range of signal reception and ensuring the quality of the received signal.

[0035] Furthermore, the signal transmission to the quality assessment unit also requires rigorous quality control. During transmission, the signal may be affected by factors such as electromagnetic interference, transmission loss, or network latency. Therefore, error correction coding and data verification methods can be employed in the signal transmission link to ensure the integrity and accuracy of the signal during transmission. Simultaneously, the quality assessment unit monitors the received signal in real time. Once an anomaly in signal quality is detected, it can promptly report it to the signal receiving point so that appropriate adjustment measures can be taken, such as adjusting the direction or gain of the receiving antenna. This process not only ensures the real-time nature of signal acquisition but also effectively filters out low-quality signals through the quality assessment unit, ensuring the efficiency and accuracy of subsequent analysis and processing.

[0036] P40: The quality assessment unit performs adaptive signal data filtering based on the periodic external radiation source signal sequence and outputs a standard radiation source signal, which carries a radiation source type identifier.

[0037] Furthermore, step P40 in this embodiment of the application also includes:

[0038] P41: The quality assessment unit periodically samples the external radiation source signals according to the type of external radiation source to obtain external radiation source signal sequences for different radiation sources; P42: Multivariate signal quality assessment is performed on the external radiation source signal sequences, and signal priorities are generated based on the quality assessment results; P43: The external radiation source signals are filtered and extracted according to the signal priorities to output standard radiation source signals.

[0039] It should be understood that after receiving a periodic external radiation source signal sequence, the quality assessment unit performs adaptive signal data filtering on the received signals and outputs standard radiation source signals that meet the requirements in order to improve signal quality and ensure that subsequent target positioning and data processing can be based on stable and clear signals.

[0040] First, the quality assessment unit performs periodic sampling based on the periodic characteristics of the external radiation source signals. Different external radiation sources (such as DTMB, DVB, FM radio, 5G signals, etc.) have their own specific signal periods, exhibiting periodic regularity over time. Therefore, the quality assessment unit needs to perform periodic sampling for each type of radiation source signal, capturing its periodic variations and forming an external radiation source signal sequence. Each type of radiation source signal has its specific periodicity and transmission characteristics; therefore, different sampling frequencies and periods need to be used for different types of signals to ensure the comprehensiveness and accuracy of the sampled data.

[0041] Next, after obtaining the external radiation source signal sequences from different sources, the quality assessment unit further performs multivariate signal quality assessment on these signals. Since different external radiation source signals may be subject to varying degrees of interference during reception, such as signal attenuation, noise contamination, and multipath effects, the quality assessment unit analyzes multiple quality indicators, including signal-to-noise ratio (SNR), distortion, and stability, to score all received signals based on these indicators. The scores are then weighted according to the importance of each indicator to generate the quality assessment result. The purpose of this process is to identify and eliminate signals with poor quality, ensuring that subsequent signal processing is based on high-quality data.

[0042] After the multi-signal quality assessment is completed, the quality assessment unit assigns a priority to each signal based on the assessment results. The priority is set according to the signal's quality score; for example, the higher the signal-to-noise ratio and the lower the distortion, the higher the priority. The generation of signal priorities helps the system dynamically adjust the use of signals based on quality. For example, signals with high signal-to-noise ratio and low interference can be prioritized for target localization, while signals with poor quality can be ignored or assigned a lower priority.

[0043] Finally, based on signal priority, external radiation source signals are filtered and extracted to output standard radiation source signals. The filtering and extraction process is based on signal priority, prioritizing the retention of higher-priority signals and discarding lower-priority signals, ensuring the high quality and reliability of the final output signal. The output standard radiation source signals not only undergo rigorous quality screening but also include radiation source type identifiers. These identifiers facilitate subsequent classification and processing based on signal origin. Radiation source type identifiers can include information such as "DTMB," "DVB," and "5G," helping the system perform specific analysis and processing based on different signal types.

[0044] Through the above steps, the quality assessment unit can effectively filter out high-quality standard radiation source signals from a large number of external radiation source signals, providing reliable data support for subsequent target location analysis. This process not only improves the efficiency of signal processing but also enhances the adaptability and stability of the passive radar system in complex electromagnetic environments.

[0045] P50: Extract feature parameters from the standard radiation source signal to obtain a multivariate signal feature set.

[0046] Furthermore, step P50 in this embodiment of the application also includes:

[0047] P51: Configure the accuracy threshold for signal parameter extraction; P52: Based on the accuracy threshold, extract the arrival time parameter, arrival direction parameter, and arrival frequency parameter of the standard radiation source signal respectively, and record the Doppler frequency shift characteristics to obtain the multivariate signal feature set.

[0048] Specifically, after quality assessment and screening, the standard radiation source signals will enter the feature parameter extraction stage. This process aims to extract key parameters that reflect the target's characteristics and motion state from the screened and optimized standard radiation source signals, forming a multi-dimensional signal feature set, which provides a data foundation for subsequent target localization analysis.

[0049] First, the accuracy threshold for signal parameter extraction needs to be configured. Setting the accuracy threshold requires comprehensive consideration of the system's design goals, signal quality, environmental interference, and the target positioning accuracy requirements. For example, in security applications, the accuracy requirements for target positioning are extremely high; therefore, the accuracy threshold should be set more strictly to ensure that the extracted feature parameters meet the requirements for high-precision positioning. In civilian surveillance scenarios, although the requirements for positioning accuracy are relatively lower, it is still necessary to set the accuracy threshold reasonably according to actual needs to ensure the effective operation of the system.

[0050] Configuring accuracy thresholds involves setting multiple parameters, including time resolution, angular resolution, frequency resolution, and the measurement accuracy of Doppler shift. Time resolution determines the accuracy of the arrival time parameter extraction, angular resolution affects the accuracy of the arrival direction parameter, and frequency resolution is closely related to the extraction quality of the arrival frequency parameter. The measurement accuracy of Doppler shift directly affects the accuracy of target velocity information. By properly configuring these accuracy thresholds, it can be ensured that the feature parameter extraction process meets the system's positioning accuracy requirements while possessing sufficient robustness to cope with signal changes in complex environments.

[0051] Next, feature extraction is performed on the standard radiation source signal based on a set accuracy threshold. First, the time of arrival (TOA) parameter is extracted by measuring the time difference of the signal arriving at different receiving antennas. By accurately measuring the TOA, the relative distance between the target and each receiving point can be calculated using the polygonal measurement method or the TDOA algorithm, thus providing crucial distance information for location analysis. The direction of arrival (DOA) parameter is extracted by measuring the incident direction of the signal. This typically requires the receiving antenna to have a certain directionality, and the incident angle of the signal must be calculated using signal processing algorithms. The extraction of the frequency of arrival (FCA) parameter involves analyzing the signal's frequency characteristics. By measuring parameters such as the carrier frequency and modulation frequency, information on the frequency changes of the signal during propagation can be obtained.

[0052] In addition to these basic parameters, it is also necessary to record the Doppler frequency shift characteristics. This characteristic, by analyzing the frequency changes of the signal, can provide more accurate information about the target's velocity. The Doppler frequency shift is generated by the relative motion of the target with respect to the receiving device, and it is a direct reflection of the target's velocity. Real-time acquisition and recording of Doppler frequency shift characteristics during target localization and tracking can improve the ability to analyze and predict the target's motion state.

[0053] During the extraction of the aforementioned feature parameters, each parameter needs to be evaluated for quality based on a pre-configured accuracy threshold. If the extracted feature parameter meets the accuracy threshold requirement, it is included in the multivariate signal feature set; if some parameters fail to meet the accuracy threshold, corresponding measures need to be taken for optimization or re-extraction to ensure that the final multivariate signal feature set can meet the needs of target localization analysis.

[0054] P60: Based on the multi-signal feature set, perform multi-band signal fusion, and based on the fused signal features, perform target positioning analysis to generate target location information.

[0055] Furthermore, based on the aforementioned multi-band signal feature set, multi-band signal fusion is performed. In this embodiment, step P60 further includes:

[0056] P61: Establish a spatiotemporal alignment model for signal features, compensate for the propagation delay differences of signals in different frequency bands for the multivariate signal feature set, and generate a reference signal feature set; P62: Assign fusion weights according to the signal-to-noise ratio of each frequency band signal, perform multi-band signal fusion on the reference signal feature set, and construct a joint feature matrix. The joint feature matrix contains a three-dimensional feature vector, which includes an arrival time vector, an arrival direction vector, and an arrival frequency vector.

[0057] Optionally, based on a multi-band signal feature set, multi-frequency signal fusion is performed to generate fused signal features, and these features are used for target localization analysis to ultimately generate the target's location information.

[0058] First, a spatiotemporal alignment model for signal characteristics needs to be established. Because signals in different frequency bands may have different propagation speeds and paths, their alignment in time and space will differ. For example, high-frequency signals (such as 5G signals) have greater propagation loss and may require more complex propagation models to describe their propagation characteristics; while low-frequency signals (such as FM radio signals) have stronger penetration capabilities and longer propagation distances. Therefore, a spatiotemporal alignment model is needed to compensate for these differences.

[0059] The establishment of a spatiotemporal alignment model involves the precise measurement and compensation of signal propagation delay. Specifically, the propagation delay can be calculated by measuring the time difference of the signal arriving at different receiving points and combining this with information on the signal's propagation speed and path. Then, by adjusting the signal's time reference, the characteristic parameters of signals from different frequency bands are aligned in time. For spatial alignment, the position and orientation of the receiving antennas can be calibrated to ensure the spatial consistency of the characteristic parameters of signals from different frequency bands. Through spatiotemporal alignment processing, a reference signal feature set is generated, providing a unified foundation for subsequent signal fusion.

[0060] Next, fusion weights are assigned based on the signal-to-noise ratio (SNR) of each frequency band signal. Signals with high SNR generally have better quality and contribute more to target localization, therefore they should be assigned higher weights; while signals with low SNR have poorer quality and contribute less to localization, so their weights should be reduced accordingly. The SNR can be calculated based on the strength of the received signal and the noise level, accurately assessing the quality of the signal in each frequency band.

[0061] Then, multi-band signal fusion is performed on the reference signal feature set to construct a joint feature matrix. The joint feature matrix is ​​a comprehensive data structure containing multi-band signal feature information, capable of integrating the feature parameters of signals from different frequency bands. In this application, the joint feature matrix contains three-dimensional feature vectors, including a time-of-arrival vector, a direction-of-arrival vector, and a frequency-of-arrival vector. The time-of-arrival vector reflects the relative distance information between the target and each receiving point; the direction-of-arrival vector provides the directional information of the target relative to the receiving antenna; and the frequency-of-arrival vector contains the frequency characteristic information of the signal, such as Doppler shift. By integrating these feature vectors into the joint feature matrix, the target's motion state and position information can be comprehensively described.

[0062] Finally, target localization analysis is performed based on the fused joint feature matrix. By employing multi-point localization algorithms, least squares methods, and other techniques, combined with the spatiotemporal characteristics of the signal, the precise location of the target can be calculated, and its position information generated. Through this series of steps, the passive radar system can fully leverage the advantages of multi-band signals, significantly improving the accuracy and reliability of target localization and meeting the positioning needs in complex environments.

[0063] Furthermore, based on the fused signal characteristics, target localization analysis is performed. Step P60 in this embodiment of the application also includes:

[0064] P63: Based on the multi-source signal feature set and the spatial layout of the corresponding signal receiving points, the target position is calculated to obtain the target position information; P64: Based on the multi-source signal feature set, the target's motion state is inferred to obtain the target's moving speed information; P65: Using the target position information and target moving speed information, the target is dynamically tracked.

[0065] In one possible embodiment of this application, the process of target localization analysis based on fused signal features can be further refined. Target localization is performed based on a multi-source signal feature set, combined with the spatial layout of the receiving points, while simultaneously calculating the target's motion state and speed to achieve high-precision dynamic target tracking.

[0066] First, based on the obtained multivariate signal feature set, the system needs to calculate the target location by considering the spatial distribution of the signal receiving points. Since the distribution of multiple receiving points within the target monitoring area varies spatially, the signals received by each receiving point carry different time delay, direction, and frequency characteristics. Therefore, by comparing and analyzing these signal characteristics, the spatial location of the target can be deduced. This process utilizes information such as the time of arrival vector, direction of arrival vector, and frequency of arrival vector in the joint feature matrix, combined with the specific spatial distribution of the signal receiving points within the target monitoring area. Through mathematical models and optimization algorithms, such as multilateration, time difference of arrival (TDOA), or angle of arrival (AOA), the target's position coordinates are accurately calculated, and the target location information is output.

[0067] Furthermore, based on a multivariate signal feature set, the target's motion state is inferred, and its velocity information is obtained. This step utilizes signal features such as Doppler frequency shift characteristics, combined with target position information, to calculate the target's velocity. Doppler frequency shift is a frequency change caused by the relative motion between the target and the radiation source. By accurately measuring the Doppler frequency shift and combining it with a signal propagation model, the target's velocity and direction can be calculated. In addition, the target's position changes over continuous time can be used to calculate its velocity vector through methods such as differential calculus, thus providing instantaneous velocity information.

[0068] Finally, target position and velocity information are used for dynamic target tracking. By continuously collecting target position and velocity information, the system can update the target's state in real time and make predictions. Target dynamic tracking algorithms (such as Kalman filtering and particle filtering) use known target position and velocity information to predict the target's future trajectory. This not only helps the system maintain high tracking accuracy during short-term dynamic changes in the target, but also enables it to cope with situations where the target suddenly changes its trajectory, ensuring the system can flexibly respond to complex dynamic environments. This process, while ensuring high accuracy, also improves the system's responsiveness to dynamic changes, making it suitable for real-time monitoring and precise positioning of various high-speed moving targets.

[0069] Furthermore, step P63 in this embodiment of the application also includes:

[0070] P63-1: Based on the multivariate signal feature set, perform time difference of arrival positioning and establish a hyperbolic equation system; P63-2: Based on the hyperbolic equation system and combined with the angle of arrival measurement results, construct a hybrid positioning model, solve and output the target position estimate in the three-dimensional coordinate system, the target position estimate includes longitude, latitude and altitude information.

[0071] Specifically, the process of calculating the target position can be further refined. By combining the results of time difference of arrival positioning and angle of arrival measurement, high-precision three-dimensional position estimation of the target can be achieved.

[0072] First, time-of-arrival (TDOA) positioning is performed based on the time-of-arrival parameters from a multivariate signal feature set. By analyzing the time differences in signals received at different receiving points, the distance differences between the target and each receiving point can be calculated. For example, based on TDOA technology, there are slight differences in the time required for a signal to travel from the target to each receiving point. These time differences can be used to calculate the target's position relative to the receiving points. In practice, the distance differences calculated from these time differences can be used to establish a set of hyperbolic equations. These equations describe the geometric relationship between the target and multiple receiving points, where each equation corresponds to a fixed distance difference between a receiving point and the target. By solving these hyperbolic equations, the target's position relative to multiple receiving points can be determined.

[0073] Next, by combining the angle of arrival (AHA) measurement results, the positioning accuracy is further improved by constructing a hybrid positioning model. The AHA measurement results provide information about the target's azimuth, effectively supplementing the distance information provided by time difference positioning (TDOS). By combining the AHA (e.g., through angle measurement using an antenna array or calculation using the phase difference of multiple receivers), positioning errors can be further reduced, and more accurate 3D positioning data can be provided. At this point, based on the hyperbolic equations and AHA information, a comprehensive hybrid positioning model can be constructed. This model integrates time difference and angle information, thereby improving the accuracy of target positioning.

[0074] Finally, by solving the hybrid positioning model, an estimated target position in a three-dimensional coordinate system is output. This estimated position includes longitude, latitude, and altitude information, accurately describing the target's location in space. The solution process can utilize numerical optimization algorithms, such as least squares or other iterative algorithms, to ensure the accuracy and reliability of the results. By combining time difference of arrival (TDOA) and angle of arrival (ACO) measurements, the hybrid positioning model fully leverages the advantages of both positioning methods, overcoming the limitations of a single method, thereby achieving high-precision three-dimensional target position estimation in complex environments.

[0075] This process fully leverages the advantages of multi-source signal characteristics, and combined with advanced mathematical models and algorithms, it can significantly improve the accuracy and reliability of target positioning, providing solid technical support for passive radar systems to monitor and track targets in complex environments.

[0076] Furthermore, step P64 in this embodiment of the application also includes:

[0077] P64-1: Based on the multivariate signal feature set, extract continuous frame signal features and calculate the target radial velocity using Doppler frequency shift; P64-2: Analyze the target's position change trajectory and calculate the target horizontal velocity and target vertical velocity; P64-3: Fuse the target radial velocity, target horizontal velocity, and target vertical velocity to output the target three-dimensional velocity vector, which contains velocity magnitude and direction information.

[0078] It should be understood that the calculation process of the target's motion state can be further refined. By combining Doppler frequency shift calculation and target position change trajectory analysis, the target's three-dimensional velocity vector can be accurately obtained.

[0079] First, continuous frame signal features are extracted from a multi-source signal feature set, and combined with Doppler frequency shift, the radial velocity of the target is calculated. Continuous frame signal features refer to extracting changes in the signal related to the target's relative motion by comparing multiple frames of signal data. Since the target's motion causes changes in the signal frequency (i.e., Doppler frequency shift), the system can deduce the target's radial velocity, i.e., the relative velocity between the target and the receiving device, by analyzing the frequency changes. For example, continuous monitoring and analysis of the signal frequency extracts the Doppler frequency shift features of the signal. By accurately measuring the Doppler frequency shift and combining it with the signal propagation speed and frequency, the target's radial velocity can be calculated.

[0080] Next, the target's position change trajectory is further analyzed, and the horizontal and vertical velocities are calculated based on the target's movement path. The position change trajectory can be obtained by tracking and calculating the target's position information at multiple time points, and can be extrapolated from the target's multiple positioning data. By analyzing the changes in the target's position, and combining appropriate filtering and interpolation algorithms, the target's velocity components in the horizontal and vertical directions are calculated, namely, horizontal velocity and vertical velocity. Horizontal velocity reflects the target's speed along the horizontal plane of the ground, while vertical velocity reflects the target's speed in the vertical direction (such as changes in flight altitude). These two velocity components are crucial for a comprehensive understanding of the target's motion state, especially for positioning and tracking in three-dimensional space.

[0081] Finally, the radial, horizontal, and vertical velocities of the target are fused to output a three-dimensional velocity vector. This three-dimensional velocity vector contains not only the target's velocity information in each direction, but also the magnitude and direction of that velocity. By combining these three velocity components, the target's total velocity can be accurately calculated, and its direction of motion can be determined. Fusing velocity components from multiple directions provides a more accurate reflection of the target's actual trajectory and velocity changes. This fusion process requires consideration of the weighting of each velocity component to ensure the accuracy and reliability of the three-dimensional velocity vector.

[0082] In summary, this step extracts and calculates the target's radial, horizontal, and vertical velocities, then fuses this velocity information to output the target's three-dimensional velocity vector. This process not only enables real-time monitoring of the target's motion state but also provides accurate velocity data for dynamic prediction and path planning. It fully leverages the advantages of multi-source signal characteristics, significantly improving the accuracy and reliability of target motion state estimation.

[0083] In summary, the embodiments of this application have at least the following technical effects:

[0084] This application effectively improves target positioning accuracy in complex environments by using multi-band signal fusion and multi-source signal feature set extraction, reducing the impact of signal attenuation and interference on positioning results. Simultaneously, the adoption of distributed receiving points and a signal quality assessment mechanism enhances the system's anti-interference capability, ensuring reliable target positioning. Utilizing passive radar technology, the system eliminates the need for active signal transmission, reducing the risk of system exposure and energy consumption, making it suitable for long-term operation. Furthermore, the system can flexibly respond to different external radiation source signals and dynamic environmental changes, ensuring efficient operation and improving system adaptability.

[0085] The technology achieves the goal of improving the positioning accuracy and dynamic monitoring capability of passive radar in complex environments through multi-band signal fusion and adaptive signal processing.

[0086] Example 2 is based on the same inventive concept as the passive radar target localization method based on an external radiation source in the foregoing examples, such as... Figure 2 As shown, this application provides a passive radar target localization system based on an external radiation source. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0087] External radiation source determination module 11 is used to screen and determine the set of external radiation source signals for the target monitoring area.

[0088] The receiving antenna array deployment module 12 is used to deploy a receiving antenna array based on the external radiation source signal set. The receiving antenna array includes multiple signal receiving points, and the multiple signal receiving points adopt a distributed network structure.

[0089] The signal receiving module 13 is used to receive signals from external radiation sources in real time based on the plurality of signal receiving points and transmit them to the quality assessment unit.

[0090] An adaptive data filtering module 14 is used by the quality assessment unit to perform adaptive signal data filtering based on a periodic external radiation source signal sequence and output a standard radiation source signal, wherein the standard radiation source signal carries a radiation source type identifier.

[0091] Feature parameter extraction module 15 is used to extract feature parameters from the standard radiation source signal to obtain a multivariate signal feature set.

[0092] The target positioning analysis module 16 is used to perform multi-band signal fusion based on the multi-element signal feature set, and perform target positioning analysis based on the fused signal features to generate target location information.

[0093] Furthermore, the adaptive data filtering module 14 is also used to perform the following steps:

[0094] The quality assessment unit periodically samples the external radiation source signals according to the type of external radiation source to obtain external radiation source signal sequences for different radiation sources; performs multivariate signal quality assessment on the external radiation source signal sequences and generates signal priorities based on the quality assessment results; and filters and extracts the external radiation source signals according to the signal priorities to output standard radiation source signals.

[0095] Furthermore, the feature parameter extraction module 15 is also used to perform the following steps:

[0096] Configure a precision threshold for signal parameter extraction; based on the precision threshold, extract the arrival time parameter, arrival direction parameter, and arrival frequency parameter of the standard radiation source signal, and record the Doppler frequency shift characteristics to obtain the multivariate signal feature set.

[0097] Furthermore, the target localization analysis module 16 is also used to perform the following steps:

[0098] A spatiotemporal alignment model for signal features is established. For the multivariate signal feature set, the propagation delay differences of signals in different frequency bands are compensated to generate a reference signal feature set. According to the signal-to-noise ratio of each frequency band signal, a fusion weight is assigned, and multi-frequency band signal fusion is performed on the reference signal feature set to construct a joint feature matrix. The joint feature matrix contains a three-dimensional feature vector, which includes an arrival time vector, an arrival direction vector, and an arrival frequency vector.

[0099] Furthermore, the target localization analysis module 16 is also used to perform the following steps:

[0100] Based on the multi-source signal feature set and the spatial layout of the corresponding signal receiving points, the target position is calculated to obtain the target position information; based on the multi-source signal feature set, the target's motion state is inferred to obtain the target's moving speed information; and the target position information and target moving speed information are used to perform dynamic target tracking.

[0101] Furthermore, the target localization analysis module 16 is also used to perform the following steps:

[0102] Based on the multivariate signal feature set, time difference of arrival (TDOA) positioning is performed, and a hyperbolic equation system is established. Based on the hyperbolic equation system and combined with the angle of arrival (ACO) measurement results, a hybrid positioning model is constructed, and the estimated target position in a three-dimensional coordinate system is solved and output. The estimated target position includes longitude, latitude, and altitude information.

[0103] Furthermore, the target localization analysis module 16 is also used to perform the following steps:

[0104] Based on the multivariate signal feature set, continuous frame signal features are extracted, and the radial velocity of the target is obtained by Doppler frequency shift calculation; the target's position change trajectory is analyzed, and the target's horizontal velocity and vertical velocity are calculated; the target's radial velocity, horizontal velocity, and vertical velocity are fused to output the target's three-dimensional velocity vector, which contains velocity magnitude and direction information.

[0105] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0106] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0107] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A passive radar target localization method based on an external radiation source, characterized in that, The method includes: For the target monitoring area, a set of external radiation source signals is selected and determined; A receiving antenna array is deployed based on the external radiation source signal set. The receiving antenna array includes multiple signal receiving points, and the multiple signal receiving points adopt a distributed network structure. Based on the multiple signal receiving points, signals from external radiation sources are received in real time and transmitted to the quality assessment unit. The quality assessment unit performs adaptive signal data filtering based on the periodic external radiation source signal sequence and outputs a standard radiation source signal, which carries a radiation source type identifier. Feature parameters are extracted from the standard radiation source signal to obtain a multi-dimensional signal feature set; Multi-band signal fusion is performed based on the aforementioned multi-signal feature set, and target positioning analysis is conducted based on the fused signal features to generate target location information. Multi-band signal fusion based on the aforementioned multi-source signal feature set includes: A spatiotemporal alignment model for signal features is established, and for the multivariate signal feature set, the propagation delay differences of signals in different frequency bands are compensated to generate a reference signal feature set; Based on the signal-to-noise ratio of each frequency band signal, a fusion weight is assigned, and multi-band signal fusion is performed on the reference signal feature set to construct a joint feature matrix. The joint feature matrix contains a three-dimensional feature vector, which includes an arrival time vector, an arrival direction vector, and an arrival frequency vector.

2. The passive radar target localization method based on an external radiation source as described in claim 1, characterized in that, The quality assessment unit performs adaptive signal data filtering based on periodic external radiation source signal sequences and outputs standard radiation source signals, including: The quality assessment unit periodically samples the external radiation source signals according to the type of external radiation source to obtain external radiation source signal sequences for different radiation sources. For the signal sequence from the external radiation source, a multivariate signal quality assessment is performed, and signal priorities are generated based on the quality assessment results. Based on the signal priority, the external radiation source signals are filtered and extracted, and a standard radiation source signal is output.

3. The passive radar target localization method based on an external radiation source as described in claim 1, characterized in that, Feature parameters are extracted from the standard radiation source signal to obtain a multivariate signal feature set, including: Configure the accuracy threshold for signal parameter extraction; Based on the accuracy threshold, the arrival time parameter, arrival direction parameter, and arrival frequency parameter of the standard radiation source signal are extracted respectively, and the Doppler frequency shift characteristics are recorded to obtain the multivariate signal feature set.

4. The passive radar target localization method based on an external radiation source as described in claim 1, characterized in that, Based on the characteristics of the fused signal, target localization analysis is performed, including: Based on the aforementioned multi-signal feature set and combined with the spatial layout of the corresponding signal receiving points, the target location is calculated to obtain the target location information. Based on the multi-signal feature set, the motion state of the target is inferred, and the target's moving speed information is obtained; The target location information and target movement speed information are used to perform dynamic target tracking.

5. The passive radar target localization method based on an external radiation source as described in claim 4, characterized in that, Based on the aforementioned multi-signal feature set and combined with the spatial layout of the corresponding signal receiving points, the target location is calculated to obtain target location information, including: Based on the aforementioned multivariate signal feature set, time difference of arrival (TDOA) positioning is performed, and a hyperbolic equation system is established. Based on the hyperbolic equations and combined with the angle of arrival measurement results, a hybrid positioning model is constructed to solve and output the target position estimate in a three-dimensional coordinate system. The target position estimate includes longitude, latitude, and altitude information.

6. The passive radar target localization method based on an external radiation source as described in claim 4, characterized in that, Based on the aforementioned multi-signal feature set, the motion state of the target is inferred, and the target's moving speed information is obtained, including: Based on the multi-element signal feature set, continuous frame signal features are extracted, and the target radial velocity is obtained by Doppler frequency shift calculation. Analyze the target's position change trajectory and calculate the target's horizontal and vertical velocities; By fusing the target's radial velocity, horizontal velocity, and vertical velocity, a three-dimensional velocity vector of the target is output, which includes velocity magnitude and direction information.

7. A passive radar target positioning system based on an external radiation source, characterized in that, The system includes: An external radiation source determination module is used to screen and determine a set of external radiation source signals for a target monitoring area. A receiving antenna array deployment module is used to deploy a receiving antenna array based on the signal set of the external radiation source. The receiving antenna array includes multiple signal receiving points, and the multiple signal receiving points adopt a distributed network structure. A signal receiving module is used to receive signals from external radiation sources in real time based on the plurality of signal receiving points and transmit them to the quality assessment unit. An adaptive data filtering module is used by the quality assessment unit to adaptively filter signal data based on a periodic sequence of external radiation source signals and output a standard radiation source signal, wherein the standard radiation source signal carries a radiation source type identifier. The feature parameter extraction module is used to extract feature parameters from the standard radiation source signal to obtain a multi-element signal feature set. The target positioning analysis module is used to perform multi-band signal fusion based on the multi-element signal feature set, and to perform target positioning analysis based on the fused signal features to generate target location information; The target localization and analysis module is also used to perform the following steps: A spatiotemporal alignment model for signal features is established. For the multivariate signal feature set, the propagation delay differences of signals in different frequency bands are compensated to generate a reference signal feature set. According to the signal-to-noise ratio of each frequency band signal, a fusion weight is assigned, and multi-frequency band signal fusion is performed on the reference signal feature set to construct a joint feature matrix. The joint feature matrix contains a three-dimensional feature vector, which includes an arrival time vector, an arrival direction vector, and an arrival frequency vector.

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