Ground wave radar positioning method, system and device based on unmanned aerial vehicle ris assistance

Abstract

The application provides a ground wave radar positioning method, system and device based on unmanned aerial vehicle RIS assistance, a radar terminal transmits a high-frequency ground wave detection signal, and the ground wave detection signal is transmitted to a target through a direct path and an RIS assisted path for detection; the RIS assisted path is a detection path in which the ground wave detection signal is first transmitted to an unmanned aerial vehicle RIS carrying platform for reflection; then, based on the direct path echo signal, the RIS assisted reflection path echo signal and the real-time position of the unmanned aerial vehicle RIS carrying platform, the target position information is calculated. The application introduces a dynamically controllable RIS reflection node, constructs a new detection system of direct and reflected dual-path signal fusion, actively enhances the signal quality in non-line-of-sight and complex environments, and uses dual-path information complementation and intelligent closed-loop regulation to significantly improve the system positioning accuracy, environmental adaptability and target tracking capability, and significantly reduces the positioning error through dynamic calibration.

Description

Technical Field

[0001] This application belongs to the field of ground wave radar positioning technology, and in particular relates to a ground wave radar positioning method, system and device based on UAV RIS-assisted positioning. Background Technology

[0002] High-frequency ground wave radar utilizes electromagnetic waves that diffract along the Earth's surface to achieve over-the-horizon target detection and is applied in fields such as marine observation. However, traditional ground wave radar systems have the following limitations: (1) Detection performance is heavily dependent on coastal deployment, with poor adaptability to non-line-of-sight scenarios such as islands, reefs, and complex nearshore terrain, resulting in severe signal attenuation and low positioning accuracy; (2) Systems are mostly fixed or limitedly mobile (such as platform-based or shipborne), lacking the ability to actively adapt to the detection environment; (3) In complex electromagnetic environments and dynamic sea conditions, signals from a single propagation path are easily interfered with, resulting in insufficient stability.

[0003] In recent years, reconfigurable smart metasurface technology has attracted attention as a novel means of controlling electromagnetic wave propagation. While there are reports of applying RIS (Radio Resonance Surface) to enhance communication, a mature solution for deeply integrating it with ground-wave radar to construct a system capable of actively optimizing signal propagation paths and achieving high-precision, stable positioning has yet to be found.

[0004] The foregoing statements are for informational purposes only and are not intended to provide background information in connection with this application. Unless otherwise stated herein, the content described in this section is not prior art to the rest of this application. Summary of the Invention

[0005] This invention proposes a ground wave radar positioning method, system, and device based on UAV RIS-assisted positioning. The core objective of this invention is to construct a new detection system that integrates direct and reflected dual-path signals by introducing dynamically adjustable RIS reflection nodes, thereby actively enhancing signal quality in non-line-of-sight and complex environments. Furthermore, by utilizing complementary dual-path information and intelligent closed-loop control, the system's positioning accuracy, environmental adaptability, and target tracking capability are significantly improved.

[0006] According to a first aspect of the embodiments of this application, a ground wave radar positioning method based on UAV RIS-assisted positioning is provided, comprising the following steps:

[0007] The radar terminal transmits high-frequency ground wave detection signals and simultaneously transmits these signals to the target via both a direct path and a RIS-assisted path. The RIS-assisted path is a detection path in which the ground wave detection signal is first transmitted to the RIS-equipped platform of the UAV for reflection.

[0008] The UAV RIS platform is equipped with a RIS panel for receiving ground wave detection signals and for generating a controllable reflected beam to the target by adjusting the electromagnetic properties of the RIS units on its surface.

[0009] Simultaneously receive and distinguish between direct path echo signals and RIS-assisted reflection path echo signals;

[0010] The target location information is calculated based on the direct path echo signal, the RIS-assisted reflected path echo signal, and the real-time location of the UAV RIS-equipped platform.

[0011] In some embodiments of this application, after the UAV RIS-equipped platform forms a controllable reflected beam and transmits it to the target, it further includes:

[0012] It receives the auxiliary detection return signal from the target and then reflects the echo signal back to the RIS auxiliary reflection path;

[0013] Based on the signal quality of the auxiliary detection return signal from the target, the attitude of the UAV RIS-equipped platform or the phase distribution of its surface units is dynamically adjusted.

[0014] In some embodiments of this application, calculating target location information includes:

[0015] The time delay between the direct path echo signal and the RIS-assisted reflection path echo signal is extracted using a cross-correlation algorithm.

[0016] Based on the signal-to-noise ratio of the direct path echo signal and the RIS-assisted reflection path echo signal, fusion weights are assigned to the two signals. Then, a weighted fusion algorithm is used to fuse the direct path echo signal and the RIS-assisted reflection path echo signal to obtain the fused signal.

[0017] Based on the time delay of the direct path echo signal and the RIS-assisted reflection path echo signal, the fused signal, and the real-time position of the UAV RIS-equipped platform, the fused distance between the target and the radar is calculated through geometric relationships.

[0018] In some embodiments of this application, after calculating the target location information, the method further includes determining the target coordinates based on the target location information; determining the target coordinates based on the target location information includes:

[0019] The radar uses an antenna array to perform beam scanning and obtain the coarse azimuth value of the target.

[0020] The precise azimuth and elevation angles of the target are calculated using the echo phase difference measured between adjacent elements of the antenna array;

[0021] Based on the fused distance, precise azimuth, and elevation angles between the target and the radar, the three-dimensional coordinates of the target are determined by converting spherical coordinates to rectangular coordinates.

[0022] In some embodiments of this application, after determining the target coordinates based on the target location information, a step of performing hierarchical compensation to dynamically calibrate the target coordinates is further included; the step of performing hierarchical compensation to dynamically calibrate the target coordinates includes:

[0023] Collect sea surface environmental parameters, UAV RIS-equipped platform status parameters, and radar terminal deployment parameters;

[0024] Based on sea surface environmental parameters, the distance deviation and azimuth deviation caused by the sea surface environment are calculated through a pre-established correlation function and mapped to the first coordinate compensation component;

[0025] Based on the position offset and attitude angle deviation of the UAV RIS-equipped platform state parameters, the reflection path length deviation and beam offset angle are calculated and converted into second coordinate compensation components.

[0026] Based on the radar terminal deployment parameters, the systematic error is calculated and converted into a third coordinate compensation component.

[0027] The first coordinate compensation component, the second coordinate compensation component, and the third coordinate compensation component are weighted and superimposed to obtain the total calibration compensation amount, which is used to correct the initial positioning target coordinates.

[0028] In some embodiments of this application, after performing the step of dynamic error calibration of the target coordinates by performing layered compensation, the method further includes:

[0029] By using a moving average filtering step, the positioning results of the dynamically calibrated target coordinates are subjected to a time-weighted average to suppress random noise.

[0030] Through a particle filtering optimization step, based on the target motion model and the observation model, nonlinear deviation correction is performed on the filtered positioning results;

[0031] The dynamic error calibration step, the moving average filtering step, and the particle filtering optimization step form a three-level positioning result optimization link.

[0032] According to a second aspect of the embodiments of this application, a ground wave radar positioning system based on UAV RIS-assisted positioning is provided, comprising:

[0033] The ground wave radar terminal 100 is used to transmit high-frequency ground wave detection signals and simultaneously transmit the ground wave detection signals to the target through a direct path and a RIS-assisted path for detection; the RIS-assisted path is a detection path in which the ground wave detection signal is first transmitted to the RIS-equipped platform of the UAV for reflection; it is also used to synchronously receive and distinguish the echo signal from the direct path and the echo signal from the RIS-assisted reflection path;

[0034] The UAV RIS platform 200 is deployed over the detection area. The UAV RIS platform is equipped with a RIS panel for receiving ground wave detection signals and for generating a controllable reflected beam to the target by adjusting the electromagnetic properties of the RIS units on its surface.

[0035] Data processing center 300 is used to calculate target location information based on direct path echo signals, RIS-assisted reflection path echo signals, and the real-time location of the UAV RIS-mounted platform.

[0036] In some embodiments of this application, the system further includes a positioning calibration module; the positioning calibration module sequentially performs dynamic error calibration, moving average filtering, and particle filtering optimization to form a three-level positioning result optimization link.

[0037] According to a third aspect of the embodiments of this application, a ground wave radar positioning device based on UAV RIS-assisted positioning is provided, comprising: a storage unit for storing executable instructions; and a processing unit for connecting to the storage unit to execute the executable instructions to complete the UAV RIS-assisted ground wave radar positioning method.

[0038] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon; the computer program is executed by a processor to implement a ground wave radar positioning method based on UAV RIS-assisted positioning.

[0039] The UAV-based RIS-assisted ground wave radar positioning method, system, and apparatus of this application include a radar terminal transmitting a high-frequency ground wave detection signal, which is simultaneously transmitted to the target via a direct path and a RIS-assisted path. The RIS-assisted path is a detection path in which the ground wave detection signal is first transmitted to the UAV RIS-equipped platform for reflection. Then, based on the echo signal from the direct path, the echo signal from the RIS-assisted reflection path, and the real-time position of the UAV RIS-equipped platform, the target position information is calculated. This application introduces a dynamically adjustable RIS reflection node to construct a new detection system that integrates direct and reflected dual-path signals, actively enhancing signal quality in non-line-of-sight and complex environments. By utilizing complementary dual-path information and intelligent closed-loop control, it significantly improves the system's positioning accuracy, environmental adaptability, and target tracking capability, while significantly reducing positioning errors through dynamic calibration.

[0040] Compared with the prior art, the present invention has the following significant advantages:

[0041] 1) Significantly improved positioning accuracy and stability: By fusing dual-path signals, the directness of the direct path and the controllability and enhancement effect of the RIS reflection path are comprehensively utilized, and the impact of interference on a single path is reduced by information redundancy. Especially in non-line-of-sight or severely signal-attenuated areas, the RIS auxiliary path provides crucial signal supplementation, making positioning possible and more accurate.

[0042] 2) Enhanced environmental adaptability and robustness: The RIS's dynamic closed-loop control mechanism enables the system to respond in real time to signal fluctuations caused by environmental changes (such as target movement and platform swaying caused by wind speed), actively maintaining the optimal signal link. The multi-level optimized link further compensates for various systematic and random errors at the algorithm level, enabling the system to maintain high accuracy even in complex nearshore environments.

[0043] 3) Flexible system deployment and expanded application scenarios: The high mobility of the UAV RIS platform, combined with the portable ground wave radar terminal, enables the system to be deployed quickly in key areas such as islands, reefs and borders without relying on fixed infrastructure, which greatly expands the application scope of ground wave radar.

[0044] 4) High level of intelligence: The system realizes intelligent processing throughout the entire process from signal perception, intelligent fusion, dynamic control to trajectory prediction, reducing the reliance on manual intervention and better serving tasks such as automated early warning and monitoring. Attached Figure Description

[0045] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0046] Figure 1 The diagram shows a step-by-step schematic of a UAV-based RIS-assisted ground wave radar positioning method according to an embodiment of this application;

[0047] Figure 2 The diagram illustrates the steps for calculating target location information according to an embodiment of this application.

[0048] Figure 3 The diagram illustrates the steps for determining target coordinates according to an embodiment of this application.

[0049] Figure 4 The diagram shows an overall flowchart of dual-path signal propagation and ground wave radar positioning according to an embodiment of this application;

[0050] Figure 5 The flowchart illustrating multi-level optimization of target coordinates according to an embodiment of this application is shown;

[0051] Figure 6The diagram shows a schematic representation of a UAV-based RIS-assisted ground wave radar positioning system according to an embodiment of this application.

[0052] Figure 7 The diagram shows a system deployment scenario according to an embodiment of this application;

[0053] Figure 8 The diagram shows a schematic of the structure of a UAV-based RIS-assisted ground wave radar positioning device 40 according to an embodiment of this application. Detailed Implementation

[0054] Regarding this application, high-frequency ground-wave radar can achieve over-the-horizon target detection and is widely used in fields such as coastal defense early warning and marine monitoring. Currently, traditional ground-wave radar has limited coverage in complex environments such as nearshore areas and islands.

[0055] In recent years, reconfigurable smart metasurface technology has attracted attention as a novel means of controlling electromagnetic wave propagation. While there are reports of applying RIS (Radio Reflection Surface) technology to enhance communication, a mature solution for deeply integrating it with ground-wave radar to construct a system capable of actively optimizing signal propagation paths and achieving high-precision, stable positioning remains elusive. In particular, how to collaboratively process both direct and RIS-reflected signals and achieve real-time dynamic control of the RIS to track moving targets is a pressing technical problem in this field.

[0056] Current radar detection technology has the following main problems:

[0057] 1. In non-line-of-sight scenarios, the positioning deviation is large and the signal attenuation is severe.

[0058] 2. Complex electromagnetic environment and terrain obstruction lead to weak anti-interference ability.

[0059] 3. Single-path signals are easily affected by the environment, resulting in poor positioning stability.

[0060] 4. Lacking a real-time adaptive adjustment mechanism, it is difficult to cope with dynamic goals and environmental changes.

[0061] Therefore, this invention uses a portable ground wave radar assisted by a UAV RIS for target positioning. It combines the mobile deployment characteristics of portable radar with the electromagnetic control capabilities of the UAV RIS, which can make up for the shortcomings of a single ground wave radar positioning method, improve positioning accuracy and environmental adaptability, and be used for target positioning, dynamic tracking and data feedback in near-shore, island and reef scenarios.

[0062] The present invention relates to a system consisting of core modules such as a portable ground wave radar terminal, a UAV RIS mounting platform, a signal receiving and parsing module, an attitude adaptive adjustment module, a positioning calibration module, and a terminal data processing center.

[0063] Through technological innovation and system integration, it has solved the technical problems of traditional ground wave radar positioning, such as limited coverage, large positioning deviation in non-line-of-sight scenarios, and weak anti-interference in complex environments, providing accurate positioning support and technical guarantee for multiple fields such as coastal defense early warning, nearshore security, and emergency monitoring.

[0064] Intelligent metasurfaces, also known as "reconfigurable intelligent surfaces" or "intelligent reflective surfaces," are called RIS (Reconfigurable Intelligence Surface) or IRS (Intelligent Reflection Surface) in English.

[0065] The core inventive concept of this invention lies in: synergistically utilizing the echo signal from the direct path and the echo signal from the RIS-assisted reflection path reflected by the RIS platform; calculating the target distance based on the time delay difference between the two path signals and the real-time spatial position of the RIS platform, and forming a closed-loop control loop, that is, dynamically generating control commands to adjust the attitude of the RIS platform or the phase distribution of its surface units according to the signal quality (such as signal-to-noise ratio) of the reflection path, thereby optimizing the direction of the reflected beam in real time and actively maintaining the signal strength of the auxiliary path.

[0066] Preferably, a weighted fusion strategy is used to process the dual-path information. Specifically, fusion weights are dynamically allocated based on the signal-to-noise ratio (SNR) of the direct path and the SNR of the auxiliary path. The echo signals of the two paths or their calculated intermediate information (such as distance) are fused to effectively suppress transient interference from a single path and improve the robustness of the results.

[0067] Furthermore, to address errors introduced by the complex nearshore environment (such as sea surface fluctuations, atmospheric refraction, and platform vibration), the system establishes a multi-level positioning result optimization chain. This chain includes at least: dynamic error calibration based on a coupled model of multi-source parameters (environment, platform status, and deployment baseline), moving average filtering to suppress random noise, and particle filter optimization to correct nonlinear biases. These three steps are performed sequentially, forming a progressive correction system from system errors to random noise and then to motion model biases.

[0068] Furthermore, the system possesses intelligent tracking and prediction capabilities. During data processing, it can predict the target's trajectory based on historical positioning results and generate feedforward control commands, enabling the RIS platform's beam to point to the target's predicted position in advance. This effectively reduces signal attenuation and lag caused by target maneuvering, achieving stable tracking of high-speed maneuvering targets.

[0069] Based on the same inventive concept, this invention provides corresponding positioning methods, systems, devices, and computer-readable storage media.

[0070] Compared with existing technologies, it has the following technical advantages:

[0071] 1) Significantly improved positioning accuracy and stability: By fusing dual-path signals, the directness of the direct path and the controllability and enhancement effect of the RIS reflection path are comprehensively utilized, and the impact of interference on a single path is reduced by information redundancy. Especially in non-line-of-sight or severely signal-attenuated areas, the RIS auxiliary path provides crucial signal supplementation, making positioning possible and more accurate.

[0072] 2) Enhanced environmental adaptability and robustness: The RIS's dynamic closed-loop control mechanism enables the system to respond in real time to signal fluctuations caused by environmental changes (such as target movement and platform swaying caused by wind speed), actively maintaining the optimal signal link. The multi-level optimized link further compensates for various systematic and random errors at the algorithm level, enabling the system to maintain high accuracy even in complex nearshore environments.

[0073] 3) Flexible system deployment and expanded application scenarios: The high mobility of the UAV RIS platform, combined with the portable ground wave radar terminal, enables the system to be deployed quickly in key areas such as islands, reefs and borders without relying on fixed infrastructure, which greatly expands the application scope of ground wave radar.

[0074] 4) High level of intelligence: The system realizes intelligent processing throughout the entire process from signal perception, intelligent fusion, dynamic control to trajectory prediction, reducing the reliance on manual intervention and better serving tasks such as automated early warning and monitoring.

[0075] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0076] Example 1

[0077] Figure 1 The diagram illustrates the steps of a UAV-based RIS-assisted ground wave radar positioning method according to an embodiment of this application.

[0078] like Figure 1 As shown, a ground wave radar positioning method based on UAV RIS-assisted localization is provided, including the following steps:

[0079] S1: The radar terminal transmits a high-frequency ground wave detection signal and simultaneously transmits the ground wave detection signal to the target through both the direct path and the RIS auxiliary path; the RIS auxiliary path is a detection path in which the ground wave detection signal is first transmitted to the UAV RIS-equipped platform for reflection;

[0080] Among them, the UAV RIS platform is equipped with a RIS panel, which is used to receive ground wave detection signals and form a controllable reflected beam to be emitted to the target by adjusting the electromagnetic properties of the RIS units on its surface.

[0081] S2: Synchronously receive and distinguish between direct path echo signals and RIS-assisted reflection path echo signals;

[0082] S3: Calculate the target location information based on the direct path echo signal, the RIS-assisted reflection path echo signal, and the real-time location of the UAV RIS-equipped platform.

[0083] In S1, the radar terminal generates a linear frequency modulated (LFM) signal as a detection signal, the expression of which is:

[0084] ;

[0085] in, Let denot be the transmitted signal amplitude, rect(·) be the rectangular window function, T be the signal duration, and f0 be the center operating frequency. = B / T is the frequency modulation slope, and B is the signal bandwidth;

[0086] Then, after being amplified by a high-gain power amplifier, it is transmitted in a vertically polarized manner by a compact high-frequency antenna array.

[0087] In S1, within the RIS-assisted path, after the UAV RIS-equipped platform forms a controllable reflected beam and transmits it to the target, it also includes:

[0088] It receives the auxiliary detection return signal from the target and then reflects the echo signal back to the RIS auxiliary reflection path;

[0089] Based on the signal quality of the auxiliary detection return signal from the target, the attitude of the UAV RIS-equipped platform or the phase distribution of its surface units is dynamically adjusted.

[0090] The expression for the signal from the RIS auxiliary reflection path after reflection by the RIS panel is as follows:

[0091] ;

[0092] in, The channel matrix for radar to RIS;

[0093] R is the RIS reflection matrix, expressed as:

[0094] ;

[0095] In the formula For the first The reflection coefficient of each RIS unit; For the first The phase offset of each element is the core parameter for achieving beamforming; j is the imaginary unit, and N is the number of RIS elements. The more elements there are, the higher the accuracy of beamforming is usually.

[0096] The attitude adaptive adjustment module monitors the signal-to-noise ratio of the reflection path in real time and dynamically adjusts the phase offset when it falls below a preset threshold. The attitude angle of the RIS panel is adjusted to compensate for signal attenuation and maintain beam alignment with the target.

[0097] Figure 2 The diagram illustrates the steps for calculating target location information according to an embodiment of this application.

[0098] like Figure 2 As shown, in S3, calculating the target location information includes:

[0099] S31: Extract the time delay of the direct path echo signal and the RIS-assisted reflection path echo signal using a cross-correlation algorithm;

[0100] S32: Based on the signal-to-noise ratio of the direct path echo signal and the auxiliary reflection path echo signal, after assigning fusion weights to the two, a weighted fusion algorithm is used to fuse the direct path echo signal and the auxiliary reflection path echo signal to obtain the fused signal;

[0101] S33: Based on the time delay of the direct path echo signal and the RIS-assisted reflection path echo signal, the fused signal, and the real-time position of the UAV RIS-equipped platform, the fused distance between the target and the radar is calculated through geometric relationships.

[0102] In the process of extracting the echo signal time delay, the direct path time delay is extracted by performing cross-correlation calculations on the transmitted signal and the echo signal. and RIS-assisted reflection path time delay ;

[0103] Then, in the process of dual-path signal fusion and distance calculation, the signal-to-noise ratio of the echo signals from the direct path and the RIS-assisted reflection path is used. and Calculate the fusion weights , The formula is as follows:

[0104] ;

[0105] ;

[0106] The preprocessed dual-path echo signals are weighted and fused. The expression of the fused signal is as follows:

[0107] ;

[0108] in, , It is the pre-processed direct and auxiliary reflection path echo signal. This fusion operation can compensate for the signal fluctuation problem caused by interference in a single path, making the target signal more stable.

[0109] Finally, combined with time delay , The real-time coordinates of the UAV RIS platform are used to calculate the fused distance between the target and the radar through geometric relationships.

[0110] Figure 3 The diagram illustrates the steps for determining target coordinates according to an embodiment of this application.

[0111] like Figure 3 As shown, after S3 calculates the target location information, S4 is also included: determining the target coordinates based on the target location information; determining the target coordinates based on the target location information includes:

[0112] S41: Beam scanning is achieved through the antenna array mounted on the radar to obtain the coarse azimuth value of the target;

[0113] S42: Calculate the precise azimuth and elevation angles of the target using the echo phase difference measured by adjacent elements of the antenna array;

[0114] S43: Based on the fused distance, precise azimuth and elevation angles between the target and the radar, the three-dimensional coordinates of the target are determined by converting spherical coordinates to rectangular coordinates.

[0115] In a preferred embodiment, after determining the target coordinates based on the target location information, the method further includes a step of performing hierarchical compensation to dynamically calibrate the target coordinates; the step of performing hierarchical compensation to dynamically calibrate the target coordinates includes:

[0116] Collect sea surface environmental parameters, UAV RIS-equipped platform status parameters, and radar terminal deployment parameters;

[0117] Based on sea surface environmental parameters, the distance deviation and azimuth deviation caused by the sea surface environment are calculated through a pre-established correlation function and mapped to the first coordinate compensation component;

[0118] Based on the position offset and attitude angle deviation of the UAV RIS-equipped platform state parameters, the reflection path length deviation and beam offset angle are calculated and converted into second coordinate compensation components.

[0119] Based on the radar terminal deployment parameters, the systematic error is calculated and converted into a third coordinate compensation component.

[0120] The first coordinate compensation component, the second coordinate compensation component, and the third coordinate compensation component are weighted and superimposed to obtain the total calibration compensation amount, which is used to correct the initial positioning target coordinates.

[0121] Further implementation, after performing the hierarchical compensation step to dynamically calibrate the target coordinates, also includes:

[0122] By using a moving average filtering step, the positioning results of the dynamically calibrated target coordinates are subjected to a time-weighted average to suppress random noise.

[0123] Through a particle filtering optimization step, based on the target motion model and the observation model, nonlinear deviation correction is performed on the filtered positioning results;

[0124] Thus, a three-level positioning result optimization link is formed through dynamic error calibration, moving average filtering, and particle filtering optimization steps.

[0125] Figure 4 The diagram shows an overall flowchart of dual-path signal propagation and ground wave radar positioning according to an embodiment of this application.

[0126] like Figure 4 The diagram illustrates the dual-path signal generation, reception, and RIS dynamic control process of an embodiment of this application, including signal transmission: a portable ground wave radar transmits a linear frequency modulated (LFM) detection signal. Dual-path propagation: the signal propagates to the target and returns via a direct path and a RIS-assisted reflection path. Signal preprocessing: the received dual-path echoes are amplified, filtered, and down-converted. Time delay extraction: a cross-correlation algorithm is used to extract the time delay of the direct and reflected paths. Distance calculation and fusion: the distance is calculated based on the time delay and RIS position geometry, and the dual-path results are fused using a signal-to-noise ratio weighted calculation. Azimuth calculation and coordinate determination: the target azimuth and elevation angles are obtained through coarse beam scanning combined with phase interferometry, ultimately outputting three-dimensional coordinates. Calibration, optimization, and tracking: dynamic error calibration, moving average filtering, and particle filtering optimization are performed on the coordinates to ultimately achieve dynamic target tracking and data feedback.

[0127] Its signal processing flow is as follows:

[0128] The radar terminal transmits a signal s(t). This signal propagates simultaneously through two paths:

[0129] (a) Direct path: The signal propagates directly to the target T by diffraction along the sea surface, and returns to the radar along the original path after reflection;

[0130] (b) RIS Auxiliary Path: The signal first propagates to the suspended RIS platform, is phase-modulated by the RIS panel (reflection matrix is ​​R), is directionally reflected to the target T, is reflected back to the RIS by the target, and finally returns to the radar terminal after being modulated by the RIS again.

[0131] The radar receiver simultaneously acquires two echo signals. and .

[0132] The signal receiving and analysis module first performs preprocessing on the raw echo, including amplification, filtering, and down-conversion. Then, it distinguishes the direct echo signal based on signal characteristics (such as arrival time and correlation with known RIS modulation). With RIS auxiliary echo signal .

[0133] The attitude adaptive adjustment module calculates the auxiliary path echo in real time. signal-to-noise ratio And set a signal-to-noise ratio threshold. (e.g., 15dB).

[0134] when Below When the module initiates the control algorithm: First, it calculates the desired beam pointing based on the geometric relationship between the radar, RIS, and the target (which can be roughly estimated through historical positioning results or beam scanning); second, it solves for a new set of RIS unit phase offsets {θ_i'} using optimization algorithms such as gradient descent, so that the main lobe of the reflected beam is aligned with the target; at the same time, if necessary, it generates attitude adjustment commands for the UAV platform (such as yaw and pitch fine-tuning) by combining the UAV attitude data.

[0135] The new phase configuration and / or attitude commands are sent to the UAV RIS platform for execution via a wireless link, thus forming a closed loop of "monitoring-calculation-adjustment" to ensure stable auxiliary path signal quality.

[0136] The core algorithm steps for target localization performed by the data processing center are as follows:

[0137] (1) Time delay extraction. For the preprocessed direct echo... and auxiliary echo Each of these is cross-correlated with the locally stored copy of the transmitted signal s(t).

[0138] The cross-correlation function is:

[0139] ;

[0140] when When the maximum value is reached, the corresponding This refers to the time delay of the target echo, i.e., the time delay. and .

[0141] (2) Preliminary distance calculation. Calculate the direct distance. ; Calculate the total propagation distance of the RIS auxiliary path .

[0142] (3) Dual path information fusion and distance calculation.

[0143] a) Estimate separately , signal-to-noise ratio and .

[0144] b) Calculate the fusion weights and .

[0145] c) Real-time coordinates based on the UAV RIS platform ) and radar coordinates ( Using geometric relationships (law of cosines) from The distance from the target to the RIS is calculated in the middle. And the estimated distance from the target to the radar (Geometric solution).

[0146] d) Calculate the fusion distance .

[0147] (4) Calculation of azimuth and pitch angle.

[0148] a) Beam scanning coarse positioning: Control the radar receiving antenna array to perform digital beamforming, scan in the azimuth dimension, and find the beam pointing θ that makes the fused signal energy the strongest.

[0149] b) Phase interferometry refinement: Utilizing the phase difference Δφ between the received signals from adjacent channels of the antenna array, ;

[0150] According to the formula Calculate the precise azimuth angle θ, where d is the spacing between adjacent elements of the antenna array. The elevation angle φ is calculated similarly using the phase difference of the vertical array.

[0151] (5) Coordinate transformation. Convert the spherical coordinates (R, θ, φ) to geodetic rectangular coordinates (R, θ, φ). ),in, , , .

[0152] Figure 5The diagram shows a flowchart of multi-level optimization of target coordinates according to an embodiment of this application.

[0153] like Figure 5 As shown, a multi-level optimization process is employed for the positioning results. To obtain the final high-precision positioning result, the initial coordinates obtained above need to be optimized at multiple levels. The core process is as follows:

[0154] Level 1: Dynamic Error Calibration. Real-time data collection includes sea surface conductivity σ, wind speed w (affecting sea surface roughness), atmospheric refractive index n, UAV position / attitude deviation, and radar reference drift. This is achieved through a pre-calibrated error propagation model (e.g., range deviation ΔR). env ∝ f(σ, w, n)), calculate the coordinate offset (ΔX) caused by various errors. err , ΔY err , ΔZ err The offsets are weighted and summed according to the confidence level of each error source to obtain the total compensation, which is then used to correct the initial coordinates in one go.

[0155] Second stage: Moving average filtering. A weighted moving average is used to filter the positioning coordinate sequence over multiple consecutive time periods (e.g., 10 cycles) after dynamic calibration. More recent data is given higher weight, smoothing out random noise and outputting a more stable trajectory.

[0156] Level 3: Particle Filter Optimization. Using the trajectory output by the moving average filter as the observation, a uniform-accelerated motion model of the target is established as the state equation. A batch of particles is initialized, and through steps such as prediction, weight update, and resampling, the optimal state (position, velocity) of the target is estimated, finally outputting the optimized three-dimensional coordinates (X_final, Y_final, Z_final). This step can effectively handle the nonlinear tracking error caused by target maneuvering.

[0157] Finally, after outputting the final positioning result, the system enters the tracking state.

[0158] Based on the optimized trajectory over a recent period (e.g., 2 seconds), the data processing center uses a Kalman filter or α-β-γ filter to predict the target's position (X_pred, Y_pred, Z_pred) at the next moment (e.g., 0.1 seconds later). Based on this predicted position, and the current positions of the radar and RIS, the optimal reflection beam direction required by the RIS is recalculated. This beam pointing command is used as a feedforward, combined with the aforementioned feedback control command based on the current signal-to-noise ratio, and sent to the UAV RIS platform. This allows the RIS beam to "wait" for the target in advance, significantly reducing tracking delay and improving the ability to continuously lock onto high-speed maneuvering targets.

[0159] In summary, the UAV-based RIS-assisted ground wave radar positioning method of this application includes the radar terminal transmitting a high-frequency ground wave detection signal, which is simultaneously transmitted to the target via a direct path and a RIS-assisted path. The RIS-assisted path is a detection path where the ground wave detection signal is first transmitted to the UAV RIS-equipped platform for reflection. Then, based on the echo signal from the direct path, the echo signal from the RIS-assisted reflection path, and the real-time position of the UAV RIS-equipped platform, the target position information is calculated. This application introduces a dynamically adjustable RIS reflection node to construct a new detection system that integrates direct and reflected dual-path signals, actively enhancing signal quality in non-line-of-sight and complex environments. By utilizing complementary dual-path information and intelligent closed-loop control, it significantly improves the system's positioning accuracy, environmental adaptability, and target tracking capability, while significantly reducing positioning errors through dynamic calibration.

[0160] Example 2

[0161] This embodiment provides a UAV-based RIS-assisted ground wave radar positioning system. For details not disclosed in this embodiment, please refer to the specific implementation details of the UAV-based RIS-assisted ground wave radar positioning schemes in other embodiments.

[0162] Figure 6 The diagram shows a schematic of a UAV-based RIS-assisted ground wave radar positioning system according to an embodiment of this application.

[0163] like Figure 6 As shown, the UAV-based RIS-assisted ground wave radar positioning system includes:

[0164] The ground wave radar terminal 100 is used to transmit high-frequency ground wave detection signals and simultaneously transmit the ground wave detection signals to the target through a direct path and a RIS-assisted path for detection; the RIS-assisted path is a detection path in which the ground wave detection signal is first transmitted to the RIS-equipped platform of the UAV for reflection; it is also used to synchronously receive and distinguish the echo signal from the direct path and the echo signal from the RIS-assisted reflection path;

[0165] The UAV RIS platform 200 is deployed over the detection area. The UAV RIS platform is equipped with a RIS panel for receiving ground wave detection signals and for generating a controllable reflected beam to the target by adjusting the electromagnetic properties of the RIS units on its surface.

[0166] Data processing center 300 is used to calculate target location information based on direct path echo signals, RIS-assisted reflection path echo signals, and the real-time location of the UAV RIS-mounted platform.

[0167] In a preferred embodiment, the system further includes a positioning calibration module; the positioning calibration module sequentially performs dynamic error calibration, moving average filtering, and particle filtering optimization to form a three-level positioning result optimization link.

[0168] Figure 7 The diagram shows a system deployment scenario according to an embodiment of this application.

[0169] like Figure 7 As shown, the UAV RIS-assisted ground wave radar positioning system of this embodiment mainly includes a portable ground wave radar terminal, a UAV RIS mounting platform, and a remote data processing center.

[0170] The portable ground wave radar terminal is deployed at a pre-defined location near the coast and includes a transmitting antenna array, a receiving antenna array, a radio frequency front-end, and a local processor. Its core function is to transmit linear frequency modulated continuous wave signals and simultaneously receive echoes from targets. The transmitted waveform parameters (such as center frequency f0=15MHz, bandwidth B=100kHz) are preset according to the detection distance and resolution requirements.

[0171] The RIS platform for drones consists of a drone aircraft and a reconfigurable smart metasurface panel.

[0172] The RIS panel consists of N (e.g., 256) independently programmable subwavelength units, each of which can have its reflected phase of the incident wave adjusted by a control circuit. The UAV integrates a high-precision GPS / BeiDou positioning module, an inertial measurement unit, and a wireless communication module to report its own position and attitude in real time and receive control commands from the data processing center.

[0173] During deployment, the absolute coordinates of the radar terminal are first calibrated.

[0174] Subsequently, the UAV RIS platform takes off and hovers in the preferred airspace between the radar and the area to be measured, establishing a stable communication link with the radar terminal and the data processing center.

[0175] The following describes the ground wave radar positioning process through a specific implementation procedure.

[0176] Step 1: Regarding the portable ground wave radar terminal, it is responsible for transmitting high-frequency ground wave detection signals and receiving target reflected echo signals.

[0177] The drone's RIS platform utilizes the onboard RIS metasurface panel to control the phase and amplitude characteristics of electromagnetic waves.

[0178] In a preferred embodiment, the signal receiving and parsing module is integrated into the radar terminal.

[0179] In a preferred embodiment, the attitude adaptive adjustment module is embedded in the UAV's RIS platform, and the positioning calibration module establishes communication with the terminal data processing center. Due to the unique characteristics of its working environment, such as complex electromagnetic interference and terrain obstruction, it needs to be designed to be interference-resistant, miniaturized, and capable of stable operation in harsh outdoor environments.

[0180] Step 2: The high-frequency ground wave signal generation and transmission module of the ground wave radar terminal generates and transmits the raw signal required for positioning. It adopts a linear frequency modulation (LFM) waveform that takes into account both detection range and resolution to ensure that the signal can meet the accurate positioning requirements of complex scenarios such as nearshore areas and islands.

[0181] For positioning scenarios with diverse terrain, such as nearshore / island / reef areas, the module uses LFM to generate and transmit high-frequency ground wave signals:

[0182] First, the radar transmitting module generates an LFM signal, the waveform parameters of which must match the characteristics of the detection scene, and its bandwidth is... , time width is FM slope The corresponding transmitted signal expression is:

[0183] ;

[0184] in The amplitude of the transmitted signal needs to be set according to the required detection distance. For rectangular window functions, only The value is 1 within the time interval to ensure the time-domain focus of the signal; The operating frequency of the radar center needs to be adapted to the sea surface propagation characteristics of ground wave signals; The time variable is used. After signal generation is completed, the generated LFM signal is first amplified to a preset power value by a high-gain power amplifier, and then transmitted in a vertically polarized manner through a compact high-frequency antenna array. This transmission method can enhance the propagation stability of ground wave signals along the sea surface.

[0185] Step 3: Next, the dual-path signal propagation and RIS electromagnetic control module is responsible for coordinating the propagation link planning and signal enhancement processing of the transmitted signal. The transmitted signal is divided into a direct path and a RIS-assisted reflection path for synchronous propagation. At the same time, the dynamic electromagnetic control capability of the UAV RIS platform is used to optimize the reflection link, thereby enhancing the propagation strength and stability of the signal in the complex environment of nearshore and island reefs.

[0186] For scenarios such as nearshore / island / reef areas where terrain obstructs signal and signal attenuation is significant, the module first ensures signal coverage through a dual-link design of a direct path and a RIS-assisted reflection path:

[0187] First, there is the direct path propagation, where the high-frequency signal diffracts along the sea surface / ground, directly illuminating the target within the detection area. After being reflected by the target, it returns to the radar receiver. The attenuation of this path follows the ground wave propagation attenuation formula:

[0188] ;

[0189] in This is the direct path attenuation (in dB), and its magnitude directly affects the received signal strength. λ represents the straight-line distance between the radar and the target (in meters); the attenuation is usually more significant with increasing distance. λ is the radar signal wavelength, determined by the speed of light. m / s and radar center operating frequency Calculation yields (i.e., λ = The higher the frequency, the shorter the wavelength, and the diffraction ability of ground waves will change accordingly. The ground attenuation coefficient for ground wave propagation is related to factors such as propagation distance, signal frequency, and ground conductivity. This coefficient is one of the key factors causing signal attenuation along a direct path.

[0190] Next is the RIS-assisted reflection path propagation. The signal first propagates from the radar terminal to the UAV's RIS panel. After phase modulation and beamforming processing by the RIS unit, it is directionally reflected to the target area, then reflected back to the RIS panel by the target, and finally returns to the radar receiver after secondary phase modulation. This link can effectively compensate for the signal attenuation problem of the direct path.

[0191] The expression for the signal after reflection by the RIS at sea is:

[0192] ;

[0193] in This is the channel matrix from radar to RIS, used to describe the signal transmission characteristics between radar and RIS. Its element values ​​are related to factors such as propagation distance and environmental obstruction.

[0194] The RIS reflection matrix is ​​expressed as follows:

[0195] ;

[0196] In the formula For the first The reflection coefficient of each RIS unit determines the intensity of signal reflection by that unit, and it usually needs to be set to a value between 0 and 1 according to the signal requirements. For the first The phase shift of each unit is the core parameter for achieving beamforming; The imaginary unit, The number of RIS units generally indicates the higher the beamforming accuracy.

[0197] The total attenuation of the reflection path is:

[0198] ;

[0199] in The straight-line distance between the radar and the RIS. Let RIS be the straight-line distance between the target and the target. The reflection loss of the RIS panel is much lower than the signal attenuation caused by complex terrain, which is one of the important reasons why the RIS auxiliary link can enhance the signal.

[0200] Finally, in the RIS dynamic control stage, the attitude adaptive adjustment module monitors the signal quality of the reflection path in real time. When the signal-to-noise ratio of the reflection path is detected to be lower than the threshold, it immediately and dynamically adjusts the attitude angle and cell phase offset of the RIS panel. By adjusting the attitude angle, the RIS panel is always facing the target area. By adjusting the phase offset, the reflected beam is always accurately aligned with the target. This compensates for the signal attenuation during propagation, reduces the overall adjustment response time, and can quickly adapt to signal fluctuations caused by target movement or environmental changes. It ensures that the dual-path signal always maintains a stable propagation state, providing a reliable signal foundation for subsequent signal reception and positioning calculations.

[0201] Step 4: The dual-path echo signal acquisition and preprocessing module is responsible for coordinating the synchronous acquisition and optimization of echo signals from the direct path and the RIS-assisted reflection path by the radar receiver. The radar receiver module synchronously acquires the echo signals from these two paths, and after multi-stage preprocessing to remove various interferences, the signals are transmitted to the terminal data processing center to improve signal quality and provide a reliable signal foundation for subsequent positioning calculations.

[0202] The first step is signal acquisition, where the receiving module simultaneously acquires the direct echo signal. With RIS auxiliary echo signal ,in:

[0203] ;

[0204] To determine the amplitude of the direct echo signal, the derived formula is as follows:

[0205] ;

[0206] ;

[0207] The formula for the amplitude of the RIS-assisted echo signal is as follows:

[0208] .

[0209] The synchronous acquisition here is to ensure the time correlation of the signals from the two paths and avoid deviations in subsequent data fusion due to acquisition time differences; at the same time, sampling must be carried out in accordance with the Nyquist criterion to ensure that there is no frequency aliasing problem when the analog signal is converted into a digital signal. The continuous analog echo signal is converted into discrete digital signal by the analog-to-digital converter (ADC) to complete the digital conversion of the signal.

[0210] Next comes the signal preprocessing stage. First, the acquired digital signal undergoes low-noise amplification to enhance the weak echo signal strength while avoiding the introduction of additional noise. Then, bandpass filtering is performed, which precisely filters out environmental noise outside the frequency band, such as ocean clutter and ionospheric interference, retaining only the echo components matching the radar's transmitted signal frequency. Following this, down-conversion is performed to convert the filtered high-frequency signal into a baseband signal, lowering the signal frequency to facilitate subsequent digital signal processing. After these basic processing steps, the signal receiving and analysis module further removes complex noise such as ocean clutter and ionospheric interference. These interferences severely affect the purity of the echo signal, and the analysis module uses signal correlation matching and clutter suppression algorithms to separate the effective echo components, ultimately obtaining the preprocessed signal. and .

[0211] After preprocessing, these two sets of optimized signals are transmitted to the terminal data processing center via a communication link. Throughout the process, the sampling rate criteria for signal acquisition, the setting of the filtering frequency band, and the selection of down-conversion parameters all need to be coordinated with the parameters (bandwidth) of the radar transmitted signal. Center frequency Strict matching.

[0212] Step 5: The echo signal time delay extraction module, using the cross-correlation algorithm of the terminal data processing center, extracts the time delay of the echo signals from the direct path and the RIS-assisted reflection path. This time delay is the round-trip time from transmission to reception and is a core parameter for subsequent distance calculation. The core process and formula are as follows: First, the time delay is determined by constructing a cross-correlation function between the transmitted and echo signals. The function expression is:

[0213] ,

[0214] in The cross-correlation function represents the transmitted signal and the echo signal. The higher the value, the stronger the similarity between the transmitted signal and the echo signal after a delay of τ. It is the original signal emitted by the radar, namely the LFM signal in step one. It's a delay. The conjugate form of the subsequent echo signal, It is the duration of the transmitted signal. It is a delayed variable;

[0215] when When the maximum value is reached, the corresponding This refers to the time delay of the target echo; subsequently, based on the relationship between signal propagation distance and the speed of light, the time delay of the two paths is determined:

[0216] The formula for the time delay of a direct path is:

[0217] ;

[0218] in, This is the round-trip time for the direct route. It is the straight-line distance between the radar and the target;

[0219] The time delay formula for the RIS-assisted reflection path is:

[0220] ;

[0221] in, It is the round-trip time of the reflection path. It is the distance between the radar and the RIS platform. This refers to the distance between the RIS platform and the target. The logic behind these two formulas is that "time delay equals the round-trip distance of the signal divided by the speed of light," obtained through a cross-correlation algorithm. and It can not only solve the corresponding distance parameters in reverse, but also reduce the error caused by interference from a single path through the complementary use of dual-path data, providing reliable core parameters for subsequent positioning.

[0222] Step Six: The dual-path signal fusion and range calculation module, based on the signal-to-noise ratio difference between the echo signals from the direct path and the RIS-assisted path, enhances the stability of the target signal through a weighted fusion algorithm. Combining the time delay and the real-time position of the RIS, it completes the range calculation between the target and the radar.

[0223] The core process and formula details are as follows:

[0224] The first step is the weighted fusion process, which involves fusion weights. , It is determined based on the signal-to-noise ratio (SNR) of the two paths, and the specific formula is as follows:

[0225] ;

[0226] ;

[0227] The signal-to-noise ratio (SNR) is calculated based on the echo signal amplitude and noise power, i.e. , ;

[0228] in, It is the amplitude of the echo signal from the direct path. These are the amplitudes of the RIS-assisted reflection path echo signal; both reflect the intensity of the echo from the corresponding path. This represents noise power, indicating the strength of environmental interference. Through this weighting, paths with higher signal-to-noise ratios will occupy a larger proportion in the fusion, thereby improving the reliability of the fused signal.

[0229] The final fused signal expression is:

[0230] ;

[0231] in , These are the direct and auxiliary path echo signals after preprocessing in step five. This fusion operation can compensate for the signal fluctuation problem caused by interference in a single path, making the target signal more stable.

[0232] The next step is the range calculation stage, which requires combining the time delay and the real-time position of the RIS to calculate the fused range between the target and the radar. Its core formula is:

[0233] ;

[0234] in This represents the spatial geometric correction terms for the radar, RIS, and target obtained through the law of cosines:

[0235] ;

[0236] and This is the horizontal distance from the RIS platform to the target. When the influence of the height difference is excluded, the near-shore target Zt≈0. The straight-line distance from the RIS platform to the target is obtained by subtracting the distance from the radar to the RIS from the total round-trip distance of the reflection path.

[0237] It is the straight-line distance from the radar deployment point to the UAV RIS platform.

[0238] Calculated directly from GPS / BeiDou coordinates, The coordinates are the real-time location coordinates of the UAV's RIS platform, provided by the GPS / BeiDou dual-mode positioning module; These are the fixed coordinates for radar deployment, which have been pre-calibrated during the deployment phase. It is the straight-line distance between the radar and the target along the direct path. It is the azimuth angle of the RIS platform, that is, the angle between the two lines connecting "radar → RIS" and "RIS → target", and the formula is:

[0239] .

[0240] The entire process combines the advantages of dual-path signals through weighted fusion, and then uses the real-time position and angle parameters of RIS to correct path deviations. The final calculated fused distance R balances the directness of the direct path and the stability of the RIS-assisted path, which can effectively improve the accuracy of distance calculation in complex near-shore and island / reef scenarios, and provide core distance parameters for subsequent target localization and tracking.

[0241] Step 7, Target Azimuth Calculation and Coordinate Determination module, adopts a combination of "beam scanning + phase interferometry". First, it accurately calculates the target's azimuth and elevation angles. Then, combined with the distance parameters obtained in Step 6, it converts the spherical coordinates into the target's rectangular coordinates. This is the core step in completing target localization in near-shore and island / reef scenarios, and its process is detailed and clearly structured.

[0242] The first step is the coarse positioning stage of beam scanning. The radar achieves electronic beam scanning through its onboard antenna array. This involves controlling the phase of each element in the antenna array to ensure that the transmitted / received beams cover the detection area angle by angle, while simultaneously recording the fused signal in each beam direction in real time, which is step six. The amplitude value—Since the target will strongly reflect the beam in the corresponding direction, the amplitude of the fused signal will reach its maximum value when the beam is aligned with the target. At this time, the direction corresponding to the beam is the coarse azimuth value of the target. This step provides a preliminary directional range for subsequent precise positioning, avoids large-scale search in the subsequent fine calculation stage, and improves positioning efficiency.

[0243] The next step is the precise positioning using phase interferometry, which calculates a more accurate azimuth angle based on the phase difference of the echoes from adjacent elements of the antenna array.

[0244] First, set the spacing between adjacent elements of the antenna array. (where λ is the wavelength of the radar signal). This spacing is chosen to avoid the "grating lobe" phenomenon, which means that when the spacing between adjacent cells is too large, multiple phase differences in multiple directions will correspond to the same angle, leading to errors in azimuth angle calculation.

[0245] Based on this, the echo phase difference received by the two units ;

[0246] in Let be the azimuth angle of the target. By transforming this formula, we can obtain the formula for calculating the azimuth angle: This formula allows phase difference to be converted into precise azimuth angle;

[0247] Similarly, between antenna array elements in the vertical direction, the target's elevation angle can be calculated using the same logic by measuring the echo phase difference. This achieves the transition from "coarse positioning" to "fine positioning," meeting the high-precision positioning requirements for nearshore and island / reef scenarios.

[0248] Finally, there's the target coordinate transformation step. Based on the transformation relationship between spherical coordinates and rectangular coordinates, the obtained distance is transformed... Azimuth Pitch angle Convert to the target's rectangular coordinates ( The conversion formula is:

[0249] ;

[0250] in the formula It is the component of distance along the X-axis in a Cartesian coordinate system. It is the component in the Y-axis direction. The Z-axis component is the component in the direction of the radar. By superimposing the three components on the radar's own coordinates, the absolute position of the target in the Cartesian coordinate system can be obtained.

[0251] The entire process, from "beam scanning to coarsely determine the direction" to "phase interferometry to refine the angle" and then to "coordinate transformation to determine the position," forms a positioning process from rough to precise: beam scanning quickly locks the approximate direction of the target, avoiding the blindness of precise calculation; phase interferometry utilizes the phase characteristics of the antenna array to significantly improve the accuracy of azimuth / elevation angles; and coordinate transformation converts the "distance + angle" parameters of spherical coordinates into more intuitive and easier-to-track rectangular coordinates. The combination of these three methods ensures positioning efficiency and meets the high-precision requirements for target position in near-shore and island / reef scenarios, providing core positional data support for dynamic target tracking in maritime defense early warning.

[0252] Step 8: Implementation process of dynamic calibration and smoothing optimization module for positioning results.

[0253] The dynamic calibration and smoothing optimization module for positioning results serves as the core guarantee of accuracy for target positioning systems in nearshore and island / reef scenarios. It focuses on positioning deviations caused by the coupling of complex environments and equipment states. Through a three-level progressive architecture of "dynamic error calibration (environment-equipment coupling closed-loop compensation) → moving average filtering (temporal random noise suppression) → particle filtering optimization (nonlinear deviation correction)," it achieves high-precision and high-stability output of positioning results, adapting to the core requirements of mobile deployment and real-time tracking of portable ground wave radar.

[0254] Dynamic error calibration, as a crucial preliminary step in the entire process, achieves real-time correction of time-varying deviations through multi-source parameter acquisition, error source quantification and correlation, hierarchical compensation iteration, and anomaly verification, combined with quantization formulas. The following provides a detailed description from the perspective of the entire process, including specific implementation logic, mechanisms, formulas, and engineering details. No specific numerical data is provided throughout; the formulas are deeply integrated with the implementation logic.

[0255] 1) Dynamic error calibration: Real-time correction of coupling deviations across the entire link.

[0256] The core objective of dynamic error calibration is to eliminate positioning errors caused by the coupling of three core factors in nearshore and island / reef scenarios: "time-varying characteristics of the sea surface environment + fluctuations in the RIS state of UAVs + deployment deviations of radar terminals". By establishing a closed-loop mechanism of "parameter acquisition - error modeling - hierarchical compensation - verification iteration", and combining signal propagation theory and geometric transformation formulas, real-time and accurate correction of deviations can be achieved. The complete implementation process is divided into six major steps, each step is interconnected and progressive, and the formula serves as the core tool throughout the entire compensation process.

[0257] (a) Calibration pre-processing: synchronous acquisition and preprocessing of multi-source parameters.

[0258] Dynamic calibration requires comprehensive and synchronized input parameters, covering three dimensions: environment, equipment status, and deployment baseline. Through multi-device collaborative data acquisition and preprocessing, reliable data support is provided for error modeling and formula calculation. The specific implementation method is as follows:

[0259] Multi-dimensional parameter acquisition system construction: adopts an architecture of "distributed acquisition + centralized aggregation" to simultaneously acquire three types of core parameters.

[0260] Marine environmental parameters are collected collaboratively by nearshore hydrological monitoring buoys, miniature meteorological sensors carried by drones, and environmental perception modules built into radar terminals.

[0261] Among them, sea surface conductivity Parameters such as water medium characteristics are monitored by buoys at fixed points over a long period of time, including wind speed. Humidity, atmospheric refractive index Meteorological parameters such as sea surface roughness are measured in real time by drones. The results were derived from meteorological parameters and fluid dynamics principles, and the derived relationships satisfy:

[0262] ( (For fluid dynamics correlation functions).

[0263] The UAV's RIS status parameters are collected using a GPS / BeiDou dual-mode positioning module, a high-precision attitude sensor, and a trajectory tracking module, including real-time spatial position. Roll / Pitch / Yaw attitude angles Hovering stability and preset trajectory deviation ;

[0264] in, The radar terminal deployment parameters, including fixed radar coordinates, are initially calibrated and updated in real time during the deployment phase, based on the preset trajectory position. Antenna array mounting attitude, transmit / receive module operating status, and reference orientation, etc.

[0265] Parameter synchronization and preprocessing mechanism: Time alignment of multi-source parameters is achieved through a unified time synchronization protocol, eliminating calibration deviations caused by timing differences in acquisition from different devices, and ensuring that all parameters participate in formula calculations based on the same time reference; The preprocessing stage mainly completes data denoising, anomaly removal, and format standardization. The acquired parameters are smoothed and filtered to remove random noise and transient interference from sensors. Abnormal parameters that exceed the physical reasonable range are marked and replaced, and parameters of different formats are uniformly converted into standardized data in a Cartesian coordinate system.

[0266] At the same time, through the time series smoothing formula The fluctuation parameters are preprocessed to ensure the stability of the input data, providing a unified and reliable input format for subsequent error modeling and compensation formula calculations. For any environment or equipment parameter, To smooth the time window.

[0267] (II) Error source analysis: coupling mechanism and influence path analysis.

[0268] After parameter preprocessing, it is necessary to systematically analyze the generation mechanism, influence path, and coupling characteristics of various error sources, clarify the way different errors affect the positioning results, and provide a theoretical basis for establishing compensation formulas. The specific analysis is as follows:

[0269] Sea surface environment coupling error: Sea surface environmental parameters cause positioning errors by affecting the propagation characteristics and echo quality of ground wave signals.

[0270] Among them, sea surface conductivity The propagation attenuation of ground wave signals is directly affected by the properties of the water medium. Changes in the medium properties cause a shift in the signal propagation velocity, satisfying... ,in For signal speed in standard media, This is a medium influence function, which in turn causes distance calculation errors;

[0271] wind speed Change sea surface roughness The clutter intensity and phase stability of the echo signal are affected by the multipath reflection interference generated by the rough surface, which hinders effective echo extraction and leads to errors in azimuth and elevation angle calculations. The phase interference amount meets the requirements. ,in Roughness-phase correlation function; atmospheric refractive index The degree of curvature of the signal propagation path affects signal refraction, further exacerbating positioning errors. The amount of curvature in the propagation path must satisfy... , This represents the linear propagation distance.

[0272] Regarding the state error of the UAV RIS (Reflection Path Controller): As a core node for signal modulation, the state fluctuation of the UAV RIS directly affects the propagation accuracy of the auxiliary reflection path. RIS position offset leads to errors in the calculation of the reflection path length, and the path deviation satisfies:

[0273] ;

[0274] The angle between the position offset direction and the path direction; attitude angle fluctuations change the reflection angle of the RIS panel, causing the reflected beam to deviate from the target area. The beam offset angle satisfies:

[0275] ;

[0276] in, The attitude-beam correlation function is used; fluctuations in the operating state of the RIS unit cause deviations in signal modulation effects, which in turn lead to positioning errors.

[0277] Regarding radar terminal deployment errors: Deviations in radar terminal deployment status are systematic errors and require real-time correction.

[0278] The deviation of the antenna array attitude from the horizontal reference causes an initial azimuth shift in the transmitted beam, satisfying:

[0279] ;

[0280] The antenna attitude-azimuth correlation function; radar deployment coordinate reference drift causes absolute position calculation deviation, and the drift amount satisfies:

[0281] ;

[0282] The initial calibration coordinates; fluctuations in the operating status of the transmit / receive modules cause signal phase deviations, which must meet the following conditions. , For clock synchronization deviation, This is the time difference-phase correlation function.

[0283] Error coupling characteristics: Various errors form a coupling effect. For example, increased wind speed causes both sea surface roughness error and affects the hovering stability of the UAV, leading to the superposition of state errors. The coupled errors satisfy the following:

[0284] ;

[0285] Due to environmental error, For RIS state error, For radar deployment errors, Since this is a coupling error term, dynamic calibration requires the establishment of a comprehensive compensation model based on this coupling formula.

[0286] (III) Layered compensation: Progressive correction of coupling error.

[0287] Based on the error source analysis results, dynamic calibration adopts a "hierarchical compensation + coupling trade-off" strategy, progressively correcting environmental errors, RIS state errors, and radar deployment errors. This is combined with a series of quantization formulas to achieve precise compensation. The specific implementation logic of each level of compensation is as follows:

[0288] Sea surface environment error compensation: First, a correlation model between environmental parameters and positioning error is established. Based on signal propagation theory, the coupling relationship between changes in environmental parameters and positioning deviation is quantified, and the distance deviation is derived. Azimuth deviation The calculation formula:

[0289] ;

[0290] This is the original distance calculation result. , This is the result of the original azimuth angle calculation.

[0291] During the compensation process, based on the geometric transformation formula between spherical and rectangular coordinates, the distance and azimuth deviations are mapped into coordinate compensation components in the rectangular coordinate system. ={ Δ , The conversion formula is:

[0292] ;

[0293] in, This is the original pitch angle calculation result. To address the path bending deviation caused by signal refraction, [further steps are taken]. Correct the distance deviation, and then substitute it into the coordinate transformation formula above to adjust the compensation component.

[0294] During the compensation process, a coupled trade-off formula is used. , The compensation weight for the i-th type of environmental error, The coupling coefficient is used to adjust the compensation weights for each environmental error, avoiding overcompensation or undercompensation.

[0295] Unmanned Aerial Vehicle (UAV) RIS Status Error Compensation: A path propagation error compensation formula is established based on the deviation between the real-time RIS status and the preset benchmark.

[0296] For the RIS position offset error, the reflection path length deviation is derived. The unit vector ensures that the compensation direction is consistent with the position offset direction, and then, combined with geometric relationships, it is converted into coordinate compensation components. :

[0297] ,

[0298] To address the attitude angle deviation of the RIS, by After calculating the beam offset angle and correcting the azimuth and elevation angles, the attitude error compensation components are obtained by substituting them into the coordinate transformation formula. .

[0299] To address the fluctuations in the operating status of RIS cells, by... , The average reflection coefficient of the RIS unit is used to correct the path deviation, and then superimposed into the total compensation component. This hierarchical compensation is achieved through a weighting formula. In conjunction with environmental error compensation, the compensation weight is dynamically adjusted.

[0300] Radar terminal deployment error compensation: Based on deployment reference parameters, systematic errors are corrected in real time.

[0301] To address antenna array attitude deviations, by Calculate the initial azimuth offset of the beam, correct the azimuth and elevation angles, and then substitute them into the coordinate transformation formula to obtain the compensation component. To address radar deployment coordinate drift, through (Reverse superposition drift) corrects for absolute position deviation; for phase deviation of the transmit / receive modules, through... This is converted into distance deviation and then mapped into coordinate compensation components. .

[0302] Compensation superposition and coupling correction: After compensation at each level is completed, the superposition formula is used... , Deploy error compensation weights for radar to meet the following requirements. + + = 1, obtaining preliminary calibration results = + , This is the original location result.

[0303] Meanwhile, based on the coupling error formula The superposition results are fine-tuned to correct deviations caused by coupling effects, forming an iterative mechanism of "compensation-feedback-optimization," with the iterative formula being: ,in For the number of iterations, The iteration step size is set until the coupling error is less than the threshold.

[0304] (iv) Anomaly verification: a dual screening mechanism for calibration results.

[0305] To prevent calibration results from becoming invalid due to abnormal parameters or deviations in the compensation formula, a dual anomaly verification mechanism is established, which combines quantization formulas to screen valid data. The specific implementation logic is as follows:

[0306] Absolute deviation verification: Based on the system's detection capabilities and scene characteristics, a reasonable range boundary for the positioning results is set, and the boundary is determined by the formula. ,in This is the maximum detection range of the radar. This is the azimuth unit vector, dynamically adjusted. If the preliminary calibration results meet... If the data is abnormal, it will be removed directly.

[0307] Relative deviation verification: Stability is determined using the time series consistency formula, and the mean deviation between the current result and the historical valid results is calculated. , For historical data volume, if , If the data meets the consistency threshold, it is marked as suspicious data; if the condition is met multiple times consecutively, it is judged as abnormal data and removed; otherwise, it is retained and included in subsequent verification.

[0308] Anomaly handling: Anomaly data is excluded from subsequent processes, but the parameters and formula calculation results at the time of the anomaly are recorded to provide a basis for model optimization; suspicious data is handled through a consistency formula based on subsequent rounds of localization results. , To verify and confirm the validity of subsequent data.

[0309] (v) Project implementation: Hardware and software support for real-time calibration.

[0310] The real-time performance and reliability of dynamic calibration rely on the collaboration of hardware platforms and software algorithms to ensure efficient and accurate formula calculations. Specific implementation details are as follows:

[0311] Hardware support system: The radar terminal data processing center has a built-in FPGA chip and real-time operating system. The FPGA chip pre-compiles error correlation formulas, coordinate transformation formulas, and compensation amount iterative calculation logic, and realizes real-time calculation of multi-source parameters through parallel computing to reduce calculation latency; it is equipped with a high-precision time synchronization module to ensure the alignment of timestamps of parameters from multiple devices and meet the timing consistency requirements of formula calculation; and it establishes an anti-interference data transmission link to ensure real-time parameter transmission and avoid interruption of formula calculation due to data loss.

[0312] Software algorithm optimization: An "expanded formula + lookup table mechanism" is employed to improve efficiency. The calculation results of common nearshore environmental parameter combinations and corresponding compensation formulas are pre-stored in a lookup table. During actual runtime, this table is directly called and the formula Δ is fine-tuned. Adjustment, To address the discrepancy between real-time and pre-stored parameters, an adaptive algorithm is introduced, which uses formulas to adapt to changes in the scene. Dynamically optimize compensation weights. The system assigns compensation weights to each level and develops an online diagnostic module to monitor the formula calculation status in real time. If an anomaly occurs, it automatically switches to a backup compensation formula to ensure uninterrupted calibration.

[0313] Data synchronization and fault tolerance mechanisms: Parameter time alignment is achieved through a distributed time synchronization protocol based on interpolation formulas. Add missing parameters to avoid interrupting formula calculations due to the absence of a single parameter.

[0314] (vi) Closed-loop optimization: continuous iterative upgrade of the calibration model.

[0315] Dynamic calibration forms a closed loop through continuous verification and formula optimization, specifically implemented as follows:

[0316] Calibration effectiveness verification: At regular intervals, cross-validation formulas are used to verify the results. Assess calibration accuracy. For independent sensor measurement results, the deviation values ​​under different scenarios are statistically analyzed.

[0317] Model parameter optimization: Based on the validation results, the least squares formula was used. Parameters and weights of the dynamic correction error correlation formula. For formula parameters, To validate the sample size and improve model adaptability.

[0318] Adaptive scene switching: based on environmental parameters using formulas The system assesses the complexity of the scenario and automatically switches between compensation formulas and weighting strategies to balance calibration accuracy and real-time performance.

[0319] II. Moving average filtering: precise suppression of temporal random noise.

[0320] Even after dynamic calibration and anomaly verification, the effective positioning results still contain random noise interference, which needs to be suppressed by moving average filtering. Combined with a weighted formula, this balances real-time performance and stability. The specific implementation logic is as follows:

[0321] Adaptive configuration of the sliding window: Based on the target motion characteristics and noise characteristics, the window length and weight allocation are dynamically adjusted, and the weights are calculated using a linear weighting formula. In the allocation, recent data is given higher weights to ensure that the weight sum is 1. For the data sequence number within the window, The window length is given by the formula. Optimize selection, As an indicator of noise suppression effectiveness, This is a lagging indicator.

[0322] Real-time calculation of the filtering process: based on the effective positioning results after dynamic calibration. Using the input as input, the filtering result is calculated using the weighted moving average formula. Decomposed into components of each coordinate axis: During the calculation, the window data is updated in real time, historical data that exceeds the range is removed, and the latest valid results are included to ensure that the filtering and positioning processes are synchronized. Simultaneously, a stability judgment formula is used. Dynamically adjust the window length and weights to optimize the filtering effect.

[0323] Secondary verification of the filtering effect: through the noise suppression formula Evaluate the effectiveness to ensure that noise suppression meets standards and that the results lag meets the requirements for real-time tracking. This represents the standard deviation of the noise before filtering.

[0324] III. Particle Filter Optimization: In-depth Correction of Nonlinear Bias.

[0325] Nonlinear deviations in nearshore scenarios need to be corrected using particle filtering, and precise optimization can be achieved by combining formulas such as state transition and weight calculation. The specific implementation logic is as follows:

[0326] Core model construction: Based on the uniform acceleration maneuver model, the particle state transition formula and particle state vector are constructed. The state transition formula is: ,in Here is the state transition matrix. Let be the process noise vector, following a Gaussian distribution. The observation model is constructed based on radar positioning principles, and the observation formula is: ,in Extract the position components from the observation matrix; To observe noise.

[0327] The complete process of particle filtering is as follows:

[0328] Particle initialization: Generate a large number of particles, and the initial state is determined by the formula. Determine the possible states of the target by superimposing random perturbations, where The initial noise variance matrix, It follows a Gaussian distribution.

[0329] Particle prediction: The state of each particle is updated using a state transition formula to simulate the target's motion state at the next moment, minimizing process noise. Through formula Dynamic adjustment, among which Let be the process noise variance matrix.

[0330] Weight Update: Particle weights are calculated using the Gaussian likelihood function, as shown in the formula below. Through the normalization formula Ensure that the weight sum is 1, where This represents the total number of particles.

[0331] Resampling: Residual resampling is used to retain high-weight particles, through the formula... Copying and expanding to avoid particle degradation, among which This refers to the index of the high-weighted particles.

[0332] State estimation: using the weighted mean formula The optimized positioning result is obtained and decomposed into components of each coordinate axis.

[0333] Enhanced optimization effect: Introducing a particle diversity preservation formula Replenish the number of particles. The disturbance coefficient is... The identity matrix is ​​calculated using the formula based on environmental complexity. The number of particles is dynamically adjusted, among which To estimate the error, To calculate the time consumption, the dynamic calibration results are combined using the formula. The observation matrix was corrected to improve optimization accuracy.

[0334] IV. End-to-end collaboration and performance assurance.

[0335] The entire module forms a complete positioning optimization chain through the coordinated work of dynamic calibration, moving average filtering, and particle filtering. Formulas serve as the core tools throughout the entire process: dynamic calibration eliminates systematic deviations through coupling error formulas and compensation superposition formulas; moving average filtering suppresses random noise through weighted formulas; and particle filtering corrects nonlinear deviations through state transition and weight calculation formulas. The project employs hardware acceleration and software optimization strategies, using FPGA chips to ensure formula calculation speed, multi-device time synchronization protocols to ensure data timing consistency, and online diagnostics and fault tolerance mechanisms to handle equipment failures and parameter anomalies.

[0336] The final positioning result output by the module It achieves high precision, high stability, and low latency through full-process formula calculation, effectively adapting to the mobile deployment and maritime defense early warning needs of complex near-shore and island / reef scenarios. It provides core data support for subsequent stages such as target dynamic tracking and threat identification. At the same time, through a closed-loop optimization mechanism, it continuously corrects formula parameters and weights, improving the module's adaptability and positioning accuracy in different scenarios.

[0337] The target dynamic tracking and data feedback storage module is the "final application stage" of the entire nearshore and island positioning system. Based on the high-precision positioning results optimized in step 8, it completes the continuous dynamic tracking of targets, real-time data flow, and full-link data accumulation, providing direct and long-term support for scenarios such as coastal defense early warning and nearshore security.

[0338] The terminal data processing center first uses the optimized positioning results (i.e., the target is in) The system continuously updates the target's trajectory using its Cartesian coordinates at each moment. Considering that targets in near-shore scenarios often exhibit a hybrid motion characteristic of "uniform cruising + sudden acceleration," the system employs a "uniform speed-uniform acceleration hybrid model" to predict the target's position at the next moment. The core derivation formula of this model is: - in yes The target state vector at time t, encompassing position ( ),speed( ) and acceleration ( Information in three dimensions This is a state transition matrix adapted to hybrid motion modes. Its matrix elements will dynamically switch according to the real-time motion characteristics of the target. Through this model, the system can accurately predict the next position of the target and avoid tracking lag. At the same time, the data processing center will send instructions to the UAV RIS platform in real time to adjust the attitude angle and unit phase parameters of the RIS panel in advance to ensure that the RIS reflection beam is always aligned with the maneuvering target and eliminate the risk of echo signal attenuation caused by target movement.

[0339] While dynamically tracking and advancing, the system simultaneously initiates a real-time data feedback process: the current positioning results and target movement trajectory data are fed back to terminal nodes such as the coastal defense command center and the near-shore emergency monitoring platform through the "microwave + backup" two-way communication link established in step four.

[0340] Finally, there is the end-to-end data storage and management: the system will uniformly store multi-dimensional core data in a local database. This data storage not only supports subsequent fast and accurate queries, but also provides a foundation for system review and optimization. At the same time, the database supports authorized sharing, and can open data access permissions to other coastal defense-related platforms, providing long-term data accumulation support for joint security and multi-system collaborative early warning in nearshore areas.

[0341] Example 3

[0342] This embodiment provides a UAV-based RIS-assisted ground wave radar positioning device. For details not disclosed in this embodiment, please refer to the specific implementation details of the UAV-based RIS-assisted ground wave radar positioning method or system in other embodiments.

[0343] Figure 8 The diagram shows a schematic of the structure of a UAV-based RIS-assisted ground wave radar positioning device 40 according to an embodiment of this application.

[0344] like Figure 8 As shown, the UAV-based RIS-assisted ground wave radar positioning device 40 includes: a storage unit 402 for storing executable instructions; and a processing unit 401 for connecting to the storage unit 402 to execute the executable instructions to complete the UAV-based RIS-assisted ground wave radar positioning method.

[0345] Those skilled in the art will understand that the illustration Figure 8 This is merely an example of a UAV-based RIS-assisted ground wave radar positioning device 40 and does not constitute a limitation on the UAV-based RIS-assisted ground wave radar positioning device 40. It may include more or fewer components than shown, or combine certain components, or different components. For example, the UAV-based RIS-assisted ground wave radar positioning device 40 may also include input / output devices, network access devices, buses, etc.

[0346] The processing unit 401 (Central Processing Unit, CPU) can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processing unit 401 can be any conventional processor. The processing unit 401 is the control center of the UAV-based RIS-assisted ground wave radar positioning device 40, connecting all parts of the UAV-based RIS-assisted ground wave radar positioning device 40 through various interfaces and lines.

[0347] Storage unit 402 can be used to store computer-readable instructions. Processing unit 401 implements various functions of the UAV-based RIS-assisted ground wave radar positioning device 40 by running or executing the computer-readable instructions or modules stored in storage unit 402 and calling the data stored in storage unit 402. Storage unit 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the UAV-based RIS-assisted ground wave radar positioning device 40, etc. In addition, storage unit 402 may include hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, read-only memory (ROM), random access memory (RAM), or other non-volatile / volatile storage devices.

[0348] If the module integrated in the UAV-based RIS-assisted ground wave radar positioning device 40 is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium, and when executed by a processor, they can implement the steps of the various method embodiments described above.

[0349] Example 5

[0350] This embodiment provides a computer-readable storage medium on which a computer program is stored; the computer program is executed by a processor to implement the UAV-based RIS-assisted ground wave radar positioning method in other embodiments.

[0351] The UAV-based RIS-assisted ground wave radar positioning device and storage medium described in this application transmit high-frequency ground wave detection signals through a radar terminal, simultaneously transmitting these signals to the target via a direct path and a RIS-assisted path. The RIS-assisted path is a detection path where the ground wave detection signal is first transmitted to the UAV RIS-equipped platform for reflection. Then, based on the echo signal from the direct path, the echo signal from the RIS-assisted reflection path, and the real-time position of the UAV RIS-equipped platform, the target position information is calculated. This application introduces dynamically adjustable RIS reflection nodes to construct a new detection system that integrates direct and reflected dual-path signals, actively enhancing signal quality in non-line-of-sight and complex environments. By utilizing complementary dual-path information and intelligent closed-loop control, it significantly improves the system's positioning accuracy, environmental adaptability, and target tracking capability, while also significantly reducing positioning errors through dynamic calibration.

[0352] Those skilled in the art will understand that the terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” as used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0353] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0354] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0355] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A ground-wave radar positioning method based on UAV RIS-assisted localization, characterized in that, Includes the following steps: The radar terminal transmits a high-frequency ground wave detection signal, which is simultaneously transmitted to the target via a direct path and a RIS-assisted path. The RIS-assisted path is the detection path through which the ground wave detection signal is transmitted to the RIS-equipped platform of the UAV and reflected back to the target. The UAV RIS platform is equipped with a RIS panel for receiving the ground wave detection signal and for generating a controllable reflected beam to the target by adjusting the electromagnetic properties of the RIS units on its surface. Simultaneously receive and distinguish between direct path echo signals and RIS-assisted reflection path echo signals; Target location information is calculated based on the direct path echo signal, the RIS-assisted reflection path echo signal, and the real-time location of the UAV RIS-equipped platform. The calculation of target location information includes: The time delay between the direct path echo signal and the RIS-assisted reflection path echo signal is extracted using a cross-correlation algorithm. Based on the signal-to-noise ratio of the direct path echo signal and the RIS-assisted reflection path echo signal, fusion weights are assigned to the two signals. Then, a weighted fusion algorithm is used to fuse the direct path echo signal and the RIS-assisted reflection path echo signal to obtain a fused signal. Based on the time delay of the direct path echo signal and the RIS-assisted reflection path echo signal, the fused signal, and the real-time position of the UAV RIS-equipped platform, the fused distance between the target and the radar is calculated through geometric relationships. After calculating the target location information, the method further includes determining the target coordinates based on the target location information. After determining the target coordinates based on the target location information, the method further includes performing a hierarchical compensation step to dynamically calibrate the target coordinates. The hierarchical compensation step to dynamically calibrate the target coordinates includes: Collect sea surface environmental parameters, UAV RIS-equipped platform status parameters, and radar terminal deployment parameters; Based on the sea surface environment parameters, the distance deviation and azimuth deviation caused by the sea surface environment are calculated through a pre-established correlation function and mapped to the first coordinate compensation component; Based on the position offset and attitude angle deviation of the UAV RIS-equipped platform state parameters, the reflection path length deviation and beam offset angle are calculated and converted into second coordinate compensation components. Based on the radar terminal deployment parameters, the systematic error is calculated and converted into a third coordinate compensation component. The first coordinate compensation component, the second coordinate compensation component, and the third coordinate compensation component are weighted and superimposed to obtain the total calibration compensation amount, which is used to correct the initial positioning target coordinates.

2. The ground wave radar positioning method according to claim 1, characterized in that, After the UAV RIS-equipped platform forms a controllable reflected beam and emits it to the target, it also includes: Receive the target's auxiliary detection return signal, and then reflect the RIS auxiliary reflection path echo signal; The attitude of the UAV RIS-mounted platform or the phase distribution of its surface units are dynamically adjusted based on the signal quality of the auxiliary detection return signal.

3. The ground wave radar positioning method according to claim 1, characterized in that, Determining the target coordinates based on the target location information includes: The radar uses an antenna array to perform beam scanning and obtain the coarse azimuth value of the target. The precise azimuth and elevation angles of the target are calculated using the echo phase difference measured between adjacent elements of the antenna array; Based on the fused distance, precise azimuth, and elevation angle between the target and the radar, the three-dimensional coordinates of the target are determined by converting spherical coordinates to rectangular coordinates.

4. The ground wave radar positioning method according to claim 3, characterized in that, After the step of performing hierarchical compensation to dynamically calibrate the target coordinates, the method further includes: By using a moving average filtering step, the positioning results of the dynamically calibrated target coordinates are subjected to a time-weighted average to suppress random noise. Through a particle filtering optimization step, based on the target motion model and the observation model, nonlinear deviation correction is performed on the filtered positioning results; The dynamic error calibration step, the moving average filtering step, and the particle filtering optimization step form a three-level positioning result optimization link.

5. A ground-wave radar positioning system based on UAV RIS-assisted positioning, characterized in that, include: The ground wave radar terminal is used to transmit high-frequency ground wave detection signals and simultaneously transmit the ground wave detection signals to the target through a direct path and a RIS-assisted path for detection; the RIS-assisted path is the detection path in which the ground wave detection signal is first transmitted to the RIS-equipped platform of the UAV for reflection; it is also used to synchronously receive and distinguish the echo signal from the direct path and the echo signal from the RIS-assisted reflection path; The UAV RIS platform is equipped with a RIS panel for receiving the ground wave detection signal and for generating a controllable reflected beam to the target by adjusting the electromagnetic properties of the RIS units on its surface. The data processing center is used to calculate the target location information based on the direct path echo signal, the RIS-assisted reflection path echo signal, and the real-time location of the UAV RIS-mounted platform. The calculation of target location information includes: The time delay between the direct path echo signal and the RIS-assisted reflection path echo signal is extracted using a cross-correlation algorithm. Based on the signal-to-noise ratio of the direct path echo signal and the RIS-assisted reflection path echo signal, fusion weights are assigned to the two signals. Then, a weighted fusion algorithm is used to fuse the direct path echo signal and the RIS-assisted reflection path echo signal to obtain a fused signal. Based on the time delay of the direct path echo signal and the RIS-assisted reflection path echo signal, the fused signal, and the real-time position of the UAV RIS-equipped platform, the fused distance between the target and the radar is calculated through geometric relationships. After calculating the target location information, the method further includes determining the target coordinates based on the target location information. After determining the target coordinates based on the target location information, the method further includes performing a hierarchical compensation step to dynamically calibrate the target coordinates. The hierarchical compensation step to dynamically calibrate the target coordinates includes: Collect sea surface environmental parameters, UAV RIS-equipped platform status parameters, and radar terminal deployment parameters; Based on the sea surface environment parameters, the distance deviation and azimuth deviation caused by the sea surface environment are calculated through a pre-established correlation function and mapped to the first coordinate compensation component; Based on the position offset and attitude angle deviation of the UAV RIS-equipped platform state parameters, the reflection path length deviation and beam offset angle are calculated and converted into second coordinate compensation components. Based on the radar terminal deployment parameters, the systematic error is calculated and converted into a third coordinate compensation component. The first coordinate compensation component, the second coordinate compensation component, and the third coordinate compensation component are weighted and superimposed to obtain the total calibration compensation amount, which is used to correct the initial positioning target coordinates.

6. The ground wave radar positioning system according to claim 5, characterized in that, It also includes a positioning calibration module; the positioning calibration module sequentially performs dynamic error calibration, moving average filtering and particle filtering optimization to form a three-level positioning result optimization link.

7. A ground-wave radar positioning device based on UAV RIS-assisted positioning, characterized in that, include: Storage unit, used to store executable instructions; as well as A processing unit is configured to be connected to a memory to execute executable instructions to perform the method as described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, It stores a computer program thereon; the computer program is executed by a processor to implement the method as described in any one of claims 1-4.

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