Method and related device for detecting environment perception and electromagnetic deception based on smart reflective surface

Abstract

The embodiment of the application provides a kind of based on smart reflecting surface detection environment perception and electromagnetic deception method and related equipment, belong to wireless communication technical field.The method first utilizes L type perception array embedded on target to receive radar signal, accurately estimates the channel parameters of radar and environment scatterer cluster, including angle of arrival and signal power gain by multiple signal classification algorithm and least square algorithm;Subsequently, under the premise of strictly limiting radar received target echo power below detection threshold, an optimization model is established to maximize the selected decoy cluster echo power;Finally, the closed-form or semi-closed-form solution of IRS reflection coefficient is derived using the Lagrange multiplier method or phase alignment method, and the IRS is configured accordingly.The application can efficiently generate false targets using the scatterers in the surrounding environment to confuse radar detection, while achieving true target stealth, with low computational complexity and high real-time performance, and can be widely applied in the electronic countermeasure field of mobile platforms such as aircraft and missiles.

Description

Technical Field

[0001] This application relates to the fields of wireless communication and radar electronic countermeasures technology, and in particular to a detection environment perception and electromagnetic deception method and related equipment based on intelligent reflective surfaces. Background Technology

[0002] With the continuous development of radar detection technology and signal processing capabilities, radar performance in target detection, localization, and identification has been continuously improved, posing greater challenges to the concealment of targets in complex electromagnetic environments. Traditional electronic countermeasures methods are no longer able to maintain stable and effective countermeasures under dynamic and changing detection conditions over long periods. Existing electromagnetic countermeasures technologies mainly include two categories: electromagnetic stealth and electromagnetic deception. Electromagnetic stealth typically involves introducing absorbing materials or special structures onto the target surface to reduce the target's radar cross-section, thereby weakening the radar echo signal strength. However, this type of method is usually quite sensitive to the operating frequency band and incident angle, and has limited environmental adaptability. Electromagnetic deception technology, on the other hand, misleads radar judgment by constructing false echoes or decoy targets. Passive deception methods lack flexible control capabilities, while active deception methods rely on active transmitting equipment, which is hardware-complex, consumes a lot of power, and carries the risk of revealing the target's location.

[0003] In recent years, intelligent reflecting surfaces (IRS) have attracted widespread attention as a reconfigurable electromagnetic control technology. IRS consists of a large number of passive reflecting elements, which can flexibly reconstruct incident electromagnetic waves by adjusting the phase and amplitude of the reflecting elements, thereby achieving precise control over the reflection direction and energy distribution. Existing research has shown that applying IRS to target surfaces can suppress radar echoes to a certain extent, providing a new approach to electromagnetic stealth.

[0004] However, existing research on IRS (Infrared Reflection System) largely focuses on single stealth or enhancement effects, with insufficient attention paid to how to rationally guide reflected energy and construct effective decoy echoes to achieve electromagnetic deception while meeting radar detection threshold constraints. Furthermore, IRS reflection design typically relies on obtaining environmental information such as the direction of arrival and signal strength; how to implement feasible and low-complexity reflection control schemes under conditions of limited system complexity still requires further research. Summary of the Invention

[0005] The main objective of this application is to propose a detection environment perception and electromagnetic deception method, electronic device, storage medium, and program product based on intelligent reflectors. This aims to address the problems of existing intelligent reflector technologies, such as a lack of proactive environment perception, heavy reliance on prior information from external channels, and difficulty in balancing theoretical performance and real-time response under high-intensity single-radar detection. This application utilizes an embedded L-shaped sensor array to perceive and estimate the channel parameters of the radar and environmental scatterers in real time. It then combines the Lagrange multiplier method or phase alignment method to quickly solve for the optimal reflection coefficient. This constructs a closed-loop countermeasure mechanism of "perception-computation-reflection" without prior information, achieving efficient directional stealth against single-radar detection and highly realistic deception of false targets. Furthermore, this method has low computational complexity and is suitable for real-time electromagnetic countermeasure scenarios.

[0006] To achieve the above objectives, one aspect of this application proposes a detection environment perception and electromagnetic deception method based on a smart reflector, applied to a moving target equipped with a smart reflector IRS and an L-shaped sensor array LSA, in a scenario where a single-base station radar and a cluster of environmental scatterers coexist. The method includes the following steps: Step 1: Establish a system model, which includes a moving target equipped with IRS and LSA, a single-base station radar, and selected decoys. A cluster of environmental scatterers, among which ; Step 2: Utilize the LSA to receive radar detection signals and scatterer cluster echo signals, and estimate channel parameters based on the received signals. The channel parameters include at least the angle of arrival (AoA) of the radar and each scatterer cluster relative to the target, as well as the effective signal power gain between the target and the radar and each scatterer cluster. Step 3: Based on the channel parameters estimated in Step 2, construct an optimization problem for the IRS reflection coefficient; the objective function of the optimization problem is to maximize the deception signal power received by the radar from the direction of a selected single scatterer cluster, and the constraints include that the power of the real target echo signal received by the radar from the target direction is lower than a preset detection threshold, and the magnitude of the reflection coefficient of each reflection unit of the IRS is not greater than 1. Step 4: Solve the optimization problem to obtain the optimal IRS reflection coefficient; Step 5: Based on the optimal IRS reflection coefficient obtained in Step 4, configure the phase and / or amplitude of each reflection unit of the IRS to form an enhanced decoy echo signal pointing towards the selected scatterer cluster at the radar, while simultaneously suppressing the echo signal in the direction of the real target.

[0007] In some embodiments, estimating the channel parameters in step 2 specifically includes: Step 2.1: Based on the signals received by the LSA, the Multiple Signal Classification (MUSIC) algorithm is used to estimate the angle of arrival (AoA) of the radar and each scatterer cluster relative to the target; Step 2.2: Based on the signal received by the LSA and the estimated angle of arrival, construct the array response matrix, and use the least squares algorithm to estimate the equivalent incident signal arriving at the target from the radar and each scatterer cluster, thereby obtaining the corresponding effective signal power gain.

[0008] In some embodiments, the optimization problem of constructing the IRS reflection coefficient in step 3 specifically includes: Step 3.1: Based on the radar direction AoA and signal power gain estimated in Step 2, construct the expression for the signal power received by the radar from the target direction. ; Step 3.2: Based on the direction AoA and signal power gain of each scatterer cluster estimated in Step 2, construct the expression for the signal power received by the radar from each scatterer cluster direction. ; Step 3.3: From One scatterer cluster is selected as the decoy target, and an optimization problem is constructed. The objective function of the optimization problem is to maximize the signal power in the direction of the selected scatterer cluster. The constraints are ,as well as ,in The preset radar detection threshold, For the first The reflection coefficient of each IRS unit, This is the set of IRS reflection unit indices.

[0009] In some embodiments, solving the optimization problem in step 4 employs a solution algorithm based on the Lagrange multiplier method, including: Step 4.1: Introduce the Lagrange multipliers corresponding to the stealth constraint and the Lagrange multiplier vector corresponding to the IRS reflection coefficient magnitude constraint to construct the Lagrange function of the optimization problem; Step 4.2: Set the partial derivative of the Lagrange function with respect to the IRS reflection coefficient vector to zero, and derive the semi-closed optimal solution expression for the IRS reflection coefficient, which is a function of the Lagrange multipliers; Step 4.3: Obtain the optimal Lagrange multipliers by solving the dual problem of the optimization problem; Step 4.4: Substitute the optimal Lagrange multiplier obtained in Step 4.3 into the semi-closed solution in Step 4.2 to obtain the final optimal IRS reflection coefficient.

[0010] In some embodiments, solving the dual problem in step 4.3 further includes: using Schur complement to equivalently transform the dual problem into a standard semidefinite program (SDP) problem or a linear matrix inequality (LMI) problem for solving.

[0011] In some embodiments, solving the optimization problem in step 4 further includes a fast solution algorithm based on the phase alignment method, used to meet extremely low latency requirements or as a system performance benchmark, specifically including: Step 4A: Relax the hidden constraints in the optimization problem; Step 4B: Based on the phase alignment principle, the reflection coefficient vector of the IRS is directly set to be conjugate aligned with the response vector of the cascaded array from the target to the selected scatterer cluster, thereby obtaining a fast closed-form solution for the reflection coefficient. The objective function value corresponding to this solution is used as the theoretical upper bound of the system's deception performance.

[0012] In some embodiments, selecting the scattering cluster for the decoy in step 3.3 further includes: selecting scattering clusters from targets at a distance greater than a preset threshold. Candidate scatterer cluster set In the process, the effective incident signal power reaching the target is selected. The largest cluster of scatterers is used as a decoy target.

[0013] In some embodiments, the system model in step 1 is further defined as follows: The IRS is a uniform planar array (UPA) covering the target surface, containing... One passive reflective unit; The LSA is an L-shaped array embedded in the target, consisting of... It consists of a series of receiving and sensing devices; The radar is a monostation radar equipped with a uniform planar array, and its total number of transceiver antennas is [number missing]. ; The target is equipped with an intelligent controller for dynamically adjusting the IRS reflection coefficient and coordinating the LSA sensing mode with the IRS reflection mode.

[0014] In some embodiments, the channel parameters estimated in step 2 may also include the departure angle AoD of the radar and each scatterer cluster relative to the target.

[0015] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0018] Compared with the prior art, this application has the following significant advantages: 1) Perception-Computation-Reflection Closed-Loop Countermeasure Mechanism: This application is the first to deploy an L-shaped sensor array and a smart reflector co-located, utilizing the LSA to perceive the channel parameters of the radar and environmental scatterers in real time, and dynamically optimizing the IRS reflection coefficient based on this to construct a complete closed-loop countermeasure system. This mechanism breaks the dependence of traditional technologies on prior channel information, enabling the target to autonomously achieve efficient countermeasures in an unknown dynamic environment.

[0019] 2) Synergistic Optimization of Stealth and Deception: The optimization model established in this application considers both stealth constraints (suppressing echoes from the target direction) and deception (enhancing echoes from the decoy direction), achieving synergy between the two by optimizing the IRS reflection coefficient. Simulation results show that this method can stably suppress the echo power from the target direction below the detection threshold, while significantly enhancing the echoes from the selected scatterer cluster direction, forming highly realistic false targets and effectively misleading radar decisions.

[0020] 3) Flexibility and efficiency of algorithm design: This application proposes two complementary solution algorithms for optimization problems. The Lagrange multiplier method can obtain the optimal semi-closed solution that satisfies the stealth constraint, ensuring the theoretical performance of the system; the phase alignment method provides a fast closed solution in extremely low latency scenarios and can be used as an upper bound reference for system performance. This dual-track design balances optimal performance and real-time response, adapting to different application requirements.

[0021] 4) Low computational complexity and real-time response capability: By deriving the (semi)closed-form solution, this application avoids complex iterative optimization processes and significantly reduces computational latency. Combining prior information prediction of the target flight trajectory with an offline pre-storage strategy, it can realize real-time invocation and configuration of the IRS reflection coefficient, meeting the high real-time requirements in electromagnetic countermeasures scenarios.

[0022] 5) System robustness and engineering practicality: This application analyzes the impact of channel estimation error on system performance, proving that effective deception can still be maintained within a certain error range. The system architecture is based on existing IRS and array signal processing technologies, with low hardware complexity, no need for additional active transmission equipment, low power consumption, and easy integration and implementation on mobile platforms such as aircraft and missiles, demonstrating good engineering practical value and promising prospects for widespread application. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of an electromagnetic deception system equipped with an intelligent reflective surface, provided in an embodiment of this application.

[0024] Figure 2 This is an optional flowchart of the intelligent reflective surface design method provided in the embodiments of this application.

[0025] Figure 3 This is a schematic diagram of the two-dimensional geometric positional relationship between the target, the environmental scattering body cluster, and the radar provided in the embodiments of this application.

[0026] Figure 4 This is a simulation result diagram showing the variation of radar received signal power with cluster horizontal position according to an embodiment of this application. Wherein, Figure 4 Figure (a) shows the signal power received by the radar from the direction of the target. Figure 4 Figure (b) shows the signal power received by the radar from the cluster direction.

[0027] Figure 5 This is a simulation result diagram showing the change of radar received signal power with radar detection threshold provided in the embodiments of this application. Among them, Figure 5 Figure (a) shows the signal power received by the radar from the direction of the target. Figure 5 Figure (b) shows the signal power received by the radar from the cluster direction.

[0028] Figure 6 This is a simulation result diagram showing the change of radar received signal power with direction of arrival angle provided in the embodiments of this application.

[0029] Figure 7 This is a simulation result diagram showing the impact of imperfect AoA information at the target location on the radar signal power received from the cluster direction, as provided in the embodiments of this application. Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0032] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0033] 1) Intelligent Reflecting Surface (IRS) is a novel wireless communication technology, also known as Reconfigurable Intelligent Surface (RIS) or Intelligent Metasurface. IRS technology utilizes large-scale reflective elements, such as micro-antennas or phase-adjusting elements, to construct a dynamically controllable surface. These reflective elements can regulate the reflection, propagation, and reception of wireless signals by changing their phase, amplitude, or polarization state as needed. In wireless communication systems, IRS can serve as an infrastructure to enhance or optimize wireless signal propagation. For example, by manipulating IRS, the propagation path of wireless signals can be altered, coverage increased, interference reduced, and the performance of the communication system improved.

[0034] With the continuous development of radar detection technology and signal processing capabilities, radar performance in target detection, localization, and identification has been continuously improved, posing greater challenges to the concealment of targets in complex electromagnetic environments. Traditional electronic countermeasures are finding it difficult to maintain stable and effective countermeasures under dynamic and changing detection conditions over the long term.

[0035] Existing electromagnetic countermeasures technologies mainly fall into two categories: electromagnetic stealth and electromagnetic deception. Electromagnetic stealth typically involves introducing absorbing materials or special structures onto the target surface to reduce the target's radar cross-section (RCS), thereby weakening the radar echo signal strength. However, this type of method is generally sensitive to operating frequency bands and incident angles, and its environmental adaptability is limited. Electromagnetic deception technology, on the other hand, misleads radar judgment by constructing false echoes or decoy targets. Passive deception methods lack flexible control capabilities, while active deception methods rely on active transmitting equipment, which is hardware-complex, consumes a lot of power, and carries the risk of revealing the target's location.

[0036] In recent years, intelligent reflective surfaces (IRS) have attracted widespread attention as a reconfigurable electromagnetic control technology. IRS consists of a large number of passive reflective elements, which can flexibly reconstruct incident electromagnetic waves by adjusting the phase and amplitude of the reflective elements, thereby achieving precise control over the reflection direction and energy distribution. Existing research has shown that applying IRS to target surfaces can suppress radar echoes to a certain extent, providing a new approach to electromagnetic stealth.

[0037] However, existing IRS-related research largely focuses on single stealth or enhancement effects, with insufficient attention paid to how to rationally guide reflected energy and construct effective decoy echoes to achieve electromagnetic deception while meeting radar detection threshold constraints. Furthermore, most existing schemes assume prior channel information about the target's radar and environmental scatterers, which is difficult to achieve in dynamic adversarial scenarios. IRS reflection design typically relies on acquiring environmental information such as the direction of arrival and signal strength; how to implement feasible, low-complexity reflection control schemes under conditions of limited system complexity still requires further research. More importantly, there is currently a lack of a closed-loop adversarial mechanism that integrates environmental perception, parameter estimation, and IRS reflection optimization design to achieve efficient directional stealth against single radar detection and high-fidelity deception of false targets.

[0038] In view of this, this application provides a detection environment perception and electromagnetic deception method, electronic device, storage medium and program product based on intelligent reflective surface. The scheme uses an L-shaped sensor array to perceive radar and environmental channel parameters in real time, and combines the Lagrange multiplier method or phase alignment method to quickly solve the optimal reflection coefficient, and constructs a closed-loop countermeasure mechanism of "perception-computation-reflection", thereby breaking the dependence of traditional technology on prior information of the channel and significantly improving the directional deception accuracy and system response speed under radar detection.

[0039] To better illustrate the technological advancements of the proposed method, the proposed intelligent reflector-based detection environment perception and electromagnetic deception method is compared with different benchmark schemes on the MATLAB platform. First, it is assumed that the target can acquire perfect information from the radar and scatterer cluster directions, including the angle of arrival and incident signal gain. Then, the impact of imperfect information estimated by the L-shaped sensing array at the target end on radar performance is further investigated. Furthermore, to compare with the proposed IRS-assisted electromagnetic deception system, this paper considers the following two baseline systems: 1) Baseline system without IRS: In this scheme, since no IRS is installed on the target, it can be directly ordered... .

[0040] 2) Baseline system with random phase: In this scheme, the reflection coefficient vector of the IRS The phase in the interval The contents are randomly generated according to a uniform distribution.

[0041] To better understand the above technical solution, the following will describe in more detail a detection environment perception and electromagnetic deception method based on an intelligent reflective surface disclosed in this embodiment, in conjunction with the accompanying drawings and specific implementation methods.

[0042] like Figure 1 As shown in the figure, an embodiment of this application discloses an electromagnetic deception and stealth implementation method based on a smart reflective surface, and its specific system design is as follows: This application considers an IRS-assisted electromagnetic deception system installed on a moving target (e.g., an aircraft) to evade detection and deceive. A distributed radar system, where the IRS is overlaid on the target surface, deceives radar detection by significantly reducing the radar cross-section (RCS) of the real target while simultaneously increasing the RCS of the surrounding scattering object clusters (acting as decoy targets). In the considered scenario, the number of scattering object clusters is denoted as . Without loss of generality, assume that the target-mounted IRS is a uniform planar array composed of... It consists of passive reflective units, among which and They represent along shaft and The number of IRS elements along the axial direction. For ease of description, the set of scatterer clusters and the set of IRS reflective elements are denoted as follows: and Furthermore, assuming the radar is a monostatic radar (i.e., the transmitter and receiver are co-located) and equipped with a uniform planar array, the total number of its transmitting and receiving antennas is... ,in and They represent along shaft and The number of transceiver antennas along the axial direction. The target is equipped with an intelligent controller capable of dynamically adjusting the reflection amplitude and / or phase of the IRS in real time, and coordinating the switching between two operating modes: a sensing mode for environmental reconnaissance and a reflection mode for electromagnetic deception. Furthermore, to enable the target to operate in the environmental reconnaissance sensing mode, an L-shaped sensing array is embedded at the target end, consisting of… It consists of a receiving sensing device, in which and They represent along shaft and Number of sensing devices along the axial direction.

[0043] like Figure 2 As shown, this embodiment provides an implementation method, including the following steps: Step 1: Establish a model of an intelligent reflector electromagnetic deception system in a single radar environment; the model includes a moving target equipped with an intelligent reflector and an L-shaped sensor array, a single base station radar, and a selected cluster of environmental scatterers.

[0044] Step 2: Utilize the L-shaped sensor array to receive radar detection signals and scatterer cluster echo signals. Estimate the angle of arrival of the radar and scatterer cluster relative to the target based on a multiple signal classification algorithm, and estimate the effective signal power gain of the target, radar, and scatterer cluster based on a least squares algorithm, as input parameters for the optimization problem.

[0045] Step 3: Construct an optimization problem for the reflection coefficient of the intelligent reflector; the objective function of the optimization problem is to maximize the signal power received by the radar from the direction of the scatterer cluster (i.e., the deception signal power), and the constraints include that the signal power received by the radar from the direction of the target is lower than a preset detection threshold (i.e., stealth constraint), and the constant magnitude constraint of the reflection coefficient of the intelligent reflector.

[0046] Step 4: Introduce Lagrange multipliers to construct the Lagrange function, transforming the non-convex optimization problem in Step 3 into a dual problem to be solved; set the partial derivative of the Lagrange function with respect to the reflection coefficient to zero, and derive the semi-closed optimal solution expression for the reflection coefficient of the intelligent reflector.

[0047] Step 5: In order to meet the requirements of extremely low latency or as a performance benchmark, the phase shift of the reflection coefficient is configured directly by relaxing the stealth constraints and using the phase alignment principle. A fast closed-form solution of the reflection coefficient is derived, which serves as the theoretical upper limit of the system's deception performance.

[0048] Step 6: Calculate the optimal intelligent reflector reflection coefficient using the above algorithm, and configure the phase shift and amplitude of each reflector element of the intelligent reflector accordingly, so as to generate a false target deception signal at the radar and suppress the real target echo signal at the same time.

[0049] In this embodiment, step one is described as follows: This embodiment considers an IRS-assisted electromagnetic deception system installed on a moving target (e.g., an aircraft) to evade detection and deceive. A distributed radar system, where the IRS is overlaid on the target surface, deceives radar detection by significantly reducing the radar cross-section (RCS) of the real target while simultaneously increasing the RCS of the surrounding scattering object clusters (acting as decoy targets). In the considered scenario, the number of scattering object clusters is denoted as . Without loss of generality, assume that the target-mounted IRS is a uniform planar array composed of... It consists of passive reflective units, among which and They represent along shaft and The number of IRS elements along the axial direction. For ease of description, the set of scatterer clusters and the set of IRS reflective elements are denoted as follows: and Furthermore, assuming the radar is a monostatic radar (i.e., the transmitter and receiver are co-located) and equipped with a uniform planar array, the total number of its transmitting and receiving antennas is... ,in and They represent along shaft and The number of transceiver antennas along the axial direction. The target is equipped with an intelligent controller capable of dynamically adjusting the reflection amplitude and / or phase of the IRS in real time, and coordinating the switching between two operating modes: a sensing mode for environmental reconnaissance and a reflection mode for electromagnetic deception. Furthermore, to enable the target to operate in the environmental reconnaissance sensing mode, an L-shaped sensing array is embedded at the target end, consisting of… It consists of a receiving sensing device, in which and They represent along shaft and Number of sensing devices along the axial direction.

[0050] In this embodiment, step two is described as follows: Because the IRS / target is in motion, and Representing time respectively The equivalent line-of-sight (LoS) channels for the IRS→radar link and the target→radar link are described. Given that aerial targets typically fly at high altitudes, the relevant LoS propagation channels can be characterized using a plane wave far-field model. For ease of subsequent derivation, a one-dimensional steering vector function is first defined for the Uniform Linear Array (ULA) as follows.

[0051] (1) in, The imaginary unit, Indicates the signal wavelength. This indicates the spacing between two adjacent antennas / array elements / sensors. This represents the constant phase difference between signals at two adjacent antennas / array elements / sensors. This indicates the number of antennas / elements / sensors in the uniform linear array. Based on this, let... and Let these represent the common array response vectors of the IRS and the radar, respectively. For any incident / exit angle (AoA / AoD) pair, including elevation and azimuth angles. In the Uniform Planar Array (UPA) model, each array response vector can be expressed as along... Axis (horizontal) and The Kronecker product of the two steering vector functions along the axial (vertical) direction. Specifically, the array response vectors at the IRS and radar are expressed as follows: (2) (3) Among them, the arrival angle / departure angle in the form of pitch / azimuth angle is... For the input variables to be specified.

[0052] For the target equipped with the IRS and radar, the LoS channel is made and They represent the times at time 1 and 2 respectively. Below are the arrival / departure angle pairs in elevation / azimuth form for the IRS and radar ends. For simplified notation, let... Represents a node From node The array response vector when incident in the direction, where , Therefore, under far-field conditions, the LoS channel from the IRS to the radar is modeled as the outer product of the array response vectors at both ends, i.e.: (4) in, For the corresponding complex path gain, Indicates time Propagation distance between the target and the radar (IRS) Let be the path gain at a reference distance of 1 meter. Furthermore, the far-field Loss channel from the target to the radar can be expressed as: (5) When using the same reference point, the IRS and the target share the same elevation / azimuth angle pairs for the radar signal, representing the angle of arrival / departure angles.

[0053] On the other hand, considering the existence between the IRS / target and the radar A cluster of scattering bodies (such as clouds, flocks of birds, and buildings) makes... and Representing time respectively The equivalent non-line-of-sight (non-LoS) channel between the IRS→radar link and the target→radar link; furthermore, let Indicates coming from the surroundings The echo channel of the scatterer cluster is returned to the radar. For the... Non-LoS channels associated with a cluster of scatterers, let and These represent the arrival / departure angle pairs in the form of elevation / azimuth angles at the IRS and radar ends, respectively. Based on the geometric channel model, the non-LoS channel from the IRS to the radar can be represented as: (6) in, Indicates the first The isotropic complex radar cross section of a cluster of scattering bodies, and This represents the corresponding complex path gain (two-way path loss), where and They represent from the first The propagation distance from a cluster of scatterers to the IRS / target and to the radar. Similarly, the non-LoS channel from the target to the radar can be represented as: (7) In addition, from the surrounding The echo channel from a cluster of scatterers back to the radar can be represented as: (8) in, Let be the complex path gain corresponding to this echo channel, where and .

[0054] Based on the above channel model, it is easy to verify that each link satisfies channel reciprocity in both forward and reverse transmission. On the other hand, let Let represent the equivalent (adjustable) reflection coefficient vector of the target-mounted IRS, where the maximum reflection amplitude of each IRS element is set to 1. Therefore, for any have Furthermore, let This represents the isotropic complex radar cross section of the target.

[0055] During radar detection, it is assumed that each single-station radar transmits a set of signals from... A coherent pulse sequence consisting of discontinuous radar pulses is used to detect moving targets, where the pulse repetition interval is constant, denoted as . The time interval during which the aforementioned pulse signal is reflected by the target and received by the radar is called the Coherent-Processing Interval (CPI), denoted as . And there are The duration of each radar pulse is... and satisfy Furthermore, it is assumed that within each CPI, the geometric positions of the IRS / target, scatterer cluster, and radar remain approximately constant, thus making changes to channel parameters and geometrically related parameters (e.g., propagation range and angle of arrival / departure in elevation / azimuth forms) negligible; however, these parameters may change between different CPIs. For ease of subsequent explanation, the following analysis focuses only on a single CPI, and time indices are omitted without ambiguity. .

[0056] make This represents the radar pulse waveform vector. Based on the above model, the radar detection signal from the IRS / target / scatterer cluster, after reflection, is at time [time missing]. The echo signal received by the radar can be represented as: (9) in, Indicates by The radar cross-sectional area stacked vector composed of a cluster of scatterers. The noise at a single-station radar is zero-mean additive white Gaussian noise with a noise variance of... In the received signal model shown in Equation (9), the first term represents the signal reflected by the IRS, the second term represents the effective echo signal from the target body (excluding the IRS), and the third term represents the echo signal from the scattering cluster. It should be noted that the first and second terms in Equation (9) only exist when the IRS / target is within the radar detection range; while the third term can be regarded as the background echo signal that always exists between the radar and the scattering cluster, regardless of whether the target exists.

[0057] It is important to note that radar can only detect targets if it receives sufficiently high reflected power from the target direction. During radar detection, performance metrics such as target presence detection and angle-of-arrival (AOA) estimation largely depend on the received signal power. The stronger the reflected signal from the target direction, the more likely the radar is to improve its detection probability and / or obtain a more accurate AOA estimate; conversely, targets aim to minimize or even eliminate reflected signal power in that direction to achieve concealment. Based on this, targets can further implement electromagnetic deception by redirecting reflected signals to the direction of surrounding scattering clusters, thereby generating decoy targets and providing misleading AOA information to the radar. Against this backdrop, this paper uses the signal power received by the radar from the IRS / target direction and the scattering cluster direction as two performance metrics to evaluate the effectiveness of IRS-assisted electromagnetic deception for radar detection.

[0058] Since a background echo signal always exists between the radar and the scattering cluster (regardless of the presence of a target), this background echo can be eliminated by the radar before target detection. After eliminating the background echo and ignoring the noise term in equation (9), the remaining received signal at each radar can be decomposed into two parts, namely the received signals from the IRS / target direction and the scattering cluster direction, respectively, and their expressions are as follows: (10) (11) For simplicity, we let: (12) Indicates from radar or scatterer clusters The effective incident signal reaching the IRS / target from the direction of transmission has a corresponding signal power denoted as . Furthermore, let (13) Indicates from node After incident and reflected by the IRS, it points to the node. The response vector of the cascaded array, where ,and and All indicate RTherefore, substituting equations (4)–(7), (12), and (13) into equation (10), and through certain mathematical derivation, the received signal from the IRS / target direction can be simplified as follows: (14) Similarly, substituting equations (4)–(7), (12), and (13) into equation (11), and through the corresponding mathematical derivation, we can obtain the equations from... The total received signal from the direction of the scatterer cluster is simplified as follows: (15) in, Indicates that the radar starts from the first Signal received from the direction of a cluster of scatterers.

[0059] According to equation (14), the signal power received by the radar from the IRS / target direction can be expressed as: (16) Similarly, according to equation (15), the radar from the first The signal power received by a cluster of scatterers in the direction of the scatterer cluster can be expressed as: (17) And the radar from The sum of the signal power received from the direction of the scatterer cluster can be expressed as: (18) To achieve radar stealth and deception, and accordingly design IRS reflection, the target needs to acquire the angle of arrival and / or signal power gain information from the radar / scatterer cluster incident on the IRS / target. Recall that, to enable the target to possess an environmental reconnaissance awareness mode, an L-shaped array is embedded on the target, which consists of... It consists of several receiving and sensing devices. Specifically, this paper uses the subscript "L" to denote the L-shaped sensing array, and lets... This represents its array response vector, which can be decomposed into along... shaft and The two one-dimensional steering vectors along the axis are shown below: (19) (20) in, For any It is established. It should be noted that, because the L-shaped sensing array is embedded on the target, it shares the same angle of arrival pair with the IRS for the same signal source, i.e. Both have approximately the same path gain. To simplify the notation, let... Indicates L-shaped sensing array from node The array response vector when receiving a directional signal, where For any This is established. Therefore, the LoS and non-LoS channels between the L-shaped sensing array and the radar are denoted as follows: and This can be modeled similarly to equations (4) and (6), by using the methods described in equation (6). Replace with (in , ), which is represented as the outer product of the response vectors of the two-end array.

[0060] Based on the above modeling, the signal received by the L-shaped sensing array can be expressed as: (twenty one) in, This represents the additive white Gaussian noise vector at the L-shaped sensing array. Noise power; This represents the array response matrix at the LSA. Indicates from radar or scatterer clusters The effective incident signal that reaches the target location.

[0061] Based on equation (21), existing algorithms (such as multi-signal classification algorithms) can be used to determine the number of signal sources (including radar and scatterer clusters) and estimate their corresponding angle-of-arrival pairs, i.e. as well as In addition to the angle of arrival information, the target also needs to estimate the equivalent signal power gain from each signal source, i.e. Specifically, based on equation (21), and using the estimated AoA, the array response matrix is ​​constructed. , The least squares estimate can be expressed as: (twenty two) in, , Let represent the equivalent noise vector used for least squares estimation. Based on this, the signal power gain estimate from the radar / scatterer cluster can be obtained as follows: Finally, using the estimated angle of arrival information and signal power gain, the reflection coefficient of the IRS can be designed according to the methods in steps 3 to 5, thereby achieving the electromagnetic deception effect on the radar.

[0062] In this embodiment, step three is as follows: To achieve shielding of the target body, it is first necessary to reduce the visibility of the real target by reducing its equivalent radar cross section (using IRS). This is equivalent to reducing the signal power received by the radar from the target direction, i.e., in equation (20). Building upon the achievement of target stealth, it is further desirable to simultaneously enhance the signal power received by the radar from the direction of the scatterer cluster, i.e., in equation (22). To achieve electromagnetic deception, an optimized design for the reflection of the IRS is proposed: under constraint... Maximize the detection threshold below a given detection threshold. Therefore, the problem can be formulated as follows (constants / irrelevant terms are omitted for brevity). ):

[0063] in, This indicates the radar's detection threshold, and has been adjusted according to the number of radar antennas. Normalization is performed. (To achieve real-time adjustment of IRS reflection, the geometric relationship between the IRS / target and the radar and scattering object cluster can be predicted and continuously updated based on prior information about the target's flight trajectory; in addition, the optimized IRS reflection vector can be pre-calculated offline and stored in the IRS controller's database to support real-time retrieval and configuration.) It should be noted that although the IRS can be directed towards multiple scatterer clusters simultaneously (i.e., While reflecting the radar cross section (IRS) to construct multiple decoy targets and thus confuse radar detection, this method disperses the overall reflected energy of the IRS, resulting in suboptimal electromagnetic deception. In contrast, focusing and redirecting the entire reflected power of the IRS to a single cluster of scatterers to construct the strongest decoy target, thereby attracting and deceiving the radar, is generally more efficient. This design concept is similar to the "Strongest Eigenmode Beamforming (SEB)" principle used to maximize the signal-to-noise ratio in multiple-input multiple-output (MIMO) systems. Therefore, without loss of generality and for simplicity, this paper selects a single cluster of scatterers for electromagnetic deception. In practical applications, the selection of the scatterer cluster for electromagnetic deception depends on its radar cross section, path gain, and distance relationship with the target / radar. Generally, the selected scatterer cluster should not be too close to the target, otherwise it may increase the risk of exposing the real target; at the same time, it should not be too far from the target, otherwise it will increase the two-way path loss, resulting in lower reflected power in the direction of the selected scatterer cluster, thus reducing the deception effect. Let... This represents the set of candidate scatterer clusters that maintain a sufficient distance from the target, i.e. ,in The distance threshold is used. To maximize the effective reflection gain of electromagnetic deception, the selected scatterer cluster can be defined as... in The effective incident signal power reaching the IRS / target can be actually estimated by the sensing array embedded at the target end.

[0064] Based on the above analysis and given the selected scatterer cluster In this case, the problem (P1) can be restated as follows (for simplicity, constant terms and irrelevant terms are omitted):

[0065] It can be verified that although the constraints in equations (26)–(28) are all convex constraints, the objective function in equation (26) is a non-concave function, making problem (P2) a non-convex optimization problem that is difficult to solve directly. To this end, this paper proposes a solution algorithm based on the Lagrange multiplier method by utilizing the quadratic structure characteristics of the objective function, and derives the optimal (semi-)closed solution of problem (P2) in the single radar single scattering cluster scenario.

[0066] Note 1: The IRS reflection design in problem (P2) depends on the cascaded array response vector defined in equation (17). and the effective incident signal in equation (16) Related signal power It can be seen that... It is built based on angle of arrival information, and This reflects the two-way path loss experienced by the non-line-of-sight link from the radar to the IRS / target via a specific scatterer cluster. Furthermore, the angle of arrival information in the direction of a particular scatterer cluster implicitly contains the relative distance relationship between that scatterer cluster and the IRS / target. Therefore, to determine the most suitable scatterer cluster for constructing a decoy target and to design the IRS reflection mode by solving problem (P2) to achieve electromagnetic deception, the target end needs to simultaneously estimate the angle of arrival information from the radar / scatterer cluster, i.e. ,in , And the signal power received by the IRS / target from the radar / scatterer cluster , .

[0067] By designing the reflection method of the IRS, the power of the echo signal from the direction of the real target is suppressed on the one hand, and the reflected signal is redirected to the direction of the scattering cluster to achieve electromagnetic deception on the other hand. Based on the above objectives, problem (P2) can be simplified to the following form (constant terms and irrelevant terms are omitted for brevity):

[0068] in, This represents the normalized detection threshold. Further analysis reveals that the objective function in equation (29) and the constraints in equation (30) share a common cross term, namely... This corresponds to the array response gain of the radar-IRS / target-scatterer cluster cascade link. It can be inferred that at the detection threshold... Taking the smaller value, in order to satisfy the constraint condition of equation (30), The value should be small and not exceed In this case, common intersection items The contribution to the objective function of equation (29) will be very limited; otherwise, the constraint of equation (30) will be violated, resulting in the real target being detected by the radar.

[0069] Based on the above analysis, the problem (P3) can be further simplified to:

[0070] In this embodiment, step four is as follows: It is easy to see that although problem (P3.1) is non-convex, it belongs to a quadratic constraint quadratic programming problem. Therefore, from an engineering implementation perspective, it is usually necessary to examine its dual problem to uncover the structure of the optimal solution and obtain an interpretation with engineering significance. For ease of solution, problem (P3.1) can be rewritten as:

[0071] in , , .

[0072] Therefore, the Lagrangian function for problem (P3.2) is defined as: (38) in, , , ,in For the Lagrange multipliers corresponding to constraint (36), Let be the Lagrange multiplier vector corresponding to equation (37), and for all have Subsequently, regarding about Taking the derivative, we get: (39) make The following semi-closed solution can be obtained: (40) Obviously, The optimal value is the Lagrange multiplier. and The function. Since problem (P3.2) is a quadratic programming problem with zero dual gap, the dual variables can be obtained through its dual problem. and Therefore, substituting equation (40) into equation (38), we can obtain its dual function as: (41) Subsequently, using the Schur complement, the dual problem can be equivalently represented as a semidefinite optimization problem, namely:

[0073] This problem can be efficiently solved using standard semidefinite programming (SDP) or linear matrix inequality (LMI) optimization methods; given a certain solution accuracy... In this case, its computational complexity is order O(n). .

[0074] In this embodiment, step five is as follows: If we only focus on achieving electromagnetic deception by maximizing the objective function in equation (32) (i.e. relaxing the constraints on stealth in equation (33)), it is not difficult to prove that its optimal solution can be expressed as: (45) Substituting equation (45) into equation (32), we can directly obtain the maximum value of the objective function: (46) It should be noted that, due to the relaxation of the constraints in equation (33), the optimal objective value obtained in equation (46) serves only as an upper bound for problem (P3.1). Furthermore, in most cases, the different cascaded array response vectors at IRS... The components are independent and approximately orthogonal to each other. Therefore, when focusing only on achieving electromagnetic deception by maximizing the objective function in equation (32), the reflection design of the IRS will not significantly affect the equivalent RCS of the real target. This conclusion will be further verified and discussed in step 6 using simulation results.

[0075] In this embodiment, step six is ​​as follows: This section evaluates the performance of the proposed IRS-assisted electromagnetic deception system in radar detection countermeasures through simulation results and verifies the effectiveness of the proposed IRS reflection design algorithm in achieving electromagnetic deception. Figure 3 As shown, consider a moving target (e.g., an aircraft), a cluster of scattering objects, and radar all located in the same two-dimensional plane. Under this simulation setting, there are... Therefore, we only need to focus on the real-time changes in the angle of arrival / departure angle in the pitch angle dimension, i.e. Unless otherwise stated, the simulation assumes the target's minimum flight altitude is 100 m; the scatterer clusters are uniformly distributed at an altitude of 95 m with an adjacent spacing of 10 m; and the countermeasure radars are uniformly distributed on a horizontal plane (i.e., Figure 3 In (Axial direction), with an adjacent spacing of 20 m.

[0076] Each single-station radar is equipped with... A uniform planar array consisting of transmitting and receiving antennas; simultaneously, it is assumed that the IRS is also a uniform planar array, composed of... It consists of passive reflective units; the L-shaped sensing array embedded at the target end is composed of Composed of a series of sensing devices, the target or the first The isotropic complex radar cross section of a cluster of scattering bodies can be expressed as: (47) in, Indicates the target or the first Effective echo surface area of ​​a cluster of scatterers This indicates its equivalent phase shift, and it follows the interval... The surface area is uniformly distributed. To simplify the simulation, the effective echo surface area of ​​the target is set to be... The effective echo surface area of ​​each scatterer cluster is , .

[0077] Assuming the radar system operates in the 6 GHz ultra-high frequency band, the corresponding signal wavelength is... Furthermore, the antenna spacing at the radar and the reflector element spacing of the IRS are respectively set as follows: and The radar pulse waveform vector is represented as: (48) Wherein, the signal bandwidth is set to , In the interval The internal distribution is uniformly distributed, and In addition, the pulse repetition interval is set to The pulse duration is Unless otherwise specified, the path gain of each link at a reference distance of 1 m is set to... The transmit power of each radar is set to... The detection threshold is set to [value] in radar scenarios. .

[0078] Figure 3 This is a schematic diagram illustrating the two-dimensional geometric positional relationship between the target, the environmental scattering body cluster, and the radar, provided in an embodiment of this application. For example... Figure 3 As shown, consider a moving target (e.g., an aircraft), a cluster of scattering objects, and radar all located in the same two-dimensional plane. Under this simulation setting, there are... Therefore, we only need to focus on the real-time changes in the angle of arrival / departure angle in the pitch angle dimension, i.e. Unless otherwise stated, the simulation assumes the target's minimum flight altitude is 100 m; the scatterer clusters are uniformly distributed at an altitude of 95 m with an adjacent spacing of 10 m; and the countermeasure radars are uniformly distributed on a horizontal plane (i.e., Figure 3 In (Axial direction), with an adjacent spacing of 20 m.

[0079] Figure 4 This is a simulation result diagram showing the variation of radar received signal power with cluster horizontal position according to an embodiment of this application. Wherein, Figure 4 Figure (a) shows the signal power received by the radar from the direction of the target. Figure 4 Figure (b) shows the signal power received by the radar from the cluster direction. For example... Figure 4 As shown, the impact of changes in the horizontal position of the scatterer cluster on the radar received signal power under different electromagnetic deception schemes is evaluated. Figure 4 As can be seen in (a), under the electromagnetic deception scheme based on Lagrange multipliers, the received signal power from the target direction is significantly suppressed to the radar detection threshold (i.e., The received signal power is below 0.5%, and hardly changes with the horizontal position of the scatterer cluster, indicating that this scheme can achieve electromagnetic deception while ensuring the target's stealth performance, effectively preventing the target from being detected by counter-radar. In contrast, the phase-aligned electromagnetic deception scheme still has a higher received signal power in the target direction, especially when the scatterer cluster is close to the target. This is because this scheme only focuses on maximizing the received power in the direction of the scatterer cluster, without considering the target's stealth constraints. Therefore, it can be regarded as an upper bound of the IRS-assisted electromagnetic deception performance (e.g., ...). Figure 4 (as shown in (b)). On the other hand, by Figure 4 As shown in (b), when the horizontal distance between the scatterer cluster and the target exceeds 10 m, both the proposed electromagnetic deception scheme based on Lagrange multipliers and the scheme based on phase alignment experience a significant reduction in received signal power in the direction of the scatterer cluster, even approaching the level of schemes without IRS or random phase baselines. This is mainly due to the fact that... and Increase, two-way path loss (i.e., with) This is due to the significant increase in power (proportional to the target). Furthermore, in the proposed scheme based on Lagrange multipliers, the received signal power is also low when the horizontal distance between the scatterer cluster and the target is less than 1 m. This is because when the scatterer cluster is extremely close to the target, it becomes more difficult to design an IRS reflection that simultaneously enhances the scatterer cluster's directional signal and suppresses the target's directional signal.

[0080] Figure 5 This is a simulation result diagram showing the change of radar received signal power with radar detection threshold provided in the embodiments of this application. Among them, Figure 5 Image (a) shows the signal power received by the radar from the direction of the target. Figure 5 Figure (b) shows the signal power received by the radar from the cluster direction. For example... Figure 5 (a) and Figure 5 As shown in Figure (b), the signal power received by the radar from the target direction and the scatterer cluster direction are given as a function of the radar detection threshold. The relationship of change. Several conclusions can be drawn: First, as the detection threshold... With the increase of , under the proposed electromagnetic deception scheme based on Lagrange multipliers, the received signal power from both the target direction and the scatterer cluster direction shows a trend of first increasing and then stabilizing. This is due to the larger . This provides greater design freedom for the IRS, enabling more effective enhancement of the received signal power in the direction of the scatterer cluster, and also demonstrates that this scheme can achieve a flexible trade-off between target stealth performance and electromagnetic deception capability. Secondly, when At that time, the received signal power of the electromagnetic deception scheme based on Lagrange multipliers in the direction of the target and the direction of the scattering cluster is basically the same as that of the scheme based on phase alignment. This is because when When the value is sufficiently large, constraint (33) no longer applies. Furthermore, the received signal power obtained in the target direction by both proposed schemes does not exceed the level of the system without an IRS baseline, indicating that even though the phase-aligned scheme sacrifices stealth performance to some extent to improve the echo in the scatterer cluster direction, it does not significantly change the target's equivalent RCS, nor does it significantly increase the probability of the target being detected. Finally, although the random phase baseline scheme can improve the received signal power in the scatterer cluster direction to some extent, it also significantly increases the received signal power in the target direction. This indicates that if the IRS reflection design is inadequate, it will not only be difficult to achieve electromagnetic deception, but may also increase the risk of the target being detected by radar.

[0081] Figure 6 This is a simulation result diagram showing the variation of radar received signal power with angle of arrival direction provided in the embodiments of this application. For example... Figure 6As shown, the radar received power distribution for various electromagnetic deception schemes under different angles of arrival is presented, where the angles of arrival of the target and the scattering body cluster are respectively set as... and It can be observed that in the proposed phase-aligned electromagnetic deception scheme and the two baseline systems, the countermeasure radar can simultaneously detect both the target and the scattering cluster. This is because none of the three schemes introduce a stealth mechanism to suppress the received signal power in the target direction. In contrast, in the proposed Lagrange multiplier-based electromagnetic deception scheme, the received signal power from the scattering cluster direction is significantly enhanced to achieve deception, while the signal power from the target direction is effectively suppressed to below a preset radar detection threshold. This indicates that the proposed IRS-assisted electromagnetic deception system can effectively reduce the probability of the target being detected by the radar by redirecting the target's reflected signal to the scattering cluster direction, thereby causing the countermeasure radar to mistakenly identify the clutter (scattering cluster) direction as the target direction.

[0082] Figure 7 This is a simulation result diagram showing the impact of imperfect AoA information at the target location on the radar's received signal power from the cluster direction, as provided in the embodiments of this application. Figure 7 As shown, the impact of imperfect angle of arrival (AHA) information in the L-shaped sensing array at the target end on the received signal power in the scatterer cluster direction under different IRS-assisted electromagnetic deception schemes was evaluated. It can be observed that as the AHA AHA estimation error increases, the received signal power from the scatterer cluster direction decreases in both the proposed Lagrange multiplier-based and phase-alignment-based electromagnetic deception schemes. This performance degradation is mainly due to insufficient signal cancellation in the radar direction and inaccurate signal redirection in the scatterer cluster direction. Furthermore, when the AHA AHA estimation error exceeds... At this point, the proposed scheme no longer outperforms baseline deception schemes without IRS or with random phases, leading to electromagnetic deception failure. This indicates that although the proposed scheme is robust to some extent, accurate estimation of the angle of arrival between the radar direction and the scatterer cluster direction remains a key factor in achieving effective reflection design in IRS-assisted electromagnetic deception systems.

[0083] In summary, this application proposes an electromagnetic stealth and deception method and system for single-station radar detection scenarios, featuring a target equipped with an intelligent reflector. By establishing a geometric channel and echo model of the target-IRS-radar and scattering object cluster, and optimizing the IRS reflection coefficient while satisfying radar detection threshold constraints, the method effectively suppresses echoes from the real target direction and redirects reflected energy to the selected scattering object cluster direction to enhance the decoy echo, thereby misleading the radar decision. For the single-radar, single-scattering object cluster scenario, a semi-closed optimal solution based on the Lagrange multiplier method is presented, and simulations verify its effective trade-off between stealth performance and deception capability. The impact of factors such as detection threshold, scattering object cluster position, and angle estimation error on system performance is also analyzed. Simulation results show that this application can significantly reduce the probability of target detection by radar and improve decoy deception effect without significantly increasing system complexity and power consumption, demonstrating good engineering practical value and promising application prospects.

[0084] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0085] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0087] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0088] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0089] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0090] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0091] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0092] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0093] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0096] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0097] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0099] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A detection environment perception and electromagnetic deception method based on an intelligent reflective surface, characterized in that, The method, applicable to moving targets equipped with intelligent reflective surface (IRS) and L-shaped sensor array (LSA) in scenarios where they coexist with single-base station radar and environmental scattering aggregates, includes the following steps: Step 1: Establish a system model, which includes a moving target equipped with IRS and LSA, a single-base station radar, and selected decoys. A cluster of environmental scatterers, among which ; Step 2: Utilize the LSA to receive radar detection signals and scatterer cluster echo signals, and estimate channel parameters based on the received signals. The channel parameters include at least the angle of arrival (AoA) of the radar and each scatterer cluster relative to the target, and the effective signal power gain between the target and the radar and each scatterer cluster. Step 3: Based on the channel parameters estimated in Step 2, construct an optimization problem for the IRS reflection coefficient; the objective function of the optimization problem is to maximize the deception signal power received by the radar from the direction of a selected single scatterer cluster, and the constraints include that the power of the real target echo signal received by the radar from the target direction is lower than a preset detection threshold, and the magnitude of the reflection coefficient of each reflection unit of the IRS is not greater than 1. Step 4: Solve the optimization problem to obtain the optimal IRS reflection coefficient; Step 5: Based on the optimal IRS reflection coefficient obtained in Step 4, configure the phase and / or amplitude of each reflection unit of the IRS to form an enhanced decoy echo signal pointing towards the selected scatterer cluster at the radar, while simultaneously suppressing the echo signal in the direction of the real target.

2. The method according to claim 1, characterized in that, The estimation of channel parameters in step 2 specifically includes: Step 2.1: Based on the signals received by the LSA, the multi-signal classification (MUSIC) algorithm is used to estimate the angle of arrival (AoA) of the radar and each scatterer cluster relative to the target; Step 2.2: Based on the signal received by the LSA and the estimated angle of arrival, construct the array response matrix, and use the least squares algorithm to estimate the equivalent incident signal arriving at the target from the radar and each scatterer cluster, thereby obtaining the corresponding effective signal power gain.

3. The method according to claim 1, characterized in that, Step 3, which involves constructing an optimization problem for the IRS reflection coefficient, specifically includes: Step 3.1: Based on the radar direction AoA and signal power gain estimated in Step 2, construct the expression for the signal power received by the radar from the target direction. ; Step 3.2: Based on the direction AoA and signal power gain of each scatterer cluster estimated in Step 2, construct the expression for the signal power received by the radar from each scatterer cluster direction. ; Step 3.3: From One scatterer cluster is selected as the decoy target, and an optimization problem is constructed. The objective function of the optimization problem is to maximize the signal power in the direction of the selected scatterer cluster. The constraints are ,as well as ,in The preset radar detection threshold, For the first The reflection coefficient of each IRS unit, This is the set of IRS reflection unit indices.

4. The method according to claim 1, characterized in that, Step 4, solving the optimization problem, employs a solution algorithm based on the Lagrange multiplier method, including: Step 4.1: Introduce the Lagrange multipliers corresponding to the stealth constraint and the Lagrange multiplier vector corresponding to the IRS reflection coefficient magnitude constraint to construct the Lagrange function of the optimization problem; Step 4.2: Set the partial derivative of the Lagrange function with respect to the IRS reflection coefficient vector to zero, and derive the semi-closed optimal solution expression for the IRS reflection coefficient, which is a function of the Lagrange multipliers; Step 4.3: Obtain the optimal Lagrange multipliers by solving the dual problem of the optimization problem; Step 4.4: Substitute the optimal Lagrange multiplier obtained in Step 4.3 into the semi-closed solution in Step 4.2 to obtain the final optimal IRS reflection coefficient.

5. The method according to claim 4, characterized in that, The solution to the dual problem in step 4.3 further includes: using Schur complement to transform the dual problem into a standard semidefinite programming problem (SDP) or a linear matrix inequality (LMI) problem for solution.

6. The method according to claim 1, characterized in that, Step 4, solving the optimization problem, also includes a fast solution algorithm based on the phase alignment method, used to meet extremely low latency requirements or as a system performance benchmark, specifically including: Step 4A: Relax the hidden constraints in the optimization problem; Step 4B: Based on the phase alignment principle, the reflection coefficient vector of the IRS is directly set to be conjugate aligned with the response vector of the cascaded array from the target to the selected scatterer cluster, thereby obtaining a fast closed-form solution for the reflection coefficient. The objective function value corresponding to this solution is used as the theoretical upper bound of the system's deception performance.

7. The method according to claim 3, characterized in that, Step 3.3, selecting the scattering cluster for the decoy, further includes: selecting scattering clusters from targets at a distance greater than a preset threshold. Candidate scatterer cluster set In the process, the effective incident signal power reaching the target is selected. The largest cluster of scatterers is used as a decoy target.

8. The method according to claim 1, characterized in that, The system model in step 1 is further defined as follows: The IRS is a uniform planar array UPA covered on the target surface, containing One passive reflective unit; The LSA is an L-shaped array embedded in the target, consisting of... It consists of a series of receiving and sensing devices; The radar is a monostation radar equipped with a uniform planar array, and its total number of transceiver antennas is [number missing]. ; The target is equipped with an intelligent controller for dynamically adjusting the IRS reflection coefficient and coordinating the LSA sensing mode with the IRS reflection mode.

9. The method according to claim 1, characterized in that, The channel parameters estimated in step 2 also include the departure angle AoD of the radar and each scatterer cluster relative to the target.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 9.

Images