Multi-agent high-security cooperative mapping optimization system and method

Abstract

The application discloses a multi-agent high-safety cooperative mapping optimization system and method, and the system comprises a mapping center and a plurality of flight agents carrying large-scale antenna arrays; the system is configured to: utilize distance dimension information in near field communication to build a physical area in which privacy information is easily leaked around the communication link between the flight agent and the mapping center in the physical space; and minimize the physical range of the area in which privacy information is easily leaked while ensuring that preset mapping accuracy and data transmission rate requirements are met by jointly optimizing the waveform of the all-sensing integrated signal, so that covert communication is realized; and the application provides a brand-new solution for wireless safe transmission of high-value data.

Description

Technical Field

[0001] This invention relates to the field of information system security technology, specifically to a multi-agent high-security collaborative mapping optimization system and method. Background Technology

[0002] In multi-agent (e.g., quadcopter drone) collaborative mapping scenarios, swarms of flying agents need to perform high-precision collaborative mapping tasks. This requires agents to not only perform high-precision terrain scanning using wireless signal echoes, but also to simultaneously feed back measurement results and real-time location information to a mapping center. Existing technologies present the following challenges: Because agents typically maintain close formation, their communication and sensing ranges are limited to the near field. Fine-tuned waveform design of the agents' feedback signals is required to ensure no signal interference between agents within the swarm and that only the mapping center can accurately receive the data. Furthermore, the mapping data and location information fed back by the agents are highly valuable and are extremely vulnerable to potential leaks during wireless transmission, leading to information leakage. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a multi-agent high-security collaborative mapping optimization system and method, which solves the problem of how to achieve covert communication based on the existing sensor-integrated communication (ISAC) technology.

[0004] Technical Solution: The multi-agent high-security collaborative mapping system of the present invention includes: a mapping center and multiple flying agents equipped with large-scale antenna arrays; the system is configured to: utilize the distance dimension information in near-field communication to construct a physical area in physical space around the communication link between the flying agents and the mapping center, where privacy information is easily leaked; and by jointly optimizing the waveform of the integrated sensing signal, while ensuring that the preset mapping accuracy and data transmission rate requirements are met, minimize the physical range of the area where privacy information is easily leaked, thereby achieving covert communication.

[0005] Furthermore, the signals emitted by the flying intelligent agent are integrated sensing signals, which are composed of a superposition of a dedicated sensing signal used for terrain mapping and a communication signal carrying mapping data. By optimizing the beamforming characteristics of the integrated sensing signal, the system ensures that potential leakage nodes cannot effectively detect the existence of communication behavior outside areas where privacy information is easily leaked.

[0006] The multi-agent high-security collaborative mapping optimization method described in this invention is applied to a system including a mapping center and multiple flight agents, and includes the following steps:

[0007] (1) Construct a near-field collaborative mapping scenario model that includes a mapping center, a flying agent and potential leakage nodes, wherein the flying agent is equipped with a large-scale antenna array to extend the near-field interaction range;

[0008] (2) Establish a near-field communication and sensing integrated signal model and a leakage node detection model based on two-dimensional information of distance and angle, and construct a multi-dimensional performance constraint that includes concealment requirements, minimum communication rate requirements and maximum sensing error requirements;

[0009] (3) Based on the signal model and performance constraints, the optimization objective is defined as minimizing the area of ​​a physical region around the communication link that is easily leaked by privacy information, defined by a specific geometric boundary;

[0010] (4) By jointly optimizing the solution, the minimum safe geometric boundary that satisfies all performance constraints is determined, and the waveform of the integrated sensing signal is optimized under the boundary constraints to finally achieve high-safety collaborative mapping.

[0011] Furthermore, in step (2), the specific steps of constructing the leak node detection model are as follows: the exploration of communication behavior by the leak node is modeled as a binary hypothesis testing problem, that is, determining whether the flying intelligent agent is only performing perception scanning or simultaneously transmitting data; based on the leak node detection model, the concealment requirement of communication is quantified as a constraint on the received signal power at the leak node.

[0012] Furthermore, in step (2), the multidimensional performance constraints include: the core security constraint requires that the detection error probability of the leaking node approaches the maximum value to ensure that the communication behavior is difficult to detect; the core communication constraint requires that the data transmission rate from the flight agent to the mapping center is not lower than the minimum threshold required by the task; and the core perception constraint requires that the estimation error of the distance and angle of the mapping target does not exceed the maximum allowable error value.

[0013] Further, step (4) is as follows: First stage: Under the premise of satisfying the concealment constraint and the minimum communication rate constraint, the minimum security geometric boundary parameter that can define the area where the privacy information is easily leaked is determined by searching; Second stage: Under the determined security geometric boundary constraint, the problem is transformed into minimizing the perception error under the condition of satisfying all constraints, and the optimal integrated sensing signal waveform design scheme is obtained by solving through mathematical optimization method.

[0014] Furthermore, in the second stage, by introducing auxiliary variables and performing semidefinite relaxation, the original non-convex optimization problem is transformed into a convex optimization problem for solution, thereby obtaining the globally optimal waveform design result.

[0015] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: it is no longer limited to beamforming design for traditional communication security, but rather, based on the physical characteristics of NFC communication, it starts from spatial geometry and signal sources, and actively shapes and compresses the physical boundaries of information security through systematic modeling and joint optimization. In the cutting-edge application scenario of near-field multi-agent collaborative mapping, based on a series of convex optimization techniques, it achieves true integration of high-security communication and high-performance sensing, providing a brand-new solution for the secure wireless transmission of high-value data. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system of the present invention;

[0017] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0019] like Figure 1 As shown, this embodiment of the invention provides a multi-agent high-security collaborative mapping optimization system, comprising two types of agents: a mapping center and... A multi-agent collaborative mapping scenario (e.g., quadcopter drones) employs a link optimization method that balances high-precision perception with high-security communication. Each agent utilizes unique distance dimension information from Near Field Communication (NFC) to construct and minimize a "privacy information easily leaked area," thereby achieving covert communication in physical space. Simultaneously, waveform design through Sensor-Integrated Communication (ISAC) ensures that mapping accuracy and data transmission rate meet mission requirements.

[0020] like Figure 2 As shown, this invention proposes a multi-agent high-security collaborative mapping optimization method, which specifically includes the following steps:

[0021] Step 1 System Scene and Physical Modeling

[0022] This step aims to build a multi-agent system model that can simultaneously accommodate the two major requirements of "collaborative mapping" and "covert communication".

[0023] Step 1.1 Definition of Multi-Agent Collaborative Mapping Scenario

[0024] Consider a swarm of multiple aerial agents performing a collaborative mapping task. Each swarm consists of a mapping hub and multiple mapping agents. The mapping agents utilize ISAC technology to perform high-precision terrain scanning using wireless signal echoes, while simultaneously feeding back the measurement results and real-time location to the mapping hub. Due to the close formation of the agents, their communication and sensing ranges are all within the near-field range. Furthermore, to ensure that the signals of the mapping agents within the swarm do not interfere with each other, the waveforms of the feedback signals from the mapping agents need to be designed based on near-field communication technology to ensure that only the mapping hub within the swarm can receive the measurement data.

[0025] Step 1.2. Antenna and Field Model Construction

[0026] Each agent in the cluster is equipped with a massive antenna array, whose huge antenna aperture D makes the near-field region (measured by Rayleigh distance) extremely wide. definition, The signal wavelength is significantly expanded, ensuring that all interactions within the mission scenario follow the near-field channel rules.

[0027] Step 2 Near-field ISAC signal and channel modeling

[0028] Step 2.1. Near-field spherical wave channel modeling

[0029] The channel between the mapping agent and the mapping center is no longer the traditional plane wave model, but a spherical wave model that relies on two-dimensional information of distance and angle. This model is the basis for achieving accurate distance sensing and highly reliable communication.

[0030] Without loss of generality, the origin of the coordinate system is placed at the center of the uniform linear array of the mapping agent. Therefore, the first... The coordinates of each array element are represented as follows: , Antenna spacing, ,in, The number of array elements corresponding to half-beamwidth; the total number of antennas is defined. Assume the distance between the target area to be scanned and the center of the antenna array is... , angle is Its coordinates can be represented as Therefore, from the first The distance from each antenna element to the target can be calculated as follows:

[0031]

[0032] exist Within this range is the Fresnel zone, which is the near field, and each antenna element is approximately identical to the scanned target area. Therefore, the... The channel between each antenna element and the scanning area can be represented as:

[0033]

[0034] In the above formula, This represents the free space path loss of the central link. Indicates the complex channel gain. The unit is imaginary. Near-field channel vector between the base station and the user or target. It can be represented as , This represents the near-field array response vector. Its first... elements ( This can be represented as:

[0035]

[0036] use Indicates distance, Indicates angle, Let represent the complex channel gain coefficient. Then the ... The channel model from a mapping agent to the mapping center can be modeled as follows:

[0037]

[0038] In the above formula , , For the first The distance, angle, and channel gain coefficient of each mapping agent to the mapping center.

[0039] Then the first The channel model from a mapping agent to a leaking node can be modeled as follows:

[0040]

[0041] Parameter definition and similar.

[0042] Step 2.2 Integrated Sensing and Covert Communication Signal Model:

[0043] Assume there is There are 1 mapping agent, and each agent has 10 antennas. . No. ISAC signal emitted by a mapping agent It consists of two superimposed parts, one of which is a dedicated sensing signal used for topographic mapping. The other part carries the surveying data, which is handled by the first... The communication signal sent by a mapping agent to the mapping center can be represented as:

[0044]

[0045] in, Indicates the first A mapping agent transmits information symbols All-digital beamformer, This represents a dedicated sensing signal used for terrain scanning. It is assumed that the information symbols are independently distributed and have unit power, i.e. .make The covariance matrix of the dedicated sensing signal, the first... The covariance matrix of the integrated sensory signal of a mapping agent is given by the following formula:

[0046]

[0047] The covariance matrix of the entire transmitted signal This is the core variable that this invention aims to optimize.

[0048] Step 2.3 Construction of Leakage Node Detection Model

[0049] For Willie, a potential leak node, obtaining information about a particular agent's transmission signal requires detecting the existence of that transmission behavior. This presents him with a binary hypothesis testing problem: determining whether a mapping agent is only performing terrain scanning ( (Assuming), or simultaneously communicating with the surveying center ( (Assumption). Because the sensed signals are generally perceived using plaintext pulse waves, interference can usually be directly eliminated. Therefore, the binary hypothesis testing problem at Willie's location can be modeled.

[0050]

[0051] In the above formula From the first Channel parameters from the mapping agent to Willie, and noise at Willie's location. It follows a complex Gaussian distribution, where, To reveal the background noise variance at the node, For dimension unit array, Let be the number of antennas for Willie. The noise here may contain transmitted signals from other mapping agents, but since it is not the target signal and combines the characteristics of a Gaussian signal, it is still modeled as Gaussian noise. For ease of representation, the noise variance is defined. The noise at Willie can be represented as .

[0052] Step 3: Construct multi-dimensional performance constraints

[0053] This step transforms the task requirements (concealed communication, sufficient data transmission, and accurate maps) into mathematical boundary conditions that must be followed in the optimization problem.

[0054] Step 3.1. Core Security Constraints

[0055] To ensure that communication cannot be detected, the total detection error probability of the leaking node must be specified. It must be close to 1, which ultimately translates into an upper limit constraint on the received signal power at the leaking node. (Detection error probability) It is a key metric for evaluating Willie's detection performance in communications. It can be calculated as:

[0056]

[0057] in, This represents the probability of a false alarm, while This is the probability of missed detection. To achieve covert communication, it is necessary to ensure... ,in It is a sufficiently small value. According to Pinsker's inequality, the minimum detection error probability is... The lower bound is given by the following formula:

[0058]

[0059] in, and They represent In the assumption and The probability distribution below, Indicates from arrive Kullback-Leibler (KL) divergence.

[0060] Given that the mapping agent transmits information using a Gaussian codebook, Willie in The signal received at each time follows the characteristic parameter: The complex Gaussian distribution. The signal received at each time follows the characteristic parameter: The complex Gaussian distribution, in which the transmit power Leaking node usage Monitoring with multiple antennas allows us to represent the KL divergence as follows:

[0061]

[0062] In the above formula, , . For the transmit beam gain; after a series of mathematical transformations, the above equation (11) can be expressed as:

[0063]

[0064] In the above formula

[0065]

[0066] Based on the Taylor series approximation when There can be moments. The above formula can be used as an example. Approximately expressed as:

[0067]

[0068] Combined with concealment constraints Ultimately, the concealment constraint can be expressed as:

[0069]

[0070] Combining formulas After mathematical transformation, the above formula becomes It can be represented as:

[0071]

[0072] Step 3.2 Calculation of Communication Link Constraints for Mapping Agents

[0073] The communication link between the surveying center and the surveying agent must guarantee a minimum effective throughput. This ensures real-time and complete interaction of collaborative mapping data. The mapping agent transmits information to the mapping center; the information transmission rate can be expressed as:

[0074]

[0075] In the above formula, It is the first A wireless channel model from a mapping agent to a mapping center. It is the received noise power at the surveying center. The rest The sum of signals transmitted by each mapping agent, relative to the first The signals from each agent constitute the interference. Communication constraints can be represented as... This means the transmission rate is lower than the minimum required rate. Converting the original communication constraints into convex constraints can be further expressed as:

[0076]

[0077] Step 3.3 Core Perception Constraint Calculation

[0078] The accuracy of topographic mapping must meet the mission requirements, and this invention uses the maximum mapping error to characterize it. This is quantified as a constraint on the estimation error of the sensing parameters (distance and angle) using the Cramer-Lao bound (CRB). The trace of the CRB matrix must be less than or equal to a preset maximum permissible error value. ,Right now

[0079]

[0080] In the above formula, For the first Near-field round-trip channel matrix between a mapping agent and the target location being mapped. Let be the noise variance when the k-th mapping agent receives the echo. The parameter definitions are consistent with those above. To calculate CRB, we first define the... Fisher information matrix of a mapping agent for:

[0081]

[0082] The required parameters include: In the formula for The real and imaginary parts. In the above formula, It is related to distance and angle Related blocks, yes With complex channel gain (real part) and the virtual part Covariance blocks between ). It is only with The relevant blocks. The CRB matrix for distance and angle estimation, which is of interest in this invention, is obtained by removing irrelevant parameters from the total Fisher information matrix. This is obtained through the influence of Shur complement, which can be achieved through the inverse of Shur complement.

[0083] After a series of calculations, the formula can be... The components of the Fisher information matrix are represented as follows:

[0084]

[0085] in,

[0086]

[0087]

[0088]

[0089]

[0090] in,

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] in,

[0097]

[0098] Where T is the signal period of radar sensing / communication.

[0099] Ultimately, based on the formula — It can be calculated that CRB can be expressed as:

[0100]

[0101] The ISAC sensing constraint can be represented as

[0102]

[0103] Step 4: Formulating the Problem

[0104] Step 4.1 Definition and Quantification of Areas Where Privacy Information Is Easily Leaked

[0105] Although A preliminary expression of the concealment index has been given. However, considering the general characteristics of near-field communication, namely the direction and distance characteristics of signal transmission, a physical space region centered on the mapping hub and mapping agent is defined. When the leaking node enters this region, no matter how the transmitted signal is adjusted, it cannot satisfy the equation. The concealment constraint is defined in the standard. Therefore, this area is the weak point of the near-field ISAC communication link. The goal of this invention is to minimize the area or volume of this region through optimized design.

[0106] It is known that the surveying center is located at the [number missing]. A mapping agent Distance and Angle. When the beam is focused on At the time, at the location of the leak node The beamforming lobe at a certain point can be represented as:

[0107]

[0108] Among them, there are parameters ,parameter . and Is the leaking node Willie relative to the first The direction and distance of each mapping agent, the specific range of which will be given later. denoted as carrier frequency, and c as the speed of light.

[0109] Combining formulas and formula The implicit constraint can be rewritten as:

[0110]

[0111] Through a series of mathematical calculations, the boundaries of areas where privacy information is easily leaked can be characterized as follows:

[0112]

[0113] In the above formula, For the Fresnel integral function with respect to the constraint index The concealment threshold parameter after inverse kinematics. That is, defining the function. If it is a Fresnel integral function, then It can be represented as:

[0114]

[0115] To minimize the area of ​​privacy-sensitive regions, the first step can be defined based on plane geometry formulas. The area where the privacy information of individual mapping agents is easily leaked. for:

[0116]

[0117] in, and For formula The upper and lower bounds in the middle, i.e. , .

[0118] Step 4.2 Optimize the problem rewriting

[0119] Next, the smallest area where privacy information is easily leaked is determined by minimizing the angular range. After applying a sine transform, the angular range becomes [-1, 1]. The minimum angular resolution is... The arcsine can be defined as the angular range of areas where privacy information is easily leaked, where the resolution coefficient... Ultimately, the optimization problem can be expressed as:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] , .

[0126] Constraint (40) ensures that the concealment constraint is satisfied at the geometric boundary points of areas where privacy information is easily leaked; Constraint (41) ensures that the transmission power does not exceed the maximum power limit; Constraint (18) ensures that the effective communication throughput of the mapping agent is not lower than the minimum required for the task; Constraint (33) ensures that the Cramerao boundary for terrain perception does not exceed an acceptable maximum error value; Constraints (42) and (43) define the structure of the ISAC signal, i.e., the total signal. It must contain communication signals and sensing signals Through semi-definite relaxation and To express.

[0127] Step 5: Two-stage joint optimization solution

[0128] The core algorithm of this invention consists of two main sub-steps:

[0129] Step 5.1 Determination of safety geometric boundaries

[0130] The goal is to identify the geometric parameters of the minimum privacy-sensitive area that is easily leaked, which is required to meet the two core security and performance requirements of concealment and communication rate.

[0131] (1) Determine the boundary range in distance equation (38) and First, determine the mapping center relative to the first point in the angular dimension. Location of a mapping agent Inverse solution of hidden constraints To obtain a safe distance range, i.e. or Therefore, the distance range of areas prone to privacy breaches was determined as follows: .

[0132] (2) Search for the minimum angle range (solution) Area where privacy information is easily leaked It is the angular resolution coefficient. The goal of this invention is to find the minimum function that satisfies all constraints. value.

[0133] This search process needs to satisfy two key constraints simultaneously:

[0134] Boundary concealment constraints: In On the defined angular boundary, the concealment requirement must be met, i.e., constraint (40).

[0135] Minimum throughput constraint: The minimum effective throughput constraint (18) must be achieved, which is equivalent to a minimum signal-to-noise ratio requirement.

[0136] Solution method: Use a one-dimensional search algorithm (such as binary search) to iterate from small to large. Value. In each Under the given value, check if there exists a (not yet optimized) signal that can simultaneously satisfy the boundary concealment constraint and the minimum throughput constraint.

[0137] Stage output: The first output that satisfies both the boundary concealment constraint and the minimum throughput constraint. Value, denoted as This value defines the smallest, most secure geometric area capable of supporting the required communication rate.

[0138] Step 5.2 Joint optimization of ISAC waveforms under geometric constraints

[0139] The minimum safety boundary (i.e.) was determined in the first phase. After that, the goal of this stage is to solve for the specific ISAC signal covariance matrix that satisfies all performance indicators (communication, sensing, power) without violating the geometric boundaries. At this point, the geometric boundary is fixed. The original optimization problem can be adapted: instead of minimizing the region where privacy information is easily leaked, it becomes a constraint. The problem becomes minimizing the perceptual error (CRB) while satisfying all other constraints, which can be expressed as:

[0140]

[0141] st (41)(42)(18)

[0142] This problem (44) is a non-convex problem, and it is necessary to introduce auxiliary variables. The original problem Equivalent conversion to:

[0143]

[0144]

[0145] st(41)(42)(18)

[0146] The constraint (42) in the above problem is non-convex, and the present invention introduces... And discard non-convex Relaxing the constraints yields a convex linear matrix inequality constraint. .

[0147] Solving the relaxed SDP problem:

[0148] After the above transformation, the original problem becomes a standard, convex semidefinite programming (SDP) problem. This SDP problem can be solved using standard algorithms such as the interior-point method to obtain the globally optimal solution. and .

[0149] Feasibility verification and deconstruction:

[0150] Feasibility verification: Check the minimum CRB value obtained from the solution (i.e. Does it meet the set perception constraints? If this condition is met, it means that... Within a defined safe area, there exists a The signal must simultaneously satisfy the requirements of concealment, communication, and sensing. If it fails to do so, the constraints are too tight, meaning the performance requirements must be relaxed (e.g., reducing...). or improve ), or increase the transmission power .

[0151] Final output: The optimal feasible solution is obtained. and They are the first A mapping agent is used to achieve the final beamforming vector and signal covariance matrix for high-security, high-performance collaborative mapping. (The remaining text appears to be incomplete and requires further context.) Each agent runs the same optimization program, ultimately obtaining all... Beam vector shaping for a mapping agent.

[0152] The technical meanings of all parameters are shown in Tables 1-4.

[0153] Table 1. Technical Meaning of Parameters 1

[0154] ;

[0155] Table 2. Technical Meaning of Parameters 2

[0156] ;

[0157] Table 3. Technical Meaning of Parameters

[0158] ;

[0159] Table 4. Technical Meaning of Parameters

[0160] .

Claims

1. A multi-agent high-security collaborative mapping optimization system, characterized in that, include: The system comprises a mapping hub and multiple flying agents equipped with large-scale antenna arrays. The system is configured to: utilize distance dimension information from near-field communication to construct a physical region in physical space around the communication link between the flying agents and the mapping hub, where privacy information is easily leaked; and minimize the physical extent of the privacy-leaking region by jointly optimizing the waveform of the integrated sensing and sensing signal, while ensuring that preset mapping accuracy and data transmission rate requirements are met, thereby achieving covert communication. Specifically, constructing a privacy-leaking physical region involves: establishing a near-field communication and sensing integrated signal model based on two-dimensional distance and angle information, a leakage node detection model, and constructing multi-dimensional performance constraints including covertness requirements, minimum communication rate requirements, and maximum sensing error requirements; based on the signal model and performance constraints, the optimization objective is defined as minimizing the area of ​​a privacy-leaking physical region around the communication link, defined by geometric boundaries; the signal emitted by the flying agents is an integrated sensing and sensing signal, composed of a superposition of sensing signals used for terrain mapping and communication signals carrying mapping data; the system optimizes the beamforming characteristics of the integrated sensing and sensing signal so that potential leakage nodes cannot effectively detect the existence of communication behavior outside the privacy-leaking region.

2. A multi-agent high-security collaborative mapping optimization method, implemented using the system described in claim 1, characterized in that, Includes the following steps: (1) Construct a near-field collaborative mapping scenario model that includes a mapping center, a flying agent and potential leakage nodes, wherein the flying agent is equipped with a large-scale antenna array to extend the near-field interaction range; (2) Establish a near-field communication and sensing integrated signal model and a leakage node detection model based on two-dimensional information of distance and angle, and construct a multi-dimensional performance constraint that includes concealment requirements, minimum communication rate requirements and maximum sensing error requirements; (3) Based on the signal model and performance constraints, the optimization objective is defined as minimizing the area of ​​a physical region around the communication link that is easily leaked by geometric boundaries; (4) By jointly optimizing the solution, the minimum safe geometric boundary that satisfies all performance constraints is determined, and the waveform of the integrated sensing signal is optimized under the boundary constraints to finally achieve high-safety collaborative mapping.

3. The multi-agent high-security collaborative mapping optimization method according to claim 2, characterized in that, In step (2), the specific steps of constructing the leak node detection model are as follows: the exploration of the communication behavior of the leak node is modeled as a binary hypothesis testing problem, that is, to determine whether the flying intelligent agent is only performing perception scanning or simultaneously transmitting data; based on the leak node detection model, the concealment requirement of communication is quantified as a constraint on the received signal power at the leak node.

4. The multi-agent high-security collaborative mapping optimization method according to claim 2, characterized in that, In step (2), the multidimensional performance constraints include: the core security constraint requires that the detection error probability of the leaking node approaches the maximum value to ensure that the communication behavior is difficult to detect; the core communication constraint requires that the data transmission rate from the flight agent to the mapping center is not lower than the minimum threshold required by the task; and the core perception constraint requires that the estimation error of the distance and angle of the mapping target does not exceed the maximum allowable error value.

5. The multi-agent high-security collaborative mapping optimization method according to claim 2, characterized in that, Step (4) is as follows: First stage: Under the premise of satisfying the concealment constraint and the minimum communication rate constraint, the minimum security geometric boundary parameter that can define the area where privacy information is easily leaked is determined by searching; Second stage: Under the determined security geometric boundary constraint, the problem is transformed into minimizing the perception error under the condition of satisfying all constraints, and the optimal integrated sensing signal waveform design scheme is obtained by solving through mathematical optimization method.

6. The multi-agent high-security collaborative mapping optimization method according to claim 5, characterized in that, In the second stage, by introducing auxiliary variables and performing semidefinite relaxation, the original non-convex optimization problem is transformed into a convex optimization problem for solution, thereby obtaining the globally optimal waveform design result.

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