A position optimization method for a co-source multipath autonomous vehicle

Abstract

This invention discloses a position optimization method for a common-source multipath autonomous vehicle, comprising the following steps: constructing a covert communication model, including a launching unmanned surface vessel (USV), a receiving USV, an autonomous vehicle, and a monitoring USV; establishing a covert communication rate optimization algorithm model, including maximizing the covert communication rate as the core objective, and constraining the monitoring USV to meet the minimum false alarm probability and minimum missed detection probability below a preset threshold, establishing the information received by the receiving USV and the channel capacity from the autonomous vehicle to the receiving USV; deriving the covert communication rate expression using Shannon's second law; and obtaining the optimal position by solving the covert communication rate expression. This invention significantly enhances the covertness and rate of communication.

Description

Technical Field

[0001] This invention relates to the field of autonomous vehicle communication technology, and specifically to a position optimization method for a common-source multipath autonomous vehicle. Background Technology

[0002] In the field of underwater communication, autonomous vehicle covert communication technology is emerging, providing strong guarantees for the covert and secure transmission of information. This technology not only supports covert communication between specific parties but also enables information transmission via autonomous vehicles acting as nodes. In point-to-point covert communication mode, the sender and receiver employ advanced encryption techniques and unique signal modulation strategies to ensure that information is difficult to detect and analyze in the underwater channel. For example, when one party needs to transmit sensitive information to another, the autonomous vehicle covert communication technology will encrypt the information with high strength and deliver it directly to the receiver via an underwater link, thereby achieving secure point-to-point communication.

[0003] When autonomous vehicles (AVs) participate in communication as nodes, the process becomes more complex. The sender first transmits information to the AV, which then uses advanced signal processing and encryption technologies to deeply process the information before forwarding it. In scenarios requiring encrypted information transmission, the information is first transmitted to the AV via an underwater link, where it undergoes encryption, modulation, and other processing before being accurately forwarded to the underwater receiving station, thus establishing a secure and reliable communication link. Multi-relay cooperative assistance involves deploying multiple AVs in the underwater environment to obtain signal thresholds at different locations, which are then used for calculation and analysis to solve problems. However, covert communication technology for AVs still faces challenges such as insufficient stealth and limited communication speed. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a position optimization method for a common-source multi-path autonomous vehicle, which solves the problems of insufficient concealment and limited communication rate that still exist in the covert communication technology of autonomous vehicles.

[0005] Technical solution: The present invention provides a position optimization method for a common-source multipath autonomous vehicle, comprising the following steps:

[0006] (1) Construct a covert communication model, including launching unmanned surface vessels, receiving unmanned surface vessels, autonomous vehicles and monitoring unmanned surface vessels;

[0007] (2) The establishment of a covert communication rate optimization algorithm model includes: taking the maximization of the covert communication rate as the core objective, and taking the monitoring of unmanned surface vessels as constrained by the minimum false alarm probability and the minimum missed detection probability being lower than the preset threshold, establishing the information received by the receiving unmanned surface vessel and the channel capacity from the autonomous vehicle to the receiving unmanned surface vessel; and deriving the covert communication rate expression by combining Shannon's second law.

[0008] (3) The optimal position is obtained by solving the covert communication rate expression, including: coordinating the set of autonomous vehicle positions; simulating and calculating the covert communication rate, false alarm probability and missed detection probability of each position; using dynamic windows to monitor performance changes in real time to accurately detect the trends of covert communication rate changes, false alarm probability and missed detection probability; performing collaborative decision and position update, integrating the evaluation results of multiple position points, and solving the optimal position through cross-validation of common source multipath signals.

[0009] Furthermore, in step (2), the expression for receiving information from the unmanned surface vessel is as follows:

[0010] ;

[0011] in, Representative receives unmanned surface vessel The first received One piece of information; To receive unmanned surface vessels The instantaneous self-interference channel coefficient, To receive unmanned surface vessels The self-interference coefficient; For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, This indicates the process from monitoring the unmanned surface vessel (USV) Eve to receiving the USV. Instantaneous channel gain; Indicates autonomous vehicle Signal transmission power, and These respectively represent receiving unmanned surface vessels. And monitor the interference signal transmission power of the unmanned surface vessel Eve. Indicates autonomous vehicle The signal sent to launch the unmanned surface vessel Alice. Indicates receipt of unmanned surface vessel Transmitted artificial noise, This indicates artificial noise emitted by the monitored unmanned surface vessel Eve; Indicates receipt of unmanned surface vessel Additive white Gaussian noise at the location, and Follows a complex Gaussian distribution ,in for The noise variance at the location.

[0012] Furthermore, in step (2), the channel capacity formula from the autonomous vehicle to the receiving unmanned surface vessel is as follows:

[0013] ;

[0014] in, Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It is the underwater noise figure.

[0015] Furthermore, in step (2), the expression for the covert communication rate is derived using Shannon's second law as follows: First, according to Shannon's second law, reliable communication cannot be achieved when the communication rate of the channel exceeds the channel capacity; that is, the following formula:

[0016] ;

[0017] in, Let P(●) represent the probability of communication interruption; P(●) represents the probability; F(●) represents the distribution function in probability theory. Indicates autonomous vehicle To receive the unmanned surface vessel Channel capacity, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It is the underwater noise figure. From autonomous vehicles To receive the unmanned surface vessel Intermittent channel gain;

[0018] Then channel capacity Substituting the expression into the above formula yields the covert communication rate from the autonomous vehicle to the receiving unmanned surface vessel. expression:

[0019] ;

[0020] in, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The signal attenuation direction vector between them It is the underwater noise figure. It is a decreasing coefficient, and , For path loss parameters, Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system The absorption coefficient during the propagation process. Represents the propagation coefficient in the medium; It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of signal attenuation between them.

[0021] Furthermore, in step (2), the formula for the covert communication rate optimization algorithm model is as follows:

[0022] ;

[0023] ;

[0024] in, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle power, This represents the minimum total false detection probability for monitoring the unmanned surface vessel Eve. It is a preset threshold. Let be a number that lies in the interval (0,1) and tends towards 0; It is a decreasing coefficient, and ; Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system Represents the propagation coefficient in the medium. This represents the absorption coefficient during the propagation process. This represents the path loss parameter. It's underwater noise.

[0025] Furthermore, in step (3), the collaborative decision and location update are performed as follows: The "AND" rule in the "hard decision" is adopted, meaning that the central node only considers the signal to exist when all local nodes independently detect and agree that the authorized user signal exists within the frequency band under test; Let the false alarm probability of each local node be... After the AND rule decision, the false alarm probability of the central node's final global decision is... for:

[0026] ;

[0027] in, It is the final false alarm probability of the central node after the judgment according to the rules. The false alarm probability for each local node. It is the number of samples participating in the judgment.

[0028] Furthermore, in step (3), the autonomous vehicle is updated. Location information, determining a certain range, i.e., autonomous vehicle The covert communication rate is set to meet the ideal node requirement of 70 kb / s or higher. The dynamic evaluation window is then reset to ensure the evaluation environment matches the current location. Next, signal threshold cross-validation is performed. For each signal threshold selected through collaborative decision-making, the iterative evaluation and collaborative decision-making process is repeated to ensure accurate and reliable evaluation results under different signal conditions. Through comparative analysis, the signal threshold and its corresponding location that maximize the covert communication rate while minimizing the false alarm probability and the missed detection probability below preset thresholds are selected. After multiple iterations and cross-validations, the optimal position of the autonomous vehicle at the equivalent center point is obtained. The formula is as follows:

[0029] ;

[0030] ;

[0031] in, The x-coordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The x-coordinate represents the location of the monitored unmanned surface vessel Eve. The ordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The coefficient representing the path loss in the underwater acoustic channel. The power of the autonomous vehicle Robbin, representing the equivalent center point, This represents the power of the receiving unmanned surface vessel Bob at the equivalent center point after cross-validation and collaborative decision-making. It is a decreasing coefficient, and ; The propagation factor represents the propagation of a signal. This represents the absorption coefficient during the propagation process; It is a number that lies in the interval (0,1) and tends towards 0. express The exponential parameter in the exponential distribution it follows. This represents the instantaneous channel gain between the receiving unmanned surface vessel Bob and the monitoring unmanned surface vessel Eve at the equivalent center point. This represents the channel threshold during signal transmission; This is the Lambert W function, used to handle complex equations involving exponents and logarithms.

[0032] The position optimization system for a common-source multipath autonomous vehicle according to the present invention includes:

[0033] Covert communication module: used to build a covert communication model, including launching unmanned surface vessels (USVs), receiving USVs, autonomous vehicles, and monitoring USVs;

[0034] The optimization algorithm module is used to establish a covert communication rate optimization algorithm model, including: taking maximizing the covert communication rate as the core objective, and using the monitoring of unmanned surface vessels (USVs) to meet the minimum false alarm probability and minimum missed detection probability below a preset threshold as constraints, establishing the information received by the USV and the channel capacity from the autonomous vehicle to the receiving USV; and deriving the covert communication rate expression by combining Shannon's second law.

[0035] The solution module is used to obtain the optimal position by solving the covert communication rate expression. This includes: coordinating the set of autonomous vehicle positions; simulating and calculating the covert communication rate, false alarm probability, and missed detection probability for each position; using a dynamic window to monitor performance changes in real time to accurately detect trends in covert communication rate changes, false alarm probability, and missed detection probability; performing collaborative decision-making and position updates; integrating the evaluation results of multiple position points; and solving for the optimal position through cross-validation of common-source multipath signals.

[0036] An electronic device according to the present invention includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the steps of any of the methods described herein.

[0037] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the methods described herein.

[0038] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: By carefully planning the relative position of autonomous vehicles, this invention aims to improve the rate of covert communication. During communication implementation, this invention deeply considers the security challenges posed by monitoring unmanned surface vessels and designs a series of targeted optimization schemes accordingly. Compared with traditional underwater communication methods, this invention successfully overcomes the problems of insufficient covertness, reduced communication rate due to improper relay vehicle positioning, and the limitation of monitoring targets, significantly enhancing the covertness and speed of communication, and providing a solid guarantee for the safe and efficient operation of underwater communication. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the scenario of the present invention;

[0040] Figure 2 This is a diagram of the covert communication model of the present invention;

[0041] Figure 3 This is a flowchart of the optimization algorithm of this invention;

[0042] Figure 4 This is a flowchart illustrating the derivation of the location of the covert communication cluster in this invention;

[0043] Figure 5 This is a schematic diagram illustrating the principle of cross-judgment during the derivation of the cluster location in the covert communication system of this invention. Detailed Implementation

[0044] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, this embodiment of the invention provides a position optimization method for a common-source multipath autonomous vehicle, comprising the following steps:

[0046] (1) Establish multiple underwater signal receivers and multiple autonomous vehicles to construct a covert communication model in an underwater covert communication system.

[0047] like Figure 1 and Figure 2 As shown, the covert communication model includes multiple autonomous vehicles (named...). ( The transmitting unmanned surface vessel (USV) located underwater transmitting signals to the autonomous vehicle (AVV) (named Alice), and multiple receiving USVs located underwater receiving signals through the AVV and emitting jamming signals to interfere with the detection USV (named Alice). ( (), and monitoring autonomous vehicles The system sends out interference signals to disrupt the monitoring unmanned surface vessel (named Eve) that is receiving the signal.

[0048] Alice, the launching unmanned surface vessel, represents an underwater unmanned surface vessel located below the waterline, designed to coordinate with another underwater receiving unmanned surface vessel. Communication is achieved. In the communication process, the launched unmanned surface vessel Alice first transmits its information to the autonomous vehicle. It is worth noting that the launch of the unmanned surface vessel Alice towards the autonomous vehicle... The transmitted information employed covert communication techniques based on environmental noise, the implementation of which led to communication difficulties with autonomous vehicles. The equivalent interference level is extremely low, therefore, it will not interfere with the monitoring activities of the monitor during communication.

[0049] Receive unmanned surface vessel It is an unmanned surface vessel (USV) located below the waterline and operating in full-duplex mode. In this communication system, the receiving USV... It is responsible for receiving data from autonomous vehicles. The mission is to forward the information sent by the unmanned surface vessel Alice. Simultaneously, in order to disrupt the monitoring activities of the potential monitoring unmanned surface vessel Eve, the receiving unmanned surface vessel... It also actively emits artificial noise as a jamming signal. While this measure effectively improves communication security, it also presents challenges in receiving signals from unmanned surface vessels. This also presents a certain degree of self-interference problem. To accurately describe the receiving unmanned surface vessel... In the communication system, this invention uses a Cartesian coordinate system for positioning, wherein the receiving unmanned surface vessel... Location Specifically represented as These three coordinate values ​​correspond to the receiving unmanned surface vessel. The x-coordinate and y-coordinate in a rectangular coordinate system.

[0050] Autonomous vehicle It is an autonomous underwater vehicle located below sea level, playing a crucial relay node role in the entire communication system. Once the autonomous vehicle... Upon receiving a message from the launching unmanned surface vessel Alice, it will accurately forward the message to the receiving unmanned surface vessel. This ensures smooth information transmission between different nodes. Furthermore, to accurately describe the position of another underwater autonomous vehicle, Robbin, within the system, this invention uses a Cartesian coordinate system for positioning. Autonomous Vehicle Location Specifically, these three coordinate values ​​correspond to the autonomous vehicle. The x-coordinate and y-coordinate in a rectangular coordinate system.

[0051] Eve, a monitoring unmanned surface vessel (USV) operating in full-duplex mode and submerged below sea level, acts as the monitor within the communication system. Eve attempts to intercept any signals originating from autonomous vehicles. The information will be transmitted and interference signals will be emitted, specifically artificial noise, to disrupt it. Receiving information. Simultaneously, launching the unmanned surface vessel Alice will also cause some self-interference. Monitoring the position of the unmanned surface vessel Eve. Represented in polar coordinates: These three coordinate values ​​correspond to Eve's x-coordinate and y-coordinate in a Cartesian coordinate system, respectively.

[0052] A covert communication rate optimization algorithm model was established. The core objective of the constructed position optimization model for the relay underwater autonomous vehicle is to maximize the covert communication rate. Simultaneously, this model is constrained by a crucial constraint: ensuring that the total false detection probability encountered by the monitoring unmanned surface vessel—encompassing both false alarms and missed detections—remains above a preset threshold. This carefully designed constraint aims to delicately balance communication efficiency and monitoring security, striving to maximize communication effectiveness while maintaining the necessary accuracy of monitoring operations.

[0053] The derivation process of the expression for the covert communication rate is as follows: Figure 3 As shown, the details are as follows:

[0054] First, using the binary assumption method, we list the autonomous vehicle. To receive unmanned surface vessels The message expression received by Eve, the monitoring unmanned surface vessel, in both cases of whether or not information is transmitted:

[0055] ;

[0056] In the formula, This indicates the information received by Eve, the monitoring unmanned surface vessel. Indicates autonomous vehicle No unmanned surface vessel was received. The situation of sending signals, and Indicates autonomous vehicle To receive unmanned surface vessels Another scenario for sending signals. Indicates autonomous vehicle Signal transmission power, and These respectively represent receiving unmanned surface vessels. And detect the interference signal transmission power of the unmanned surface vessel Eve, i.e., the artificial noise power. Indicates autonomous vehicle The signal sent to launch the unmanned surface vessel Alice. Indicates receipt of unmanned surface vessel Transmitted artificial noise, This indicates artificial noise emitted by the unmanned surface vessel Eve. Indicates receiving unmanned surface vessels To monitor the instantaneous channel gain of the unmanned surface vessel Eve, Indicates from unmanned surface vessel The instantaneous channel gain of the unmanned surface vessel Eve is monitored. The instantaneous self-interference channel gain of Eve is denoted as... . To monitor the self-interference coefficient of the unmanned surface vessel Eve. It is the additive white Gaussian noise used to monitor the location of the unmanned surface vessel Eve, and , This represents the noise variance.

[0057] Secondly, list the instantaneous channel gain and intermittent channel gain between the communicating parties in the system.

[0058] Listing from autonomous vehicles To receive the unmanned surface vessel Monitoring the instantaneous channel gain of the unmanned surface vessel Eve , And from launching the unmanned surface vessel Alice to receiving the unmanned surface vessel. Monitoring the instantaneous channel gain of the unmanned surface vessel Eve , :

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] Since the instantaneous channel gain is continuous, the expression can be written as:

[0064] , ;

[0065] in,

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] Since the intermittent channel gain is continuous in each time interval, the expressions can be written as:

[0072] , ;

[0073] in, Indicates autonomous vehicle To receive the unmanned surface vessel Intermittent channel gain, Indicates autonomous vehicle To monitor the intermittent channel gain of the unmanned surface vessel Eve, This indicates the process from launching the unmanned surface vessel Alice to receiving it. Instantaneous channel gain, This represents the instantaneous channel gain from the launch of the unmanned surface vessel Alice to the monitoring of the unmanned surface vessel Eve; It is a decreasing coefficient, and . Indicates the distance between communicating objects. The propagation factor represents the propagation of a signal. The absorption coefficient during the propagation process. For path loss parameters, Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system This indicates the launch of the unmanned surface vessel Alice and the reception of the unmanned surface vessel. Distance in a Cartesian coordinate system Indicates autonomous vehicle The distance between Eve, the unmanned surface vessel being monitored, and Eve in a Cartesian coordinate system. This represents the distance between the launching unmanned surface vessel Alice and the monitoring unmanned surface vessel Eve in a Cartesian coordinate system. This indicates the total number of time gaps.

[0074] Next, list the false alarm probability, missed detection probability, and total false detection probability (the sum of the false alarm probability and the missed detection probability) faced by the monitoring unmanned surface vessel Eve.

[0075] False alarm probability refers to the probability of false alarms in autonomous vehicles. No unmanned surface vessel was received. In scenarios involving covert communication, inappropriate interference from artificial noise can cause monitoring results to abnormally exceed the predetermined monitoring threshold γ, thus triggering false detection alarms. Conversely, the probability of missed detections reveals a completely different situation: even autonomous vehicles... Receiving unmanned surface vessel Covert communication was conducted, but unfortunately, this communication activity was missed during monitoring because the communication signal strength failed to reach the monitoring threshold γ, and was not detected in time. Based on the mathematical expression of the information received by the monitoring unmanned surface vessel Eve, this invention can derive the false alarm probability. With the probability of missed detection The specific expression is as follows:

[0076] ;

[0077] ;

[0078] Follows the exponential parameter The exponential distribution can be used to determine the false alarm probability. and the probability of missed detection Further transformed into:

[0079] ;

[0080] ;

[0081] The total false detection probability of monitoring the unmanned surface vessel Eve is obtained from the false alarm probability and the false negative probability. :

[0082] ;

[0083] Then, by utilizing the monotonicity of the function, we can obtain the minimum total false detection probability for monitoring the unmanned surface vessel Eve. The calculation model is as follows:

[0084] ;

[0085] in, This indicates the monitoring threshold set during the monitoring process.

[0086] Then, list the communication interruption probability constraints: ;in For the probability of communication interruption, It is a number located in the region 0 within the range (0,1).

[0087] List the unmanned surface vessel to be received Received message expression:

[0088] ;

[0089] in, Representative receives unmanned surface vessel The first received This is a message. (Received from) the unmanned surface vessel. The instantaneous self-interference channel gain is denoted as ,and To receive unmanned surface vessels The self-interference coefficient. In this embodiment, it is assumed that the receiving unmanned surface vessel... Unaffected by self-interference, i.e., set = 0. Furthermore... Defined as from autonomous vehicles To receive the unmanned surface vessel The instantaneous channel gain, and This indicates the process from monitoring the unmanned surface vessel (USV) Eve to receiving the USV. Instantaneous channel gain. Indicates autonomous vehicle Signal transmission power, and These respectively represent receiving unmanned surface vessels. And detect the interference signal transmission power of the unmanned surface vessel Eve. Indicates autonomous vehicle The signal sent to launch the unmanned surface vessel Alice. Indicates receipt of unmanned surface vessel Transmitted artificial noise, This indicates artificial noise emitted by the unmanned surface vessel Eve. Indicates receipt of unmanned surface vessel Additive white Gaussian noise at the location, and Follows a complex Gaussian distribution ,in for The noise variance at the location.

[0090] Next, list the autonomous vehicles arrive Channel capacity (With a bandwidth of 1000Hz):

[0091] ;

[0092] in, Indicates autonomous vehicle Signal transmission power, This indicates the channel capacity from the autonomous vehicle to the receiving unmanned surface vessel. It is an autonomous vehicle The direction vector of the signal attenuation direction between Bob and the receiving unmanned surface vessel. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It's underwater noise.

[0093] Finally, using Shannon's second law, which states that reliable communication cannot be achieved when the communication rate of a channel exceeds its capacity:

[0094]

[0095] in, Let P(●) represent the probability of communication interruption; P(●) represents the probability; F(●) represents the distribution function in probability theory. Indicates autonomous vehicle To receive the unmanned surface vessel Channel capacity, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It is the underwater noise figure. From autonomous vehicles To receive the unmanned surface vessel Intermittent channel gain;

[0096] Channel capacity Substituting the expression into the above formula yields the autonomous vehicle. To receive the covert communication rate of the unmanned surface vessel Bob for:

[0097] ;

[0098] in, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The signal attenuation direction vector between them It is the underwater noise figure. It is a decreasing coefficient, and , For path loss parameters, Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system The absorption coefficient during the propagation process. This represents the propagation coefficient in the medium.

[0099] Construct an autonomous vehicle by combining the expression for the minimum total false detection probability. The covert communication rate optimization model is as follows:

[0100] ;

[0101] ;

[0102] in, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle power, This represents the minimum total false detection probability for monitoring the unmanned surface vessel Eve. It is a preset threshold. Let be a number that lies in the interval (0,1) and tends towards 0; It is a decreasing coefficient, and ; Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system Represents the propagation coefficient in the medium. This represents the absorption coefficient during the propagation process. This represents the path loss parameter. It's underwater noise.

[0103] For Eve, the monitoring unmanned surface vessel, the goal is to strive to... To minimize this probability. Conversely, to achieve covert communication, this embodiment aims to maximize this probability, striving to approach a perfect state of 1. Therefore, under the constraints set above, It is defined as a number in the interval (0,1) that tends toward 0, while 1-ξ is set as a preset threshold.

[0104] (3) such as Figure 4 As shown, the optimal location is obtained by solving the covert communication rate expression; as Figure 5 As shown, firstly, cross-validation and collaborative decision-making of the signals are performed. Figure 5 In the context of the autonomous vehicle location cluster, R represents the location cluster after collaborative decision-making and location updates. , , ... This represents the different locations of autonomous vehicles in the cluster.

[0105] Initiate initialization work. Based on the assumption of a single autonomous vehicle. Position optimization model, setting autonomous vehicle The initial candidate position set is determined, and the dynamic evaluation window and signal threshold set are initialized simultaneously, laying the foundation for the subsequent optimization process.

[0106] ;

[0107] ;

[0108] in, These are various autonomous vehicles The coordinates on the x-axis, These are various autonomous vehicles The y-coordinate value, s, is an index variable that ranges from 1 to... Used to identify a set The differences between them. Represents a set The total number of elements in the text. It is an autonomous vehicle The direction vector of the signal attenuation direction between the receiver and the unmanned surface vessel. For autonomous vehicles To receive the covert communication rate of the unmanned surface vessel Bob, Indicates autonomous vehicle power, It is a decreasing coefficient, and . Indicates autonomous vehicle The distance between Eve, the unmanned surface vessel being monitored, and Eve in a Cartesian coordinate system. Represents the propagation coefficient in the medium. This represents the absorption coefficient during the propagation process. This represents the path loss parameter. This represents underwater noise, where i and j represent the receiving unmanned surface vessel in the corresponding sequence. and autonomous vehicles The serial number.

[0109] Following this, the iterative evaluation phase begins. For each candidate location, two key metrics—covert communication rate and false detection probability—are simulated and calculated. During this process, a dynamic window is fully utilized to monitor performance changes in real time, accurately detecting any drift phenomena to promptly capture performance trends. Next, collaborative decision-making and location update operations are performed. By integrating the evaluation results from multiple locations, a comprehensive and detailed centralized collaborative decision is made to determine the presence of a signal, thereby identifying the optimal location and forming a candidate location set.

[0110] This approach uses the AND rule from the "hard decision" method, meaning the central node only considers a signal present when all local nodes, after independent detection, determine that a licensed user signal exists within the tested frequency band. After the AND rule decision:

[0111] ;

[0112] in, This represents the final false alarm probability of the central node after the rule-based judgment. The false alarm probability for each local node. It is the number of samples participating in the judgment.

[0113] Based on this, update the autonomous vehicle. The location information is used to determine the ideal node within a certain range (i.e., the covert communication rate of the autonomous vehicle is greater than or equal to 70kb / s); and the dynamic evaluation window is reset to ensure that the evaluation environment matches the current location status.

[0114] Next, cross-validation of signal thresholds is performed. For each signal threshold selected through collaborative decision-making, the iterative evaluation and collaborative decision-making process described above is repeated to ensure accurate and reliable evaluation results under different signal conditions.

[0115] Through comparative analysis, signal thresholds and their corresponding locations were selected that maximize the rate of covert communication while keeping the false detection probability within an acceptable range.

[0116] After rigorous screening through multiple iterations and cross-validation, the minimum total false positive probability is output as a partial ideal.

[0117] Finally, system analysis and final calculations are performed. The goal of this embodiment is to maximize the covert communication rate and minimize the total false detection probability. Substituting the calculation formula into the constraints and analyzing it in conjunction with the system, using the depth of the receiving unmanned surface vessel as a reference, i.e. And effective autonomous vehicles Both Eve and the monitoring unmanned surface vessel are underwater, assuming The further transformed expression can be obtained as follows:

[0118] ;

[0119] ;

[0120] The optimal position of the effective autonomous vehicle Robbin, calculated by combining the constraints, is expressed as follows:

[0121] ;

[0122] ;

[0123] in, The x-coordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The x-coordinate represents the location of the monitored unmanned surface vessel Eve. The ordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The coefficient representing the path loss in the underwater acoustic channel. The power of the autonomous vehicle Robbin, representing the equivalent center point, This represents the power of the receiving unmanned surface vessel Bob at the equivalent center point after cross-validation and collaborative decision-making. It is a decreasing coefficient, and ; The propagation factor represents the propagation of a signal. This represents the absorption coefficient during the propagation process; It is a number that lies in the interval (0,1) and tends towards 0. express The exponential parameter in the exponential distribution it follows. This represents the instantaneous channel gain between the receiving unmanned surface vessel Bob and the monitoring unmanned surface vessel Eve at the equivalent center point. This represents the channel threshold during signal transmission.

[0124] The technical meanings of all parameters in this invention are shown in Tables 1-4.

[0125] Table 1. Technical Meaning of Parameters 1

[0126] ;

[0127] Table 2. Technical Meaning of Parameters 2

[0128] ;

[0129] Table 3. Technical Meaning of Parameters

[0130] ;

[0131] Table 4. Technical Meaning of Parameters

[0132] .

Claims

1. A method for optimizing the position of a co-source type multipath autonomous vehicle, characterized in that, Includes the following steps: (1) Construct a covert communication model, including launching unmanned surface vessels, receiving unmanned surface vessels, autonomous vehicles and monitoring unmanned surface vessels; (2) The establishment of a covert communication rate optimization algorithm model includes: taking the maximization of the covert communication rate as the core objective, and taking the monitoring of unmanned surface vessels as constrained by the minimum false alarm probability and the minimum missed detection probability being lower than the preset threshold, establishing the information received by the receiving unmanned surface vessel and the channel capacity from the autonomous vehicle to the receiving unmanned surface vessel; and deriving the covert communication rate expression by combining Shannon's second law. (3) The optimal position is obtained by solving the covert communication rate expression, including: coordinating the set of autonomous vehicle positions; simulating and calculating the covert communication rate, false alarm probability, and missed detection probability for each position; using dynamic windows to monitor performance changes in real time to accurately detect the trends of covert communication rate changes, false alarm probability, and missed detection probability; performing collaborative decision-making and position updates, integrating the evaluation results of multiple position points, and solving for the optimal position through cross-validation of common-source multipath signals; the specific details of performing collaborative decision-making and position updates are as follows: adopting the "AND" rule in "hard decision-making", that is, the central node only considers the signal to exist when all local nodes independently detect and believe that the authorized user signal exists in the frequency band to be tested; after the "AND" rule decision: ； in, It is the final false alarm probability of the central node after the judgment according to the rules. The false alarm probability for each local node. It is the number of samples participating in the judgment; Upgrade autonomous vehicles Location information to determine the autonomous vehicle The covert communication rate is set to meet the ideal node requirement of 70 kb / s or higher. The dynamic evaluation window is then reset to ensure the evaluation environment matches the current location. Next, signal threshold cross-validation is performed. For each signal threshold selected through collaborative decision-making, the iterative evaluation and collaborative decision-making process is repeated to ensure accurate and reliable evaluation results under different signal conditions. Through comparative analysis, the signal threshold and its corresponding location that maximize the covert communication rate while minimizing the false alarm probability and the missed detection probability below preset thresholds are selected. After multiple iterations and cross-validations, the optimal position of the autonomous vehicle at the equivalent center point is obtained. The formula is as follows: ； ； in, The x-coordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The x-coordinate represents the location of the monitored unmanned surface vessel Eve. The ordinate represents the position of the autonomous vehicle Robbin at its equivalent center point. The coefficient representing the path loss in the underwater acoustic channel. The power of the autonomous vehicle Robbin, representing the equivalent center point, This represents the power of the receiving unmanned surface vessel Bob at the equivalent center point after cross-validation and collaborative decision-making. It is a decreasing coefficient, and ; The propagation factor represents the propagation of a signal. This represents the absorption coefficient during the propagation process; It is a number that lies in the interval (0,1) and tends towards 0. express The exponential parameter in the exponential distribution it follows. This represents the instantaneous channel gain between the receiving unmanned surface vessel Bob and the monitoring unmanned surface vessel Eve at the equivalent center point. This represents the channel threshold during signal transmission; This is the Lambert W function, used to handle complex equations involving exponents and logarithms.

2. The position optimization method for a common-source multipath autonomous vehicle according to claim 1, characterized in that, In step (2), the expression for receiving information from the unmanned surface vessel is as follows: ； in, Representative receives unmanned surface vessel The first received One piece of information; To receive unmanned surface vessels The instantaneous self-interference channel coefficient, To receive unmanned surface vessels The self-interference coefficient; For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, This indicates the process from monitoring the unmanned surface vessel (USV) Eve to receiving the USV. Instantaneous channel gain; Indicates autonomous vehicle Signal transmission power, and These respectively represent receiving unmanned surface vessels. And monitor the interference signal transmission power of the unmanned surface vessel Eve. Indicates autonomous vehicle The signal sent to launch the unmanned surface vessel Alice. Indicates receipt of unmanned surface vessel Transmitted artificial noise, This indicates artificial noise emitted by the monitored unmanned surface vessel Eve; Indicates receipt of unmanned surface vessel Additive white Gaussian noise at the location, and Follows a complex Gaussian distribution ,in for The noise variance at the location.

3. The position optimization method for a common-source multipath autonomous vehicle according to claim 1, characterized in that, In step (2), the channel capacity formula from the autonomous vehicle to the receiving unmanned surface vessel is as follows: ； in, Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It is the underwater noise figure.

4. The position optimization method for a common-source multipath autonomous vehicle according to claim 1, characterized in that, In step (2), the expression for the covert communication rate is derived using Shannon's second law as follows: First, according to Shannon's second law, reliable communication cannot be achieved when the communication rate of the channel exceeds the channel capacity; that is, the following formula: ； in, Let P(●) represent the probability of communication interruption; P(●) represents the probability; F(●) represents the distribution function in probability theory. Indicates autonomous vehicle To receive the unmanned surface vessel Channel capacity, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel Instantaneous channel gain, It is the underwater noise figure. From autonomous vehicles To receive the unmanned surface vessel Intermittent channel gain; Then channel capacity Substituting the expression into the above formula yields the covert communication rate from the autonomous vehicle to the receiving unmanned surface vessel. expression: ； in, For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle Signal transmission power, It is an autonomous vehicle With receiving unmanned surface vessels The signal attenuation direction vector between them It is the underwater noise figure. It is a decreasing coefficient, and , For path loss parameters, Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system The absorption coefficient during the propagation process. Represents the propagation coefficient in the medium; It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of signal attenuation between them.

5. The position optimization method for a common-source multipath autonomous vehicle according to claim 1, characterized in that, In step (2), the formula for the covert communication rate optimization algorithm model is as follows: ； ； in, It is an autonomous vehicle With receiving unmanned surface vessels The direction vector of the signal attenuation direction between them. For autonomous vehicles To receive the unmanned surface vessel The rate of covert communication. Indicates autonomous vehicle power, This represents the minimum total false detection probability for monitoring the unmanned surface vessel Eve. It is a preset threshold. Let be a number that lies in the interval (0,1) and tends towards 0; It is a decreasing coefficient, and ; Indicates autonomous vehicle With receiving unmanned surface vessels Distance in a Cartesian coordinate system Represents the propagation coefficient in the medium. This represents the absorption coefficient during the propagation process. This represents the path loss parameter. It's underwater noise.

6. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the program to implement the steps of the method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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