An environment perception based distributed detection station optimization method

Abstract

The application discloses a distributed detection station optimization method based on environment perception, establishes an electromagnetic environment data set, establishes constraint conditions of a target area, establishes an action distance model between a single radiation source and a receiving station, establishes a station position optimization target function determined by a signal-to-noise ratio based on the action distance model, estimates a detection probability of a single radiation source signal according to signal parameters in the electromagnetic environment data set, establishes a station position optimization target function determined by an average detection probability based on the detection probability of the single radiation source signal, determines a measurement distance from any detection station to the radiation source according to coordinates of the single radiation source, and establishes a station position optimization target function determined by an effective positioning area based on the measurement distance from any detection station to the radiation source; and the distributed detection station optimization result is determined based on the established station position optimization target functions. The application establishes a complete electromagnetic environment data set support, and the designed station optimization method is more universal and adaptive.

Description

Technical Field

[0001] This application relates to the field of distributed detection technology, and in particular to a method for selecting the optimal distributed detection station based on environmental perception. Background Technology

[0002] Currently, there are four main methods for optimizing distributed detection stations: 1. Polygonal Array Method: Based on empirical parameters, Y-shaped, star-shaped, rhomboid, and triangular layouts are used to obtain relatively optimal detection and positioning capabilities. 2. Trial and Error Method: Selecting the physical quantities of interest and performing exhaustive calculations at certain step sizes until a relatively optimal array configuration is found. 3. Single-Objective Function Optimization Method: Using a single objective function as the optimization objective, an optimization model is established with maximum coverage or average positioning error as the index, and particle swarm optimization is used to achieve multiple iterative approximations of the optimal detection station layout. 4. Multi-Objective Function Optimization Method: Using multiple objective functions as the optimization objectives, with geometric dilution factor of accuracy (GDOP) and Cramer-Rao bound (CRB) as constraints, an optimization model is established with target estimation and surveillance area coverage as optimization indices, and multi-objective particle swarm optimization is used to solve for the optimal detection station layout.

[0003] Due to the complex geographical environment and significant tactical constraints of real battlefields, existing methods for optimizing distributed reconnaissance stations based on empirical parameters struggle to achieve optimal layout and deployment. Furthermore, the solution models established using existing optimization methods are prone to local convergence in practical applications, and the resulting station deployment models are more susceptible to factors such as baseline length, leading to poor detection and positioning performance. Moreover, as a significant factor, the changing electromagnetic environment impacts the positioning accuracy of distributed reconnaissance stations. Existing methods lack an optimization deployment approach using time-varying radiation source signal characteristics as an evaluation metric, thus preventing the system from achieving optimal scattering detection characteristics.

[0004] Existing distributed detection station optimization algorithms are prone to getting stuck in local convergence. On the one hand, they cannot solve the problem of sensor deployment under the constraints of complex battlefield geography and tactical conditions. On the other hand, the system cannot obtain optimal scattering characteristics in time-varying radiation source environments. Summary of the Invention

[0005] This application provides a method for optimizing distributed detection stations based on environmental perception. It takes into account the actual terrain limitations and the differences in the electromagnetic environment of time-varying radiation sources, establishes a complete electromagnetic environment dataset to support it, and the designed station optimization method is more universal and adaptable.

[0006] This application provides a method for selecting the best distributed detection station based on environmental perception, characterized by the following steps:

[0007] During the observation period, the reconnaissance network is used to measure the parameters of radiation source signals in space, and to obtain the characteristic parameters of multiple radiation source signals in the time domain, frequency domain and energy domain, so as to establish an electromagnetic environment dataset.

[0008] The system's azimuth detection angle range is evenly divided into multiple sub-intervals;

[0009] Based on the discontinuity of the target region, establish constraints for the target region;

[0010] Establish a model of the interaction distance between a single radiation source and a receiving station, and establish a station location optimization objective function determined by the signal-to-noise ratio based on the interaction distance model;

[0011] The detection probability of a single radiation source signal is estimated based on the signal parameters in the electromagnetic environment dataset, and a site optimization objective function determined by the average detection probability is established based on the detection probability of a single radiation source signal.

[0012] Based on the coordinates of a single radiation source, determine the measurement distance from any detection station to that radiation source, and establish a station location optimization objective function determined by the effective positioning area based on the measurement distance from any detection station to that radiation source.

[0013] Based on the established objective function for each station and the constraints of the established target area, the optimal result for distributed detection station deployment is determined.

[0014] Optionally, based on the discontinuity of the target region, the constraints for the target region can be established by: approximating the boundary of the target region with line segments, forming a closed region by multiple line segments, to determine the regional constraints determined by the terrain.

[0015]

[0016] Among them, a l b l c l x is the coefficient. min x max Let x be the minimum and maximum value, and y be the maximum and minimum values. min y max Let y be the minimum and maximum values, l = 1, 2, ..., J, and J be the number of line segments constituting the region.

[0017] Optionally, a range model is established between a single radiation source and a receiving station. Based on this range model, a station location optimization objective function determined by the signal-to-noise ratio is established, including:

[0018] For the i-th radiation source, the interaction distance model between it and the receiving station satisfies:

[0019]

[0020] Among them, P i t Let i be the emission gain of the i-th radiation source. Let A be the gain of the i-th radiation source in the direction of the detection receiver. r The effective area of ​​the reconnaissance antenna is given by π, K is Boltzmann's constant, T0 is the reference absolute temperature, and B is the reference absolute temperature. r To detect the receiver's linear wavelength division bandwidth, F r To detect the noise figure (S / N) of the linear portion of the receiver. i This represents the signal-to-noise ratio at the input of the receiving station during normal testing.

[0021] In specific radiation source and reconnaissance network scenarios, the signal-to-noise ratio (S / N) will be detected. i Considered as R i The relevant functions satisfy:

[0022] (S / N) i =f(R) i )=f(||Φ i -Φ R ||2)

[0023] Where, Φ i Φ R Let be the position vectors of the radiation source and the detection station, respectively, and ||·||2 denote the L2 norm;

[0024] The sum of the signal-to-noise ratios of all radiation sources is used as the optimization quantity A, satisfying:

[0025]

[0026] This yields the objective function for station optimization, which is determined by the signal-to-noise ratio:

[0027] Γ1 = max(A)

[0028] The optimization quantity A is expressed as A=χ(||Θ1-Θ R ||2,||Θ2-Θ R ||2,...,||Θ K -Θ R ||2).

[0029] Optionally, the detection probability of a single radiation source signal is estimated based on the signal parameters in the electromagnetic environment dataset, and a site optimization objective function determined by the average detection probability is established based on the detection probability of a single radiation source signal, including:

[0030] Electromagnetic environment dataset The signal parameters in the table represent the detection probability of the signal from the i-th radiation source. An estimation is performed, satisfying:

[0031]

[0032] Based on the detection probability of the i-th radiation source signal, the average detection probability of the system is estimated as follows:

[0033]

[0034] The objective function for station optimization, determined by the average detection probability, satisfies:

[0035]

[0036] Here, min(·) represents finding the minimum value.

[0037] Optionally, a range model is established between a single radiation source and a receiving station. Based on this range model, a station location optimization objective function determined by the signal-to-noise ratio is established, including:

[0038] Define the coordinates of the i-th radiation source signal as (x... i ′,y i If ′), then the measured distance from the k-th detection station to the radiation source satisfies:

[0039]

[0040] Where, n k This indicates an error source that conforms to the characteristics of Gaussian white noise;

[0041] Define the location estimate of the i-th radiation source as Then, performing a Taylor expansion around the estimated value satisfies:

[0042]

[0043] Based on the Taylor expansion near the estimated value, the least-squares solution of the measurement error matrix is ​​determined, satisfying:

[0044] X=(H T H) -1 H T dR

[0045] in, dR represents the measurement error matrix;

[0046] The positioning error covariance is determined based on the least-squares solution of the measurement error matrix, satisfying:

[0047]

[0048] Where, σ r For the equivalent distance error of the distributed detection system, σ x σy These are the standard deviations of target positioning errors in the horizontal and vertical directions, respectively.

[0049] The geometrical factor of precision (GDOP) is determined based on the covariance of the positioning error, satisfying the following:

[0050]

[0051] Where tr(·) represents the trace of the matrix;

[0052] Based on the geometric accuracy factor, the objective function for station optimization, determined by the effective positioning area, is obtained:

[0053] Γ3=maxS

[0054] Where S = {(x,y)|GDOP<1}.

[0055] Optionally, based on the established objective function for each station and the constraints of the established target area, the optimal results for distributed detection station deployment are determined, including:

[0056] Based on the established objective functions for each station location, a multi-index detection station location optimization objective function is established that satisfies:

[0057]

[0058] The constraints are:

[0059]

[0060] in, Ψ represents the regional constraints determined by the electromagnetic environment dataset, and Ψ represents the set of constructed angular regions.

[0061] Optionally, the optimal distributed detection deployment results may also be determined using the following methods:

[0062] Within the deployment area constrained by the aforementioned constraints, a cyclical search is performed for each deployment method.

[0063] Using a convex optimization method, the optimal solution set I1 that takes into account both objective functions Γ2 and Γ3 is found.

[0064] Search for the optimal solution of objective function Γ1 in the optimal solution set I1, and the corresponding deployment locations of each station. This is the optimal result for distributed detection station deployment.

[0065] This application also proposes a distributed detection station selection device, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned distributed detection station selection method based on environmental perception.

[0066] This application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method for optimizing a distributed detection station based on environmental awareness.

[0067] The site selection optimization method proposed in this application takes into account the actual terrain limitations and the differences in the electromagnetic environment of time-varying radiation sources, and establishes a complete electromagnetic environment dataset to support it. The designed site selection optimization method is more universal and adaptable.

[0068] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0069] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0070] Figure 1 This is an example of the overall process of the preferred method for a distributed detection station in an embodiment of this application. Detailed Implementation

[0071] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0072] This application provides a method for optimizing distributed detection stations based on environmental perception. The detection network in this embodiment consists of N distributed detection stations that jointly detect and process electromagnetic radiation signals. The coordinates of the k-th detection station are (x... k ,y k ), k = 1, 2, ..., N. For example Figure 1 As shown, it includes the following steps:

[0073] In step S101, during the observation time period T0, the reconnaissance network is used to measure the parameters of radiation source signals in space, and the characteristic parameters of K radiation source signals in the time domain, frequency domain, and energy domain are obtained to establish an electromagnetic environment dataset.

[0074]

[0075] in, Nt(i) represents the number of pulses of the i-th radiation source signal, and Te(i) represents the duration of the i-th radiation source signal. Let Fi be the pulse average repetition frequency of the i-th radiation source signal, F0(i) be the carrier frequency of the i-th radiation source signal, and Pg(i) be the amplitude of the i-th radiation source signal. Let be the azimuth angle of the i-th radiation source signal, where i = 1, 2, ..., K.

[0076] In step S102, the system's azimuth detection angle range [θ] is set. min ,θ max The angle is uniformly divided into T sub-intervals, and the j-th angle interval Θ is... j It can be represented as:

[0077]

[0078] Where, θ min θ is the lower limit of the angle. max The upper limit of the angle,

[0079] In step S103, constraints are established for the target region based on its discontinuities; and based on the electromagnetic environment dataset... The sum of radiation source amplitudes distributed within the j-th angular interval can be obtained:

[0080]

[0081] Where Σ denotes summation. The radiation source distribution weighting factor σ(j) within the j-th angular interval is obtained as follows:

[0082]

[0083] A larger radiation source distribution weighting factor σ(j) corresponds to a greater radiation source signal distribution density and intensity. To observe more radiation source signals, embodiments of this application construct an angular region set Ψ (Ψ∈[θ]). min ,θ max Within this set of angles, the radiation source distribution weighting factor σ(j) has a large value.

[0084] By approximating the angular region set Ψ with line segments, the regional constraints determined by the electromagnetic environment dataset can be expressed as:

[0085]

[0086] Based on the specific terrain of the target area, one or more specific regions D (usually multiple regions) can be given, within which stations can be deployed, and D∈{D} v ,v=1,2,…,M}, where D v Let M be a single discontinuous region, and M be the number of discontinuous regions. The boundary of the actual region is approximated by line segments, with multiple line segments forming a closed region. The regional constraints determined by this terrain can be expressed as:

[0087]

[0088] Among them, a l b l c l The coefficient is determined by the coordinates of the two endpoints of the line segment, x. min x max Let x be the minimum and maximum value, and y be the maximum and minimum values. min y max Let y be the minimum and maximum values, l = 1, 2, ..., J, and J be the number of line segments constituting the region.

[0089] In step S104, a model of the interaction distance between a single radiation source and a receiving station is established, and a station location optimization objective function determined by the signal-to-noise ratio is established based on the interaction distance model.

[0090] In step S105, the detection probability of a single radiation source signal is estimated based on the signal parameters in the electromagnetic environment dataset, and a site optimization objective function determined by the average detection probability is established based on the detection probability of a single radiation source signal.

[0091] In step S106, the measurement distance from any detection station to the radiation source is determined based on the coordinates of a single radiation source, and a station optimization objective function determined by the effective positioning area is established based on the measurement distance from any detection station to the radiation source.

[0092] In step S107, based on the established objective function for each station and the established constraints for the target area, the optimal result for distributed detection station deployment is determined.

[0093] The site selection optimization method proposed in this application takes into account the actual terrain limitations and the differences in the electromagnetic environment of time-varying radiation sources, and establishes a complete electromagnetic environment dataset to support it. The designed site selection optimization method is more universal and adaptable.

[0094] The deployment of distributed detection stations requires comprehensive consideration of system design parameters, including detection signal-to-noise ratio, average detection probability, and effective positioning area in this embodiment. In some embodiments, a range model between a single radiation source and a receiving station is established, and a station location optimization objective function determined by the signal-to-noise ratio is established based on the range model, including:

[0095] For the i-th radiation source, the interaction distance model between it and the receiving station satisfies:

[0096]

[0097] Among them, P i t Let i be the emission gain of the i-th radiation source. Let A be the gain of the i-th radiation source in the direction of the detection receiver. r The effective area of ​​the reconnaissance antenna is given by π, K is Boltzmann's constant, T0 is the reference absolute temperature, and B is the reference absolute temperature. r To detect the receiver's linear wavelength division bandwidth, F r To detect the noise figure (S / N) of the linear portion of the receiver. i This represents the signal-to-noise ratio at the input of the receiving station during normal testing.

[0098] In specific radiation source and reconnaissance network scenarios, the relevant parameters in the effective range equation are fixed, and the detection signal-to-noise ratio (S / N) is considered. i It can be regarded as being related to R i The relevant functions, namely:

[0099]

[0100] The above equation can be written as a function related to the position vector, satisfying:

[0101] (S / N) i =f(R) i )=f(||Φ i -Φ R ||2)

[0102] Where, Φ i Φ R Let be the position vectors of the radiation source and the detection station, respectively, and ||·||2 denote the L2 norm;

[0103] The sum of the signal-to-noise ratios of all radiation sources is used as the optimization quantity A, satisfying:

[0104]

[0105] The above formula can be expressed as a function related to the difference of position vectors:

[0106] A=χ(||Θ1-Θ R ||2,||Θ2-Θ R ||2,...,||Θ K -Θ R ||2)

[0107] This yields the objective function for station optimization, which is determined by the signal-to-noise ratio:

[0108] Γ1 = max(A)

[0109] Here, max(·) represents finding the maximum value.

[0110] In some embodiments, estimating the detection probability of a single radiation source signal based on signal parameters in an electromagnetic environment dataset, and establishing a site optimization objective function determined by the average detection probability based on the detection probability of a single radiation source signal includes:

[0111] Electromagnetic environment dataset The signal parameters in the table represent the detection probability of the signal from the i-th radiation source. An estimation is performed, satisfying:

[0112]

[0113] Based on the detection probability of the i-th radiation source signal, the average detection probability of the system is estimated as follows:

[0114]

[0115] The objective function for station optimization, determined by the average detection probability, satisfies:

[0116]

[0117] Here, min(·) represents finding the minimum value.

[0118] In some embodiments, establishing an interaction distance model between a single radiation source and a receiving station, and establishing a station location optimization objective function determined by the signal-to-noise ratio based on the interaction distance model, includes:

[0119] Define the coordinates of the i-th radiation source signal as (x′) i ,y′ i If the distance from the k-th detection station to the radiation source satisfies:

[0120]

[0121] Where, n k This indicates an error source that conforms to the characteristics of Gaussian white noise;

[0122] Define the location estimate of the i-th radiation source as Then, performing a Taylor expansion around the estimated value satisfies:

[0123]

[0124] Among them, symbols Let's take the partial derivative. Then we have...

[0125]

[0126] Where dx is the differential in the horizontal direction and dy is the differential in the vertical direction.

[0127] definition Then the measurement error matrix dR can be obtained:

[0128] dR=HX+N

[0129] Since N represents an error source that conforms to the characteristics of Gaussian white noise, its variance can be ignored. Based on the Taylor expansion near the estimated value, the least squares solution of the measurement error matrix is ​​determined, satisfying:

[0130] X=(H T H) -1 H T dR

[0131] Since the distance error distributions between the detection stations are identical and independent, the positioning error covariance is determined based on the least-squares solution of the measurement error matrix, satisfying:

[0132]

[0133] Where, σ r For the equivalent distance error of the distributed detection system, σ x σ y These are the standard deviations of target positioning errors in the horizontal and vertical directions, respectively.

[0134] The geometrical factor of precision (GDOP) is determined based on the covariance of the positioning error, satisfying the following:

[0135]

[0136] Where tr(·) represents the trace of the matrix. Since GDOP reflects the influence of the positional relationship between the distributed detection and positioning system's station layout and the target on the target positioning accuracy, the coordinate region where GDOP < 1 is defined as the effective positioning area S, i.e.:

[0137] S = {(x,y)|GDOP<1}

[0138] That is, the system has relatively ideal positioning accuracy within this area. Based on the geometric accuracy factor, the objective function for station optimization determined by the effective positioning area is obtained:

[0139] Γ3=maxS.

[0140] In some embodiments, the optimal distributed detection station deployment result is determined based on the established objective function for each station location and the established constraints for the target area, including:

[0141] Based on the established objective functions for each station location, a multi-index detection station location optimization objective function is established that satisfies:

[0142]

[0143] The constraints are:

[0144]

[0145] in, Ψ represents the regional constraints determined by the electromagnetic environment dataset, and Ψ represents the set of constructed angular regions.

[0146] In some embodiments, the optimal distributed detection deployment result is determined by the following method:

[0147] Within the deployment area constrained by the aforementioned constraints, a cyclical search is performed for each deployment method.

[0148] Using a convex optimization method, the optimal solution set I1 that takes into account both objective functions Γ2 and Γ3 is found.

[0149] Search for the optimal solution of objective function Γ1 in the optimal solution set I1, and the corresponding deployment locations of each station. This is the optimal result for distributed detection station deployment.

[0150] The distributed detection station optimization method of this application can monitor the surrounding electromagnetic environment in real time, and the obtained radiation source information can provide a basis for dynamic optimization of detection station deployment. The regional constraint conditions proposed in this application comprehensively consider the radiation source distribution weight factor and the actual terrain conditions, and the multi-objective constraint conditions include multiple factors such as detection signal-to-noise ratio, average detection probability, and effective positioning area, thus improving the optimality and environmental adaptability of the optimization model. The step-by-step convex optimization method of this application solves the multi-constraint optimization problem, which improves the model solution speed and has greater versatility and adaptability.

[0151] This application also proposes a distributed detection station selection device, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned distributed detection station selection method based on environmental perception.

[0152] This application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method for optimizing a distributed detection station based on environmental awareness.

[0153] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0154] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0156] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims. All of these forms are within the protection scope of this application.

Claims

1. A method for selecting the optimal distributed detection station based on environmental perception, characterized in that, Includes the following steps: During the observation period, the reconnaissance network is used to measure the parameters of radiation source signals in space, and to obtain the characteristic parameters of multiple radiation source signals in the time domain, frequency domain and energy domain, so as to establish an electromagnetic environment dataset. The system's azimuth detection angle range is evenly divided into multiple sub-intervals; Based on the discontinuity of the target region, establish constraints for the target region; Establish a model of the interaction distance between a single radiation source and a receiving station, and establish a station location optimization objective function determined by the signal-to-noise ratio based on the interaction distance model; The detection probability of a single radiation source signal is estimated based on the signal parameters in the electromagnetic environment dataset, and a site optimization objective function determined by the average detection probability is established based on the detection probability of a single radiation source signal. Based on the coordinates of a single radiation source, determine the measurement distance from any detection station to that radiation source, and establish a station location optimization objective function determined by the effective positioning area based on the measurement distance from any detection station to that radiation source. Based on the established objective function for each station and the established constraints for the target area, the optimal result for distributed detection station deployment is determined. A model of the interaction distance between a single radiation source and a receiving station is established. Based on this model, an objective function for station location optimization, determined by the signal-to-noise ratio, is established, including: For the i A radiation source, whose interaction distance with the receiving station is modeled to satisfy: in, For the first i The emission gain of each radiation source For the first i The gain of a radiation source in the direction of the detection receiver. To detect the effective area of ​​the reconnaissance antenna, Pi K Boltzmann's constant, For reference absolute temperature, To detect the linear wavelength division bandwidth of the receiver, To detect the noise figure of the linear part of the receiver, This represents the signal-to-noise ratio at the input of the receiving station during normal testing. In specific radiation source and reconnaissance network scenarios, the signal-to-noise ratio will be detected. Considered as The relevant functions satisfy: in, , These are the position vectors of the radiation source and the detection station, respectively. Represents the L2 norm; The sum of the signal-to-noise ratios of each radiation source is used as the optimization metric. A ,satisfy: This yields the objective function for station optimization, which is determined by the signal-to-noise ratio: Among them, optimization quantity A Represented as .

2. The method for selecting the best distributed detection station based on environmental perception as described in claim 1, characterized in that, Based on the discontinuity of the target region, the constraints for establishing the target region include: approximating the boundary of the target region with line segments, forming a closed region by multiple line segments, to determine the regional constraints determined by the terrain. in, , , For coefficients, , for x The minimum and maximum values, , for y The minimum and maximum values, , J This represents the number of line segments that constitute the region.

3. The method for selecting the best distributed detection station based on environmental perception as described in claim 1, characterized in that, The detection probability of a single radiation source signal is estimated based on the signal parameters in the electromagnetic environment dataset, and a site optimization objective function determined by the average detection probability is established based on the detection probability of a single radiation source signal, including: Electromagnetic environment dataset The signal parameters in the middle, for the first i Detection probability of a radiation source signal An estimation is performed, satisfying: Based on the i The detection probability of each radiation source signal is estimated by the average detection probability of the system as follows: The objective function for station optimization, determined by the average detection probability, satisfies: in, This indicates that the minimum value is being sought.

4. The method for selecting the optimal distributed detection station based on environmental perception as described in claim 3, characterized in that, A model of the interaction distance between a single radiation source and a receiving station is established. Based on this model, an objective function for station location optimization, determined by the signal-to-noise ratio, is established, including: Definition of the first i The coordinates of the radiation source signal are Then the first k The measured distance from each detection station to the radiation source satisfies: in, This indicates an error source that conforms to the characteristics of Gaussian white noise; Definition of the first i The location of each radiation source is estimated to be... Then, performing a Taylor expansion around the estimated value satisfies: Based on the Taylor expansion near the estimated value, the least-squares solution of the measurement error matrix is ​​determined, satisfying: in, , , , Represents the measurement error matrix; The positioning error covariance is determined based on the least-squares solution of the measurement error matrix, satisfying: in, The equivalent distance error of the distributed detection system. , These are the standard deviations of target positioning errors in the horizontal and vertical directions, respectively. The geometric precision factor (GDOP) is determined based on the positioning error covariance, satisfying the following: in, Represents the trace of a matrix; Based on the geometric accuracy factor, the objective function for station optimization, determined by the effective positioning area, is obtained: in, .

5. The method for selecting the best distributed detection station based on environmental perception as described in claim 4, characterized in that, Based on the established objective function for each station location and the constraints of the established target area, the optimal results for distributed detection station deployment are determined as follows: Based on the established objective functions for each station location, a multi-index detection station location optimization objective function is established that satisfies: The constraints are: in, This represents the regional constraints determined by the electromagnetic environment dataset. This represents the set of angular regions to be constructed.

6. The method for selecting the best distributed detection station based on environmental perception as described in claim 5, characterized in that, This also includes determining the optimal distributed detection deployment results using the following methods: Within the deployment area constrained by the aforementioned constraints, a cyclical search is performed for each deployment method. Using a convex optimization method, a solution that satisfies the objective function is found. and optimal solution set ; In the optimal solution set Search objective function The optimal solution, and the corresponding deployment locations of each station. This is the optimal result for distributed detection station deployment.

7. A preferred device for a distributed detection station, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, implements the steps of the preferred method for a distributed detection station based on environmental awareness as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the preferred method for a distributed detection station based on environmental awareness as described in any one of claims 1 to 6.

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