A lightweight radiation source positioning method

Abstract

The present invention relates to a lightweight radiation source positioning method, comprising the following steps: obtaining a mask map containing building distribution information within a target area; drawing a grayscale image containing the location information and received signal strength of each sampling point within the target area as an electromagnetic sampling map; fusing the mask map and the electromagnetic sampling map to obtain input image samples, and analyzing and obtaining the radiation source position coordinates; the lightweight radiation source positioning model includes: several feature extraction modules for performing multi-scale feature extraction on the input image samples based on residual connections and an attention mechanism; a feature fusion module for converting the multi-scale feature maps output by different feature extraction modules to a set size and then performing feature fusion; and a spatial mapping module for predicting the radiation source position coordinates based on the fused features generated by the feature fusion modules. The present invention can effectively and accurately obtain the radiation source position.

Description

Technical Field

[0001] The present invention relates to the technical field of radiation source positioning, and in particular to a lightweight radiation source positioning method. Background Art

[0002] Electromagnetic maps can intuitively present information such as electromagnetic energy distribution and spectrum resource distribution. Ideally, the distribution of electromagnetic signal strength can accurately map the number of radiation sources and their locations, effectively assisting in the precise location of radiation sources. The main principles and shortcomings of existing positioning technologies are as follows:

[0003] 1. Time-based (ToA and TDoA) and angle-based (AoA) methods

[0004] ToA: Positioning is performed by measuring the arrival time of the received signal between the measurement terminal and the target emitter and converting it into distance. At least three measurement terminals are required to calculate the position of the target emitter.

[0005] TDoA: Based on the distance difference between each measurement terminal and the target radiation source, the relative position of the target radiation source relative to each measurement terminal is inferred by solving a set of nonlinear hyperbolic equations, thereby performing positioning.

[0006] AoA: It mainly measures the arrival angle between the target radiation source and the measurement terminal. The rays formed with the measurement terminal as the starting point must pass through the target radiation source. The intersection of the two rays is the position of the target radiation source.

[0007] Disadvantages: High dependence on high-precision clocks and antenna arrays, and high hardware costs.

[0008] 2. Ranging-based methods

[0009] Main principle: A certain statistical signal attenuation or time delay model is required to estimate the distance between the target radiation source and the measurement terminal.

[0010] Disadvantages: The complex urban environment cannot be accurately described by a deterministic physical model.

[0011] 3. Fingerprint-based methods

[0012] Main principle: Offline measurement is required to collect RSS measurements with location tags at multiple points in the target area to build a fingerprint database. The target is located by comparing the RSS measurements with the fingerprints in the database.

[0013] Disadvantages: Building a fingerprint database requires a lot of time and labor costs.

[0014] Therefore, a radiation source positioning method is needed to solve the problems existing in the prior art. Summary of the Invention

[0015] The technical problem to be solved by the present invention is to provide a radiation source positioning method, which can effectively and accurately obtain the position of the radiation source.

[0016] The technical solution adopted by the present invention to solve the technical problem is to provide a lightweight radiation source positioning method, comprising the following steps:

[0017] Get a mask map containing building distribution information in the target area;

[0018] Drawing a grayscale image including the location information of each sampling point in the target area and the received signal strength thereof as an electromagnetic sampling map;

[0019] fusing the mask map and the electromagnetic sampling map to obtain an input image sample;

[0020] The input image sample is analyzed using a lightweight radiation source positioning model to obtain radiation source position coordinates; the lightweight radiation source positioning model includes:

[0021] A plurality of feature extraction modules, wherein the plurality of feature extraction modules are connected in sequence and used to perform multi-scale feature extraction on the input image sample based on residual connection and attention mechanism;

[0022] A feature fusion module is used to convert the multi-scale feature maps output by different feature extraction modules into a set size and then perform feature fusion;

[0023] The spatial mapping module is used to predict the position coordinates of the radiation source according to the fusion features generated by the feature fusion module.

[0024] Furthermore, drawing a grayscale image including the location information of each sampling point in the target area and the received signal strength thereof as an electromagnetic sampling map includes:

[0025] Converting the received signal strength into a grayscale value between 0 and 1;

[0026] The grayscale value of the pixel corresponding to the sampling point is set to the received signal strength at the corresponding position after conversion, and the grayscale value of the pixels in the remaining area is set to 0, to obtain an electromagnetic sampling map.

[0027] Furthermore, in the mask map, the grayscale value of the pixels in the building area is 1, and the grayscale value of the pixels in the non-building area is 0.

[0028] Furthermore, the feature extraction module includes a first convolution block and a second convolution block connected in sequence; after the input image sample is input into the first convolution block, the output features are sequentially processed by the convolution layer and batch normalization layer of the second convolution block to obtain a first feature map, the first feature map is fused with the input image sample through a residual connection to obtain a second feature map, and the second feature map is sequentially passed through an attention mechanism module and an activation function to output the multi-scale feature map.

[0029] Furthermore, the first convolution block includes a convolution layer, a batch normalization layer and an activation function connected in sequence, and the activation functions of the first convolution block and the second convolution block are both ReLU functions.

[0030] Furthermore, different feature extraction modules are connected through a maximum pooling layer.

[0031] Furthermore, the feature fusion module includes several adaptive average pooling layers and a dimensionality transformation layer; the adaptive average pooling layer corresponds to the feature extraction module one by one, and different multi-scale feature maps are input into the corresponding adaptive average pooling layer and then spliced ​​in the channel dimension. The spliced ​​feature maps are processed by the dimensionality transformation layer and then the fused features are output.

[0032] Furthermore, the spatial mapping layer is a multi-layer perceptron.

[0033] Furthermore, the spatial mapping layer includes an input layer, a first hidden layer, a second hidden layer and an output layer, and the number of neurons in the input layer is the same as the number of features of the fusion feature.

[0034] Furthermore, the loss function of the lightweight radiation source positioning model is

[0035]

[0036] Where W is the number of input image samples, InputMap i and are the i-th input image sample and the i-th training label, F position (·) and Θ represent the lightweight radiation source positioning model and its model parameters respectively.

[0037] Beneficial effects

[0038] Due to the adoption of the above-mentioned technical solution, the present invention offers the following advantages and positive effects compared to existing technologies: The present invention only requires building distribution information, sampling point location information, and the received signal strength at the corresponding locations. It does not rely on high-precision clocks and antenna arrays, nor does it require the construction of fingerprint data to obtain relatively accurate predicted coordinates. This makes implementation easy and cost-effective, thus having a wide range of applications. The present invention uses the sampling point location information and the received signal strength at the corresponding locations to generate an electromagnetic sampling map, which is then superimposed with a mask map containing building distribution information. The generated input image samples are analyzed and processed using a lightweight radiation source localization model to obtain radiation source coordinates. This eliminates the need for abstracting complex physical models and imposes no special requirements on the implementation environment, making it particularly suitable for complex urban environments. The present invention directly utilizes sparse sampling values ​​for positioning, eliminating the error-prone electromagnetic map recovery step, effectively reducing error propagation, and improving the reliability and accuracy of positioning results. The present invention significantly reduces the computational resources consumed during the operation process, ensuring more efficient allocation and utilization of CPU, GPU, and memory resources. The present invention significantly improves inference speed, enabling the completion of positioning tasks in a short period of time, meeting the requirements of applications with high real-time performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of the structure of a lightweight radiation source positioning model according to an embodiment of the present invention;

[0041] Figure 3 is a schematic structural diagram of a feature extraction module according to an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of the structure of the feature fusion module according to an embodiment of the present invention;

[0043] Figure 5 2 is a schematic structural diagram of a spatial mapping module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0045] The embodiment of the present invention relates to a lightweight radiation source positioning method, such as Figure 1 As shown, the following steps are included:

[0046] Get a mask map containing building distribution information in the target area;

[0047] Draw a grayscale image containing the location information of each sampling point in the target area as an electromagnetic sampling map;

[0048] The mask map and the electromagnetic sampling map are fused to obtain the input image sample;

[0049] The lightweight radiation source localization model is used to analyze the input image samples to obtain the radiation source position coordinates.

[0050] like Figure 2 As shown in the figure, the lightweight radiation source localization model includes: Feature Extraction (FE) feature extraction module, which is a structure that combines residual connection and attention mechanism for feature extraction and enhancement; Feature Fusion (FF) feature fusion module, which is used to convert the output features of different levels of FE into fixed-size output feature maps and fuse them while retaining feature distribution information; Spatial Mapping (SM) spatial mapping module, which is used to map high-dimensional and complex feature space to a low-dimensional space that is more suitable for coordinate prediction tasks.

[0051] like Figure 3 As shown in the figure, the FE module consists of two convolution blocks, each of which contains a convolution layer, a batch normalization layer, and a ReLU activation function. In the second convolution block, the input features are subjected to a 1×1 convolution operation to match the output dimension of the batch normalization layer, and then added through a residual connection. The fused feature map passes through a CoordinateAttention (CA) and finally passes through a ReLU activation function. The model connects L FE modules in series, and each FE module is connected by a maximum pooling layer. The input of the first FE module is InputMap, and the input of the second FE module is InputMap. The input of the first FE module is The output of the FE module,

[0052] like Figure 4 As shown in Figure 1, the FF module consists of an adaptive average pooling layer (AAP) and a dimensional transformation layer (DT). The FF module contains L AAPs, The input of the AAP is The output of the FE module, The output results of L AAPs are concatenated in the channel dimension and input into the DT layer. The dimension is converted into a two-dimensional tensor form as the input of the SM module.

[0053] like Figure 5 As shown in the figure, the SM module is a four-layer multilayer perceptron (MLP) consisting of an input layer, two hidden layers, and an output layer. The number of neurons in the input layer is the same as the number of output features of the FF module. The number of neurons in the first and second hidden layers is usually adjusted based on the requirements of the specific task and experiments. The second hidden layer is usually smaller than the first hidden layer. The output layer is responsible for generating the final radiation source localization result and has two neurons.

[0054] This embodiment uses the following method to establish an image sample data set and conduct simulation experiments:

[0055] Make size N x ×N y The building mask map BuildingMask is used to set the radiation source parameters, thereby simulating the electromagnetic map based on the received signal strength (RSS) in the urban environment. And save the radiation source coordinate label GroundTruth. Normalize BuildingMask and RadioMap, where the pixels corresponding to buildings in BuildingMask are 1 and the pixels corresponding to non-building areas are 0, and the pixels corresponding to buildings in RadioMap are 1 and the pixels corresponding to non-building areas are between (0,1). According to BuildingMask, the simulation measurement terminal performs sparse sampling around the building to obtain the mask SampledMask representing the RSS sampling position, where the pixel corresponding to the sampling position is 1 and the rest of the pixels are 0. According to SampledMask, the pixels at the sampling position on RadioMap are retained and the rest of the pixels are set to 0 to obtain the sampled map SampledMap;

[0056] The dataset Divided into training set and test set, where i is the sample number, M is the total number of samples, GroundTruth = (GT x ,GT y );

[0057] SampledMap i and BuildingMask i After conversion to tensors, they are concatenated along the channel dimension to obtain InputMap as the model input.

[0058] When training the model, the training set SampledMap i and BuildingMask i After converting to a tensor, it is spliced ​​in the channel dimension to obtain InputMapi As the model input, the model is optimized by minimizing the Euclidean distance loss function. The Euclidean distance loss function is expressed as:

[0059]

[0060] Among them, W is the number of training samples, InputMap i and are the i-th sample and the i-th radiation source coordinate label, respectively, F Map (·) and Θ represent the model and model parameters, respectively. The trained model is the lightweight radiation source localization model.

[0061] In practical applications, a sampling data acquisition module can be deployed within the target area to obtain the location information of sampling points and the received signal strength at the corresponding locations. The actual location of the radiation source is used as a training label. The sampling data within the target area obtained by the sampling data acquisition module is then represented as a grayscale image. Each pixel represents a sampling location or a location to be estimated. The pixel value at the sampling location is represented by different grayscale levels based on the received signal strength, and the pixel values ​​at other locations are set to 0. A building mask map is generated based on the environmental image data of the target area. Pixels corresponding to buildings are set to 1 in the map, and pixels corresponding to non-building areas are set to 0. The image obtained by superimposing the electromagnetic sampling map and the building mask map is input into the trained model to obtain the predicted coordinates of the radiation source.

Claims

1. A lightweight radiation source positioning method, characterized in that: The following steps are involved: Get a mask map containing building distribution information in the target area; Drawing a grayscale image including the location information of each sampling point in the target area and the received signal strength thereof as an electromagnetic sampling map; fusing the mask map and the electromagnetic sampling map to obtain an input image sample; Analyzing the input image sample using a lightweight radiation source positioning model to obtain radiation source position coordinates; The lightweight radiation source positioning model includes: A plurality of feature extraction modules, wherein the plurality of feature extraction modules are connected in sequence and used to perform multi-scale feature extraction on the input image sample based on residual connection and attention mechanism; A feature fusion module is used to convert the multi-scale feature maps output by different feature extraction modules into a set size and then perform feature fusion; The spatial mapping module is used to predict the position coordinates of the radiation source according to the fusion features generated by the feature fusion module.

2. The method according to claim 1, characterized in that Drawing a grayscale image including the location information of each sampling point in the target area and the received signal strength thereof as an electromagnetic sampling map includes: Converting the received signal strength into a grayscale value between 0 and 1; The grayscale value of the pixel corresponding to the sampling point is set to the received signal strength at the corresponding position after conversion, and the grayscale value of the pixels in the remaining area is set to 0, to obtain an electromagnetic sampling map.

3. The method according to claim 1, characterized in that In the mask map, the grayscale value of pixels in the building area is 1, and the grayscale value of pixels in the non-building area is 0.

4. The method according to claim 1, wherein The feature extraction module includes a first convolution block and a second convolution block connected in sequence; after the input image sample is input into the first convolution block, the output features are sequentially processed by the convolution layer and batch normalization layer of the second convolution block to obtain a first feature map, the first feature map is fused with the input image sample through a residual connection to obtain a second feature map, and the second feature map is sequentially passed through an attention mechanism module and an activation function to output the multi-scale feature map.

5. The method according to claim 4, characterized in that The first convolution block includes a convolution layer, a batch normalization layer, and an activation function connected in sequence, and the activation functions of the first convolution block and the second convolution block are both ReLU functions.

6. The method according to claim 1, characterized in that The different feature extraction modules are connected through a maximum pooling layer.

7. The method according to claim 1, characterized in that The feature fusion module includes several adaptive average pooling layers and a dimensionality transformation layer; the adaptive average pooling layer corresponds to the feature extraction module one by one, and different multi-scale feature maps are input into the corresponding adaptive average pooling layer and then spliced ​​in the channel dimension. The spliced ​​feature maps are processed by the dimensionality transformation layer and then the fused features are output.

8. The method according to claim 1, characterized in that The spatial mapping layer is a multi-layer perceptron.

9. The method according to claim 8, characterized in that The spatial mapping layer includes an input layer, a first hidden layer, a second hidden layer and an output layer, and the number of neurons in the input layer is the same as the number of features of the fusion feature.

10. The method according to claim 1, characterized in that The loss function of the lightweight radiation source positioning model is: Where W is the number of input image samples, InputMap i and are the i-th input image sample and the i-th training label, F position (·) and Θ represent the lightweight radiation source positioning model and its model parameters respectively.

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