Radiation source positioning method

Abstract

The invention relates to a radiation source positioning method. The method comprises the following steps: acquiring a mask map containing building distribution information in a target area; drawing a grey-scale map containing the position information and the received signal intensity of each sampling point in the target area 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 by using a radiation source positioning model to obtain a radiation source position coordinate; the radiation source positioning model comprises an encoder and a decoder which are used for extracting multi-scale fusion features of the input image samples; and the space mapping layer is used for predicting the position coordinates of the radiation source according to the multi-scale fusion features. The position of the radiation source can be effectively and accurately obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of radiation source positioning, and in particular to a 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, thereby effectively assisting in the precise positioning 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 radiation source and then converting it into distance. At least three measurement terminals are required to calculate the position of the target radiation source.

[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 ray 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 reliance 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 needed 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 radiation source position.

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

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

[0018] Draw 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 radiation source positioning model to obtain the radiation source position coordinates; the radiation source positioning model includes:

[0021] An encoder, used for performing multi-scale feature extraction on the input image sample to obtain multi-scale coding features;

[0022] A decoder, used for gradually restoring the size of the multi-scale coding feature along different scales to be the same as the size of the input image sample, so as to obtain a multi-scale decoding feature;

[0023] Nested and dense skip connections are used to fuse the multi-scale encoding features and the multi-scale decoding features of the same scale to obtain multi-scale fused features;

[0024] The spatial mapping layer is used to predict the position coordinates of the radiation source according to the multi-scale fusion features.

[0025] Furthermore, the drawing of 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:

[0026] Convert the received signal strength into a grayscale value between 0 and 1;

[0027] 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 pixel in the remaining area is set to 0, so as to obtain an electromagnetic sampling map.

[0028] 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.

[0029] Furthermore, the spatial mapping layer includes a Flatten layer and a fully connected layer, and the multi-scale fusion features are processed by the Flatten layer and the fully connected layer in sequence to output the radiation source position coordinates.

[0030] Furthermore, the encoder and decoder form a nested connection of UNet sub-encoder-decoder networks of different depths.

[0031] Furthermore, the UNet sub-codec networks of different depths include UNet sub-codec networks with depths of 2, 3, 4, and 5, respectively.

[0032] Furthermore, the loss function of the radiation source positioning model is:

[0033]

[0034] 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, respectively, position (·) and Θ represent the radiation source positioning model and its model parameters respectively.

[0035] Beneficial Effects

[0036] Due to the adoption of the above technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention only needs to obtain the building distribution information, the location information of the sampling points and the received signal strength of the corresponding positions, does not rely on high-precision clocks and antenna arrays, and does not need to build fingerprint data to obtain relatively accurate predicted coordinates, which is easy to implement and deploy and has low cost, so it has a wide range of application scenarios; the present invention uses the location information of the sampling points and the received signal strength of the corresponding positions to generate an electromagnetic sampling map, and superimposes it with a mask map containing the building distribution information. The generated input image samples are analyzed and processed by the radiation source positioning model based on UNet++ to obtain the radiation source coordinates. There is no need to abstract a complex physical model, and there is no special requirement for the implementation environment, which is particularly suitable for complex urban environments; the present invention directly uses sparse sampling values ​​for positioning, omitting the electromagnetic map recovery step that is prone to errors, effectively reducing error transmission, and improving the reliability and accuracy of the positioning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of an embodiment of the present invention;

[0038] Figure 2 It is a structural diagram of a radiation source positioning model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it 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 within the scope limited by the appended claims of the application equally.

[0040] The embodiment of the present invention relates to a radiation source positioning method, such as Figure 1 As shown, the following steps are included:

[0041] Get a mask map containing building distribution information in the target area;

[0042] Draw a grayscale image containing the location information of each sampling point in the target area as an electromagnetic sampling map;

[0043] The mask map and the electromagnetic sampling map are fused to obtain the input image sample;

[0044] The radiation source positioning model is used to analyze the input image samples to obtain the radiation source position coordinates.

[0045] The radiation source positioning model includes: a codec part and a spatial mapping layer. The spatial mapping layer consists of a Flatten layer and a fully connected layer, and the functions of the remaining parts can be replaced by any other network to achieve.

[0046] In this implementation, a multi-scale fusion deep supervision network based on the UNet++ model is used. Figure 2 As shown, including:

[0047] The encoder part is used to extract and compress features from the input data;

[0048] The decoder part is used to decode the features extracted by the encoder and gradually restore the spatial resolution of the feature map to generate a result corresponding to the input image;

[0049] The nested and dense skip connections connect different levels in the encoder and decoder, bridging the semantic gap between the encoder and decoder, allowing the model to better utilize feature information at different levels;

[0050] The spatial mapping layer is used to convert the feature map into specific coordinate values ​​to realize the mapping process from feature space to coordinate space.

[0051] The encoder part includes five VGGBlocks, each of which contains two encoding blocks. Each encoding block contains a 3×3 convolution layer, a batch normalization layer, and a LeakyReLU activation function. VGG Blocks are downsampled by average pooling to compress features.

[0052] The decoder part contains five VGGBlocks, each of which contains two decoding blocks. Each decoding block contains a 3×3 convolution layer, a batch normalization layer, and a LeakyReLU activation function. The sub-VGGBlocks are upsampled by nearest neighbor interpolation to gradually restore the original size of the feature map.

[0053] The nested and dense skip connections part nests UNet sub-encoder-decoder networks with depths of 2, 3, 4, and 5 in the overall encoder-decoder network. Specifically, the deeper sub-network contains the shallower sub-network as part of it, forming a hierarchical structure. The VGGBlock in the decoding structure receives the outputs of the previous VGGBlocks of the same layer and the upsampled outputs of the lower layer VGGBlocks.

[0054] The spatial mapping layer consists of a Flatten layer and a fully connected layer.

[0055] This embodiment establishes an image sample data set and conducts simulation experiments by the following method:

[0056] 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; 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 remaining pixels are 0. According to SampledMask, retain the pixels at the sampling position on RadioMap, and set the remaining pixels to 0 to obtain the sampled map SampledMap;

[0057] Add SampledMap and BuildingMask to get InputMap;

[0058] 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 ).

[0059] When training the model, the training set Input the model and optimize the model by minimizing the Euclidean distance loss function. The Euclidean distance loss function is expressed as:

[0060]

[0061] 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, Map (·) and Θ represent the model and model parameters respectively. The trained model is the multi-scale fusion deep supervision radiation source localization model.

[0062] In practical applications, the sampling data acquisition module can be deployed in the target area to obtain the location information of the sampling point and the received signal strength at the corresponding position, and the actual position of the radiation source can be used as a training label. The sampling data in the target area obtained by the sampling data acquisition module is then represented as a grayscale image. One pixel represents a sampling position or a position to be estimated. The pixel value at the sampling position is represented by different grayscale levels according to the strength of the received signal, and the pixel values ​​of the remaining positions are set to 0. A building mask map is generated based on the environmental image data of the target area. The corresponding pixels of the building in the figure are 1, and the corresponding pixels of the non-building area are 0. The image obtained by superimposing the electromagnetic sampling map and the building mask map is input into the trained model to obtain the prediction result of the radiation source coordinates.

Claims

1. A radiation source positioning method, characterized in that: The following steps are involved: Get a mask map containing building distribution information in the target area; Draw 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 samples using a radiation source positioning model to obtain radiation source position coordinates; The radiation source positioning model includes: An encoder, used for performing multi-scale feature extraction on the input image sample to obtain multi-scale coding features; A decoder, used for restoring the size of the multi-scale coding feature to the same size as the input image sample step by step along different scales to obtain a multi-scale decoding feature; Nested and dense skip connections are used to fuse the multi-scale encoding features and the multi-scale decoding features of the same scale to obtain multi-scale fused features; The spatial mapping layer is used to predict the position coordinates of the radiation source according to the multi-scale fusion features.

2. The method according to claim 1, characterized in that The drawing of 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 comprises: Convert 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 pixel in the remaining area is set to 0, so as to obtain an electromagnetic sampling map.

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

4. The method according to claim 1, characterized in that: The spatial mapping layer includes a Flatten layer and a fully connected layer. The multi-scale fusion features are processed by the Flatten layer and the fully connected layer in sequence to output the radiation source position coordinates.

5. The method according to claim 1, characterized in that The encoder and decoder form a nested connection of UNet sub-encoder-decoder networks of different depths.

6. The method according to claim 5, characterized in that The UNet sub-codec networks of different depths include UNet sub-codec networks with depths of 2, 3, 4, and 5 respectively.

7. The method according to claim 1, characterized in that The loss function of the 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, respectively, position (·) and Θ represent the radiation source positioning model and its model parameters respectively.

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