CN115508773B - Multi-station passive positioning method and system by time difference method, electronic equipment and storage medium - Google Patents
Multi-station passive positioning method and system by time difference method, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure provides a time difference method multi-station passive positioning method, a system, electronic equipment and a storage medium, belonging to the field of electronic reconnaissance, wherein the method comprises the following steps: obtaining observation parameter information of each base station; and inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate. The target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information. Compared with the prior art, the method for obtaining the target radiation source positioning model by training the deep neural network replaces the traditional iterative algorithm, increases the accuracy and reliability of multi-station passive positioning by the time difference method, does not depend on priori information, is quicker to position, and has very good robustness.
Description
Technical Field
The embodiment of the disclosure belongs to the technical field of electronic reconnaissance, and particularly relates to a multi-station passive positioning method and system by a time difference method, electronic equipment and a storage medium.
Background
Distributed passive positioning based on time difference of arrival (Time Difference of Arrival, TDOA), also known as time difference multi-station passive positioning, refers to receiving and processing non-cooperative signals from the same target radiation source by a plurality of fixed or mobile receiving stations distributed in different places, so as to obtain time difference of arrival of the non-cooperative signals of the target radiation source at different receiving stations, and a related positioning equation containing the position of the radiation source is established according to the time difference information, so that the calculation of the position of the target radiation source is realized.
For the conventional iterative method, there are mainly local optimization algorithms represented by gradient descent method, newton method, gaussian-newton iterative method, levenberg-Marquardt (Levenberg-Marquardt) method, etc., and global optimization algorithms represented by particle swarm algorithm, genetic evolution algorithm, etc. The method is characterized in that the position of a target radiation source is set as a parameter to be solved, and an optimization algorithm is used for iterating the target function to enable the target function to be lower than a set threshold value or reach the maximum iteration step number to stop iterating, so that the final target radiation source position is obtained. The local optimization algorithm has high convergence speed, is easily influenced by an initial value, and is extremely dependent on prior information; global optimization algorithms, while somewhat reducing the dependence on initial value selection, are still subject to accuracy of a priori information and take longer to iterate.
Therefore, a more accurate, rapid and a priori information free positioning method is needed for a time difference multi-station passive positioning system. This is critical to the performance enhancement of distributed passive location electronic scout systems.
Disclosure of Invention
The embodiment of the disclosure aims to at least solve one of the technical problems in the prior art and provides a time difference method multi-station passive positioning method, a system, electronic equipment and a storage medium.
In one aspect, the disclosure provides a multi-station passive positioning method by a time difference method, including:
obtaining observation parameter information of each base station; the observation parameter information comprises base station actual position information and actual base station time difference information;
inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information.
Optionally, the target radiation source positioning model is obtained by training the following steps:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates;
and training the deep neural network by taking the base station position information and the base station time difference information as input and the target radiation source coordinates as output to obtain the trained target radiation source positioning model.
Optionally, the base station position information includes position coordinates of a fixed primary base station and a plurality of secondary base stations, and a base station position measurement standard deviation; and/or, the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of each secondary base station relative to the fixed main base station.
Optionally, the training the deep neural network with the base station position information and the base station time difference information as inputs and the target radiation source coordinates as outputs includes:
pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size;
taking the base station position information and the base station time difference information as input, taking the target radiation source coordinates as output, and training the configured deep neural network;
stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network.
Optionally, the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) And outputting a neuron activation value vector for the final layer of the deep neural network.
Optionally, inputting the sample set into a trained target radiation source positioning model, outputting a target radiation source calculation coordinate, and calculating a positioning relative error for evaluating the accuracy of the target radiation source positioning model;
the positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the stationary main base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the stationary main base station and the coordinates of the target radiation source.
Another aspect of the present disclosure provides a multi-station passive positioning system using the time difference method, wherein the system includes:
the acquisition module is used for acquiring the observation parameter information of each base station; the observation parameter information comprises base station actual position information and actual base station time difference information;
the positioning module is used for inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information.
Optionally, the system further comprises a training module, wherein the training module is used for:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates;
and training the deep neural network by taking the base station position information and the base station time difference information as input and the target radiation source coordinates as output to obtain the trained target radiation source positioning model.
Another aspect of the present disclosure provides an electronic device, including:
at least one processor; the method comprises the steps of,
and a memory communicatively coupled to the at least one processor for storing one or more programs that, when executed by the at least one processor, cause the at least one processor to implement the time difference multi-station passive positioning method as described above.
A final aspect of the present disclosure provides a computer readable storage medium storing a computer program which when executed by a processor implements a time difference multi-station passive positioning method as described above.
Compared with the prior art, the method for obtaining the target radiation source positioning model by training the deep neural network replaces the traditional iterative algorithm, increases the accuracy and reliability of the multi-station passive positioning by the time difference method, does not depend on priori information, is quicker to position, and has very good robustness.
Drawings
FIG. 1 is a flow chart of a multi-station passive positioning method by time difference method according to an embodiment of the disclosure;
FIG. 2 is a block diagram of a deep neural network according to another embodiment of the present disclosure;
FIG. 3 is a graph showing a loss function versus training time during training of a deep neural network according to another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a multi-station passive positioning system according to another embodiment of the disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to another embodiment of the disclosure.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present disclosure, the present disclosure will be described in further detail with reference to the accompanying drawings and detailed description.
As shown in fig. 1, an embodiment of the present disclosure provides a multi-station passive positioning method by a time difference method, which includes the following steps:
and S11, training a target radiation source positioning model.
First, a sample set of training parameter information is generated. Wherein each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates.
Specifically, in this step, a sample set including a large number of samples may be generated according to a time difference of arrival model (TDOA) in a simulation scene, where each sample includes data such as base station position information, base station time difference information, and target radiation source coordinates. The sample sets are randomly disturbed, 4/5 of the data in the sample set form a training set, and 1/5 of the data form a verification set.
The above base station location information may include location coordinates of a fixed primary base station and each secondary base station and a base station location measurement standard deviation, and the base station time difference information may include a measurement time difference and a time difference measurement standard deviation of each secondary base station relative to the fixed primary base station.
In one embodiment, the target radiation source may be located using five base stations, at least one of which is a fixed base station, and the remaining base stations may be mobile base stations. Any one fixed base station is used as a fixed main base station, and the rest base stations are used as auxiliary base stations. The position coordinates of each base station are respectivelyStandard deviation of base station position measurement is sigma s The measurement time difference of each secondary base station relative to the main base station is respectively deltat 21 、Δt 31 、Δt 41 、Δt 51 Standard deviation of time difference measurement is sigma Δt 。
And then, taking the base station position information and the base station time difference information as inputs, taking the target radiation source coordinates as outputs, and training a deep neural network (Deep Neural Networks, DNN) to obtain the trained target radiation source positioning model.
Specifically, in this step, the above five base stations will be described as an example, and the base station position coordinates will be describedStandard deviation sigma of base station position measurement s Measurement time difference deltat of each secondary base station relative to the primary base station 21 、Δt 31 、Δt 41 、Δt 51 Standard deviation sigma of time difference measurement Δt As input to the DNN, i.e. inputTaking the target radiation source coordinates as the output of the DNN, i.e. output y= [ x ] t ,y t ,z t ]。
The DNN's loss function, number of hidden layers and number of hidden neurons, maximum number of training, network training optimizer, learning rate, and batch size should also be preconfigured before training the DNN.
Wherein the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) Is saidThe vector of neuron activation values output by the DNN final layer.
Training is then started, weighting w for each neuron in DNN using a random gradient descent method l And bias b l (l=2, 3, …, L) learning. First initialize w l And b l (l=2, 3, …, L), the DNN is trained according to the learning rate η, the maximum training number N, and the batch size m set as described above.
And (3) saving DNN parameters with the minimum loss function in the verification set in the training process, and stopping training until the maximum training times are reached.
In some embodiments, after the DNN is trained, the sample set may be input into a trained target radiation source positioning model, the target radiation source calculation coordinates are output, and the positioning relative error is calculated, for evaluating the accuracy of the target radiation source positioning model.
The positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the stationary main base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the stationary main base station and the coordinates of the target radiation source.
And step S12, obtaining the observation parameter information of each base station. The observation parameter information comprises actual position information of a base station and actual base station time difference information.
Specifically, in this step, a fixed primary base station and four secondary base stations respectively receive signals from the target radiation source, and form observation parameter information. The observed parameter information comprises base station actual position information and actual base station time difference information, wherein the base station actual position information comprises real-time position coordinates of five base stations Standard deviation sigma 'of base station position measurement' s The actual base station time difference information comprises the measured time difference delta t 'of each secondary base station relative to the main base station' 21 、Δt' 31 、Δt' 41 、Δt' 51 Time difference measurement standard deviation sigma' Δt . And then obtaining the observation parameter information of the five base stations for subsequent processing of data.
And S13, inputting the observation parameter information into a trained target radiation source positioning model to obtain a target radiation source prediction coordinate.
Specifically, the observed parameter information in the step S12 is input into the trained target radiation source positioning model, that is, the actual position information of the base station and the actual base station time difference informationInputting the target radiation source positioning model to obtain an output target radiation source prediction coordinate y= [ x ]' t ,y' t ,z' t ]。
Compared with the prior art, the method for obtaining the target radiation source positioning model by training DNN replaces the traditional iterative algorithm, the accuracy and the reliability of the time difference method multi-station passive positioning method are improved, priori information is not relied on, positioning is faster, and the method has good robustness.
It should be noted that, in the time difference multi-station passive positioning method of the present embodiment, the step of training the target radiation source positioning model is not necessary, and in a possible implementation manner, the step of training the target radiation source positioning model may be omitted, and a pre-trained target radiation source positioning model may be directly adopted.
The effect of the time difference method multi-station passive positioning method disclosed by the disclosure will be further verified and explained through a specific simulation experiment.
Simulation conditions
The simulation conditions are configured by adopting a notebook computer with an Intel (R) Xeon (R) W-10855M CPU@2.80GHz2.81GHz, a memory 64G and a Windows 10 operating system and a NVIDIA Quadro T2000 with Max-Q Design independent display card, and the simulation software is configured by adopting MATLAB (R2021 a) and JetBrainsPyCharm 2018.3.7x64.
(II) simulation content and result analysis
Assuming a total of five base stations, base station 1 is set as the fixed master base station, coordinates s 1 = (20,3,0.02) (km), the remaining four base stations are secondary base stations, including ground base station 2, coordinates s 2 = (35,45,0.01) (km), sea base station 3, coordinates s 3 = (10,50,0) (km), ground base station 4, coordinates s 4 = (40,55,0.3) (km), sea base station 5, coordinates s 5 = (45,10,0.05) (km). Setting standard deviation sigma of base station position measurement s ∈[1,10](m) time difference measurement standard deviation sigma Δt ∈[1,20](ns). Setting the coordinate distribution range of the target radiation source as A total of 523748 samples were generated from the above conditions, taking 0.43 hours, and the 523748 samples were regarded as one sample set. The sample sets are randomly disturbed, 4/5 of the data in the sample set form a training set, and 1/5 of the data form a verification set.
The number of hidden layers is set to be 3, the number of hidden neurons is respectively 150,100 and 50, namely the DNN structure is [21,150,100,50,3], and the structure diagram is shown in figure 2. The maximum training times were set to 1000, the network training optimizer was an SGD optimizer, the learning interest rate was set to 0.0001, and the batch size was set to 40.
And training DNN by using the generated sample set and the set parameters, wherein the final training time is 48.27 hours. The graph of the change of the loss function C with training time is shown in fig. 3.
Respectively inputting the base station position information and base station time difference information in the training set and the verification set samples into a target radiation source positioning model to obtain output target radiation source calculation coordinates (x) cal ,y cal ,z cal ) And then it is matched with the target radiation source coordinates (x real ,y real ,z real ) Primary base station coordinatesSubstituting the above formula (2) together, calculating the relative positioning error re of each sample, and recording the number of samples in which the relative positioning error re is within 1%, to obtain the percentage of the number of samples in which the relative positioning error is within 1% as shown in table 1.
Table 1:
as can be seen from table 1, in the embodiment of the present disclosure, the positioning accuracy of the target radiation source positioning model trained by using DNN is extremely high, and the method is applicable to a fixed base station and a mobile base station, and has strong robustness and universality. In this embodiment, all the other base stations except the main base station are fixed base stations, which may be fixed base stations or mobile base stations, or may be partially fixed base stations, with the other base stations being mobile base stations.
Another embodiment of the present disclosure provides a time difference multi-station passive positioning system, as shown in fig. 4, comprising:
an acquisition module 401, configured to acquire observation parameter information of each base station; the observation parameter information comprises base station actual position information and actual base station time difference information;
the positioning module 402 is configured to input the observation parameter information into a pre-trained target radiation source positioning model, so as to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training DNN in advance according to training parameter information.
Specifically, a fixed primary base station and four secondary base stations respectively receive signals from signal sources to form observation parameter information. The observed parameter information comprises base station actual position information and actual base station time difference information, wherein the base station actual position information comprises real-time position coordinates of five base stationsStandard deviation sigma 'of base station position measurement' s The actual base station time difference information comprises the measured time difference delta t 'of each secondary base station relative to the main base station' 21 、Δt' 31 、Δt' 41 、Δt' 51 Time difference measurement standard deviation sigma' Δt . The acquisition module 401 then acquires the observed parameter information of the five base stations, and the positioning module 402 processes the observed parameter information. The positioning module 402 will observe parameter information, i.eInputting the target radiation source positioning model to obtain an output target radiation source prediction coordinate y= [ x ]' t ,y' t ,z' t ]。
According to the multi-station passive positioning system adopting the time difference method, the target radiation source is positioned by adopting the method, an initial value is not needed to be selected, and compared with the traditional iterative algorithm, the multi-station passive positioning system adopting the time difference method is more accurate, reliable and rapid.
Illustratively, the system further includes a training module 403 for:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates;
and training the DNN by taking the base station position information and the base station time difference information as input and the target radiation source coordinates as output to obtain the trained target radiation source positioning model.
Specifically, the training module 403 generates a large number of samples in the simulation scene, randomly scrambles the samples, and trains DNN using the scrambled samples, thereby obtaining the target radiation source positioning model.
According to the embodiment of the disclosure, a large number of samples are generated through the training module, DNN is trained, so that the trained target radiation source positioning model has high precision, and the positioning model can be directly provided for the positioning module for use, so that the positioning system can rapidly and accurately position the target radiation source.
As shown in fig. 5, another embodiment of the present disclosure provides an electronic device, including:
at least one processor 501, and a memory 502 communicatively coupled to the at least one processor 501 for storing one or more programs that, when executed by the at least one processor 501, enable the at least one processor 501 to implement the time difference multi-station passive positioning method as described above.
Where the memory and the processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors and the memory together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory may be used to store data used by the processor in performing operations.
According to the electronic equipment, the multi-station passive positioning method of the time difference method is achieved, and compared with equipment for positioning a target radiation source by using a traditional iterative algorithm, the electronic equipment has better accuracy and reliability and is faster in positioning.
Another embodiment of the present disclosure provides a computer readable storage medium storing a computer program which when executed by a processor implements a time difference multi-station passive positioning method as described above.
The computer readable storage medium may be included in the system and the electronic device of the present disclosure, or may exist alone.
A computer readable storage medium may be any tangible medium that can contain, or store a program that can be electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, device, more specific examples including, but not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
The computer readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, with the computer readable program code embodied therein, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.
It is to be understood that the above embodiments are merely exemplary embodiments employed to illustrate the principles of the present disclosure, however, the present disclosure is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the disclosure, and are also considered to be within the scope of the disclosure.
Claims (5)
1. A multi-station passive positioning method by a time difference method, which is characterized by comprising the following steps:
obtaining observation parameter information of each base station; the observation parameter information comprises base station actual position information and actual base station time difference information;
inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training a deep neural network in advance according to training parameter information; the target radiation source positioning model is obtained by training the following steps:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates;
taking the base station position information and the base station time difference information as input, taking the target radiation source coordinates as output, training the deep neural network to obtain a trained target radiation source positioning model, and comprising the following steps: pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size; taking the base station position information and the base station time difference information as input, taking the target radiation source coordinates as output, and training the configured deep neural network; stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network; the base station position information comprises position coordinates of a fixed main base station and a plurality of auxiliary base stations and base station position measurement standard deviation; and/or, the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of each secondary base station relative to the fixed primary base station;
inputting the sample set into a trained target radiation source positioning model, outputting a target radiation source calculation coordinate, calculating a positioning relative error, and evaluating the precision of the target radiation source positioning model; the positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the stationary main base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the stationary main base station and the coordinates of the target radiation source.
2. The method according to claim 1, characterized in that the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) And outputting a neuron activation value vector for the final layer of the deep neural network.
3. A time difference multi-station passive positioning system, the system comprising:
the acquisition module is used for acquiring the observation parameter information of each base station; the observation parameter information comprises base station actual position information and actual base station time difference information;
the positioning module is used for inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training a deep neural network in advance according to training parameter information;
training module for: generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, base station time difference information and target radiation source coordinates;
taking the base station position information and the base station time difference information as input, taking the target radiation source coordinates as output, training the deep neural network to obtain a trained target radiation source positioning model, and comprising the following steps: pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size; taking the base station position information and the base station time difference information as input, taking the target radiation source coordinates as output, and training the configured deep neural network; stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network; the base station position information comprises position coordinates of a fixed main base station and a plurality of auxiliary base stations and base station position measurement standard deviation; and/or, the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of each secondary base station relative to the fixed primary base station;
the verification module is used for inputting the sample set into a trained target radiation source positioning model, outputting target radiation source calculation coordinates, calculating positioning relative errors and evaluating the accuracy of the target radiation source positioning model; the positioning relative error re satisfies the following relation:
wherein re represents the relative error in positioning,(x cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the stationary main base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the stationary main base station and the coordinates of the target radiation source.
4. An electronic device, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor for storing one or more programs that, when executed by the at least one processor, cause the at least one processor to implement the time difference multi-station passive positioning method of any of claims 1 or 2.
5. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the time difference multi-station passive positioning method according to any one of claims 1 or 2.
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