CN113970762A - Method and system for positioning multistage interference source - Google Patents
Method and system for positioning multistage interference source Download PDFInfo
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Abstract
The invention discloses a method and a system for positioning a multi-stage interference source, which are characterized in that signals sampled by all receivers are transmitted to a processing center for Fourier transformation, the average power of the signals received by each receiver is calculated, a plurality of groups of training data sets are generated by randomly generating interference source positions in different ranges, and then the training data sets are used for training an FNN neural network corresponding to each group of data; respectively inputting the power vectors of the received signals of the receiver obtained by the processing center into a plurality of FNN neural networks, and reducing the current positioning area through the output of the neural networks until the range of the current positioning area is smaller than the lower limit of the range of the sub-area; and gridding the final sub-region, calculating a cost function corresponding to each grid central point through the receiving vectors of the receivers after Fourier transformation and the grid central points, and taking the central point of the grid with the maximum cost function as an estimated value of the position of the interference source. The problem that the traditional precision is limited by parameter estimation precision and the calculation complexity of a direct positioning technology is too high is solved.
Description
Technical Field
The invention belongs to the technical field of target positioning, and particularly relates to a method and a system for positioning a multistage interference source.
Background
The Global Navigation Satellite System (GNSS) is a generic name of a Satellite Navigation System that provides continuous positioning, Navigation and Time service (PVT) for ground users through a space Satellite constellation, and is widely applied to various fields to become a key infrastructure, and the importance of Satellite Navigation and positioning technology in national defense and military is self-evident, and has also shown huge application prospects and broad commercial markets in the civil field. GNSS systems are highly susceptible to interference, and signal-only interference includes intra-satellite (multipath), intra-system (near-far problem), inter-system (e.g., galileo interfering with the global positioning system), and extra-system interference, which may be naturally occurring (e.g., weather) or artificially induced. In addition, the application field and environment thereof are increasingly complicated, which makes GNSS susceptible to radio frequency interference from intentional or unintentional sources, so that its anti-interference technology becomes a hot spot for research and application in the related field. The main focus here is the identification of the location of the disturbance in the anti-jamming technique, i.e. the localization of the disturbance source.
Positioning of an interference source for a navigation satellite signal receiver belongs to passive positioning, and usually requires mutual cooperation of a plurality of sensors or receivers, and a positioned object neither sends out a positioning request nor communicates with a positioning system to exchange information. Conventional two-step positioning methods generally use a combination of Received Signal Strength (RSS), direction of Arrival (AOA), Time difference of Arrival (TDOA), and Frequency difference of Arrival (FDOA) measurements to estimate the location of the interference source, but the positioning accuracy is limited by parameter estimation errors and is not ideal at low snr. Different from the traditional two-step positioning method, the Direct Position Determination (DPD) method uses the original signal sampled by the receiver, directly performs grid search on the positioning range to find the grid center point with the largest cost function as the interference source Position estimation, without estimating the intermediate parameter first, and the DPD method performs better than the two-step method at low signal-to-noise ratio, but the DPD method uses grid search to find the global optimal solution complexity is too high.
In addition, at present, deep learning technology has become a research hotspot in the anti-interference field due to strong feature mining and data processing and analyzing capabilities, and research for performing interference detection and interference identification of a GNSS receiver by using the deep learning technology appears, but deep learning is rarely applied to positioning of an outdoor interference source of a navigation satellite system.
Aiming at a static interference source scene of a GNSS receiver, the interference source positioning technology based on deep learning is researched, and a multi-stage interference source positioning method and a multi-stage interference source positioning system based on a neural network and a direct positioning method are provided, wherein the positioning process comprises two steps: firstly, carrying out preliminary positioning by using a plurality of FNN neural networks trained in advance to obtain a region with an interference source in a certain size; and secondly, carrying out grid search in the primarily positioned area by using a DPD method.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and a system for positioning a multi-stage interference source, aiming at the above deficiencies in the prior art, and solve the problems that the precision of the conventional two-step method is limited by the parameter estimation precision and the calculation complexity of the direct positioning technology is too high.
The invention adopts the following technical scheme:
a multi-stage interference source positioning method comprises the following steps:
s1, modeling the positioning problem of the interference source in the satellite navigation system, inputting the signal power received by all receivers as a neural network, using the number of the sub-region where the interference source is located as a training label, establishing a plurality of FNN neural networks corresponding to different regions in size, and training;
s2, halving the current positioning range into two sub-regions, inputting the signal power of all receivers into the FNN neural network trained in the step S1 to obtain the sub-region where the interference source is located, and reducing the current positioning range to the sub-region;
s3, judging the size of the current positioning range obtained in the step S2, and if the size of the obtained sub-region is smaller than the set lower limit T, calling the current region as a final sub-region;
s4, dividing grids in the final sub-area determined in the step S3, carrying out Fourier transform on the sampling signals of all receivers, and then calculating a target function corresponding to each grid;
and S5, comparing all the objective functions obtained in the step S4 to obtain a maximum value, taking a grid central point corresponding to the maximum value of the objective function as an estimated value of the position of the interference source, and obtaining the position of the interference source according to the estimated value.
Specifically, step S1 specifically includes:
s101, modeling an interference source positioning problem in a satellite navigation system, wherein L static receivers synchronous in frequency and time are provided, and sampling frequencies are fsThe location of the l-th receiver is pl＝(xl,yl) Determining the mixed signal r received by the ith receiver, wherein the position of the static interference source is q ═ x, yl(t) a model determining that a real satellite signal is embedded in the hybrid signal;
s102, fixing the position of a receiver, randomly generating the position of an interference source in regions in different ranges, dividing the current region into two sub-regions, generating a plurality of groups of training data sets in different ranges, wherein the training data are the received signal strength of the receiver, and the training labels are the numbers of the sub-regions where the interference source is located;
s103, training a plurality of FNN neural networks corresponding to different range areas, and then selecting a network structure capable of accurately distinguishing the numbers of the sub-areas where the interference sources are located according to the training results.
Further, in step S101, the real satellite signal model is represented as:
wherein the content of the first and second substances,
representing the true satellite signal power received by the ith receiver, C (t) representing the spreading code, D (t) representing the navigation message data,
representing the time delay of the real satellite signal to the ith receiver, fcRepresenting the carrier frequency of the real satellite signal, fD,lWhich is indicative of the doppler shift frequency and,
indicating the initial phase of the carrier.
Further, in step S102, the initial region is gradually divided into two regions of different ranges, and the number i, i being 1,2 is performed on each divided sub-region, and the received signal power vector P and the sub-region number of the multiple groups of receivers corresponding one to one are obtained by randomly setting the interference source positions multiple times in the regions of different sizes, so as to obtain training data sets in the regions of different ranges.
Further, in step S103, the FNN neural network inputs the received signal strength power vector P of the L receivers, and outputs the received signal strength power vector P as a sub-region number i, where i is 1 or 2, the FNN neural network includes an input layer, an output layer, and a hidden layer, and the number of nodes and the number of layers in the hidden layer are continuously adjusted manually during training.
Specifically, in step S3, if the size of the current positioning range obtained in step S2 is larger than the set lower limit T of the size of the sub-area, step S2 is repeated.
Specifically, in step S4, the objective function L (r; p) associated with the received signal vector r and the interference source location p is:
wherein the maximization of B is equivalent toMaximum eigenvalue of B is maximum, L is number of receivers, rlIn order to superimpose the complex signals,
for the frequency domain received signal, N is the received signal sampling point, blT is the time interval for which the path attenuation coefficient is unknown.
Specifically, in step S5, the estimated value of the interference source position
Comprises the following steps:
wherein, the maximization of B is equivalent to the maximization of the maximum characteristic value of B, and p is the coordinate of the center point of each grid.
Another technical solution of the present invention is a multi-stage interference source positioning system, including:
the signal processing module is used for modeling the positioning problem of the interference source in the satellite navigation system, inputting the signal power received by all the receivers as a neural network, using the number of the sub-area where the interference source is positioned as a training label, and establishing and training a plurality of FNN neural networks corresponding to different areas in size;
the offline training module equally divides the current positioning range into two sub-regions, inputs the signal power of all receivers into the FNN neural network trained by the signal processing module to obtain the sub-region where the interference source is located, and reduces the current positioning range to the sub-region;
the online positioning module is used for judging the size of the current positioning range obtained by the offline training module, if the size of the obtained subregion is smaller than a set lower limit T, the region at the moment is called a final subregion, the Fourier transform is carried out on the sampling signals of all the receivers, and then a target function corresponding to each grid is calculated;
and the direct positioning module compares all the target functions obtained by the online positioning module to obtain a maximum value, takes a grid central point corresponding to the maximum value of the target function as an estimation value of the position of the interference source, and obtains the position of the interference source according to the estimation value.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the multi-stage interference source positioning method, the range of the whole positioning area is large, and the complexity of grid searching is overhigh by directly using a direct positioning method during online positioning, so that the range of the positioning area is gradually reduced by using a trained neural network, and the direct positioning method is used in a final sub-area, so that the positioning accuracy close to that of the direct positioning method is obtained, and the complexity of online positioning is reduced.
Further, modeling is carried out on a system scene of the interference source positioning problem in the satellite navigation system, the input of the neural network is determined to be the signal power received by all receivers, the training label is the number of the sub-area where the interference source is located, and a plurality of FNN neural networks corresponding to different areas in size are established and trained.
Further, for fitting reality, since a model of real satellite signals is the background of the entire system, simulation is performed based on this model to generate training data.
Further, the position of the interference source is randomly generated in the regions of different ranges, the current region is divided into two sub-regions, a plurality of sets of training data sets in different ranges are generated under the system model in step S101, the training data is the received signal strength of the receiver, and the training labels are the numbers of the sub-regions where the interference source is located.
Further, the FNN neural networks corresponding to a plurality of different range regions are trained by using the training data set and the training labels generated in S102, and then a network structure capable of accurately distinguishing the numbers of the sub-regions where the interference sources are located is selected according to the training result.
Further, if the size of the current positioning range obtained in step S2 is greater than the set lower limit T of the size of the sub-region, it is indicated that the current positioning range can be further narrowed, and narrowing of the positioning range means reduction of complexity of a direct positioning method in the final sub-region, so that the current positioning range is continuously narrowed by repeating step S2, and the current positioning range has a lower limit, that is, the lower limit T of the size of the sub-region.
Furthermore, grids are divided in the final sub-area, an objective function corresponding to each grid in the final sub-area is calculated, and the center of the grid point with the maximum objective function is the estimated interference source position.
Furthermore, the estimated value is the positioning result of the interference source, and the positioning performance of the scheme is evaluated by the mean square error of the estimated value and the true value.
In summary, for a static interference source scenario for a GNSS receiver, the present invention provides a method and a system for positioning a multi-stage interference source based on a neural network and a direct positioning method, wherein the positioning process includes two steps: firstly, carrying out preliminary positioning by using a plurality of FNN neural networks trained in advance to obtain a smaller area with an interference source; and secondly, carrying out grid search in the primarily positioned area by using a DPD method. The invention obtains the high-precision positioning effect similar to that of the direct positioning method, greatly reduces the calculation complexity compared with the method of directly using the direct positioning method to search grids in a large range, and is beneficial to realizing the quick high-precision positioning in the on-line positioning stage.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a system scenario diagram of the present invention;
FIG. 2 is a flow chart of the online location phase of the present invention;
FIG. 3 is a schematic diagram of the sub-area division of the present invention;
FIG. 4 is a schematic diagram of a subdivision of sub-regions;
FIG. 5 is a block diagram of a FNN neural network used in the present invention;
FIG. 6 is a graph of positioning error versus signal-to-noise ratio for different positioning schemes in an embodiment of the present invention;
FIG. 7 is a graph of a positioning error with respect to a signal-to-noise ratio for different grid side lengths in a final sub-region according to an embodiment of the present invention;
figure 8 is a graph of the change in the effect of FNN neural network sub-region number determination errors on the positioning accuracy of the subsequent direct positioning method in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be understood that the terms "comprises" and/or "comprising" indicate the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides a method for positioning a multistage interference source, which comprises the steps of firstly, carrying out primary positioning by using a neural network to obtain an area with the interference source in a certain size; then, a grid search is performed using the DPD method in the area of the preliminary location. The performance close to that of a DPD method is obtained, the complexity of online calculation is reduced, the positioning precision is higher than that of a traditional two-step method, and a brand new thought for positioning an outdoor interference source by using a neural network is provided.
The invention discloses a method for positioning a multistage interference source, which comprises the following steps:
s1, modeling the interference source positioning problem in the satellite navigation system, inputting signal power received by all receivers as a neural network, using the position of the interference source as a training label, and establishing a plurality of FNN neural networks corresponding to different areas;
s101, modeling an interference source positioning problem in a satellite navigation system, and assuming that L static receivers synchronous in frequency and time exist, wherein sampling frequencies are fsTheir location is known, the location of the I-th receiver is pl＝(xl,yl) And (4) showing. The location of the stationary interferer is denoted by q ═ x, y, and its location is known. Ignoring data information and directivity in the pilot signal, the composite signal r received by the ith receiverlThe expression of (t) is:
rl(t)＝Sl(t)+Jl(t)+nl(t)
wherein the subscript l denotes the receiver number and the hybrid signal comprises the real satellite signal Sl(t), interference signal Jl(t) and noise nl(t) three moieties, nl(t) is additive white Gaussian noise and the variance is
The true satellite signal model is represented as:
wherein the content of the first and second substances,
representing the true satellite signal power received by the ith receiver, C (t) representing the spreading code, D (t) representing the navigation message data,
representing the time delay of the real satellite signal to the ith receiver, fcRepresenting the carrier frequency of the real satellite signal, fD,lWhich is indicative of the doppler shift frequency and,
indicating the initial phase of the carrier.
In practice, since the satellite is far away from the ground, the power of the interference source is much larger than that of the real satellite signal, and therefore, the influence of the real satellite signal on the positioning of the interference source can be ignored in the experiment. Parameters such as the signal form of the interference source, the carrier frequency, the number of receivers, the sampling frequency, the interference signal attenuation model, the noise variance and the like are set according to the model, and the interference signal power received by each receiver can be obtained through the interference signals sampled by the receiver under the model, as shown in fig. 1.
S102, generating training data and training labels;
referring to fig. 3, an initial region is gradually divided into two regions of different sizes, and the number i, i being 1,2 is performed on each divided sub-region, and by randomly setting interference source locations multiple times in the regions of different sizes, a plurality of sets of receiving power vectors P of receivers corresponding to one another and the numbers of the sub-regions can be obtained, so that training data sets in the regions of different sizes are obtained.
S103, training FNN neural networks corresponding to a plurality of different size areas, inputting the neural networks into receiving power vectors P of L receivers, outputting the receiving power vectors P into sub-area numbers i, i being 1 and 2, wherein the neural networks comprise input layers, output layers and hidden layers, the number of nodes and the number of layers of the hidden layers are continuously adjusted manually in the training process, and then selecting a network structure capable of accurately distinguishing the numbers of the cells where the interference sources are located according to a training result.
Considering a situation that an interference source appears near the boundary of two sub-regions, as shown in the left diagram of fig. 4, for this situation, we propose a scheme of repartitioning the sub-regions, as shown in fig. 4, after repartitioning, the interference source is no longer near the boundary of two sub-regions, and the FNN neural network can accurately distinguish the sub-regions where the interference source is located.
Meanwhile, in the training process of the FNN neural network, as the size of the area is gradually reduced, the lower limit T of the size of the sub-area is selected according to the training result, namely the accuracy of distinguishing the cell number of the interference source by the FNN neural network, and when the size of the sub-area is smaller than the lower limit T of the size of the sub-area, the accuracy of distinguishing the cell number of the interference source by the FNN neural network is reduced to be below 99.9%.
And the off-line training stage is finished, and the next on-line positioning stage is started.
S2, halving the current positioning range into two sub-regions, inputting the signal power of all receivers into the trained FNN neural network to obtain the sub-region where the interference source is located, and reducing the positioning range to the sub-region;
referring to fig. 2, in the stage of online positioning, the position of the interference source is determined according to the interference source signal sampled by the receiver, the current positioning range is divided into two sub-regions in two halves, the interference signal power of all the receivers is input into the FNN neural network corresponding to the current positioning range, the number of the sub-region where the interference source is located is obtained, the positioning range is reduced to the sub-region where the interference source is located, and then the process proceeds to step S3.
S3, judging whether the size of the current positioning range obtained in the step S2 is smaller than the lower limit T of the size of the sub-region determined in advance, otherwise, repeating the step S2, if the size of the obtained sub-region is smaller than the lower limit T determined in advance, calling the current region as a final sub-region, and performing the step S4 in the final sub-region;
s4, dividing grids in the final sub-area, carrying out Fourier transform on the sampling signals of all receivers, and then calculating a target function corresponding to each grid;
considering only the interference signal, the superimposed complex signal observed by the first receiving station at time t is
rl(t)＝blsl(t-τl)+wl(t)，0≤t≤T
Wherein s isl(t) represents an interference signal, blFor the unknown attenuation coefficient of the path,
is the propagation delay.
Sampling received signal for N points, with time interval T, then
The superimposed complex signal is written as
rl＝blsl+wl
The above formula is subjected to discrete Fourier transform to obtain a frequency domain received signal expression of
Further, suppose
Representing a zero mean, correlation matrix Rl＝σ2I, and the noise at each receiving station is independent of each other, for the noise-only hypothesis H0And an objectHypothesis H of Presence1The probability density function of which is respectively
Wherein, K0And K1Is a constant independent of the target position.
The log-likelihood ratio can be calculated as
Order to
To further derive a representation of the position estimate, the position-independent attenuation coefficient β is solved according to the generalized likelihood ratio maximum criterionlLet us order
To obtain
Substitution into
To obtain
Suppose that
Then there is
And finally obtaining:
in practice, the interference source is non-cooperative, and the form of its transmitted signal is mostly unknown, and in this case, it is possible to let:
wherein, wk＝2πk/(NT)。
The cost function L (r; p) associated with the position of the received signal and the position of the interfering source is
And calculating cost functions, namely target functions, corresponding to all grid central points.
And S5, comparing all the objective functions obtained in the step S4 to obtain the maximum value of the objective functions, and taking the grid central point corresponding to the maximum value of the objective functions as the estimated value of the position of the interference source.
Taking L (r; p) as a target, comparing all the objective functions obtained in the step S4 to obtain the maximum value of the objective functions, and taking the center point of the grid corresponding to the maximum value of the objective functions as an estimated value of the position of the interference source, namely traversing all grids to find out the position p which enables the maximum characteristic value of B to be maximum, namely the estimated value of the position of the interference source. Namely, it is
In another embodiment of the present invention, a multi-stage interference source positioning system is provided, which can be used to implement the multi-stage interference source positioning method described above.
The signal processing module is used for transmitting the signals sampled by all the receivers to a processing center, performing Fourier transform and calculating the average power of the signals received by each receiver;
the offline training module generates a plurality of groups of training data sets by randomly generating interference source positions in different ranges of regions, and then is used for training the FNN neural network corresponding to each group of data;
the online positioning module is used for respectively inputting the power vectors of the received signals of the receiver obtained by the processing center into a plurality of FNN neural networks, and gradually reducing the current positioning area through the output of the neural networks until the range of the current positioning area is smaller than the lower limit of the range of the sub-area obtained in advance;
and the direct positioning module is used for gridding the final sub-region, calculating a cost function corresponding to each grid central point through the receiving vectors and the grid central points of the receivers after Fourier transformation, and taking the central point of the grid with the maximum cost function as an estimation value of the position of the interference source.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor according to the embodiment of the present invention may be used for the operation of the method for positioning a multi-stage interference source, and includes:
modeling an interference source positioning problem in a satellite navigation system, inputting signal power received by all receivers as a neural network, using a sub-region number where an interference source is located as a training label, and establishing a plurality of FNN neural networks corresponding to different region sizes and training; halving the current positioning range into two sub-regions, inputting the signal power of all receivers into the trained FNN neural network to obtain the sub-region where the interference source is located, and reducing the current positioning range to the sub-region; judging the size of the current positioning range, and if the size of the obtained sub-region is smaller than a set lower limit T, calling the current region as a final sub-region; dividing grids in the final sub-region, carrying out Fourier transform on the sampling signals of all receivers, and then calculating a target function corresponding to each grid; and comparing all the target functions to obtain a maximum value, taking a grid central point corresponding to the maximum value of the target function as an estimation value of the position of the interference source, and obtaining the position of the interference source according to the estimation value.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for positioning a multi-stage interference source in the above embodiments; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
。
in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
The solution described hereinbefore is applied to locate a stationary interferer that is randomly present within the location area, taking into account this interferer. The initial positioning range is set to a square having a side length of 4km, and four receivers are arranged at four vertices [0,0], [0,4000], [4000,0], [4000,4000] of the square region, respectively. The interference source signal is a QPSK signal, the carrier frequency is 1.25MHz, the number of sampling points N is 200, and the sampling frequency is 5 MHz.
Comparison scheme
Comparative scheme 1: the TDOA parameters are estimated first, and then position calculation is carried out by a chan method.
Comparative scheme 2: firstly, wavelet denoising is carried out, then TDOA parameters are estimated, and finally position calculation is carried out by using a chan method.
Comparative scheme 3: and directly carrying out grid search type position calculation by using a DPD method in the whole positioning range.
According to the experimental setup, the FNN has four input nodes and one output node, the hidden layer structure of the neural network is determined as described in step S103, and according to the results of multiple experiments, the final FNN structure is set to (4,6,18,1), as shown in fig. 5.
In order to determine the final lower limit T of the side length of the sub-region, the side length of the sub-region is gradually reduced by adopting a bisection method, so that the accuracy rate 1 of distinguishing the cell numbers of the interference sources in the sub-region of the corresponding side length by the FNN neural network is obtained, in addition, a method of re-dividing the sub-region is adopted to correct the numbers of the interference sources close to the junction of the two sub-regions, so that the accuracy rate 2 is obtained, as shown in table 1, the method is effective, and the lower limit T of the side length of the sub-region is determined to be 250 m.
Table 1 accuracy of FNN network numbers for distinguishing cell numbers of interference sources under different side length sub-regions
Defining the signal-to-noise ratio as
Wherein P isJPower of interference, PSFor the power of noise, the positioning error is taken as root mean square error
The measurement (K is the experimental times), the positioning area and the receiver position are set as above, and the scheme of firstly performing wavelet denoising and then using TDOA to perform chan positioning settlement under large-scale fading and firstly performing wavelet denoising and then using TDOA under large-scale fading and Rayleigh fading are simulated respectivelyWhen the chan location settlement scheme is performed, the DPD large-scale search scheme is directly used, and the final sub-regions are respectively 200m and 400m, the graph of the variation of the location error with the signal-to-noise ratio using the scheme proposed herein shows that fig. 6 shows the result, and it can be seen from the graph that the TDOA-based contrast scheme 1 and the TDOA-based contrast scheme 2 have poor performance and depend heavily on the parameter estimation accuracy, and the performance is lower than that of the DPD-based contrast scheme 3 and the scheme proposed herein despite the wavelet denoising. While the scheme proposed herein is in terms of signal-to-noise ratio>The performance similar to that of the direct DPD method for searching is obtained in 10dB, and the calculation complexity is lower than that of the grid searching type position calculation directly in the whole positioning range by using the DPD method.
In order to explore the relationship between the DPD search performance in the final sub-region and the grid side length, the grid side length is set to 40m,20m,10m, and 5m, respectively, and in order to divide the final sub-region size to 400m, the simulation result is shown in fig. 7, it can be seen from the figure that the smaller the grid side length is, the higher the positioning accuracy is, but the higher the computation complexity is, which is in accordance with our expectation, in practice, a trade-off should be made between the positioning accuracy and the computation cost, and an appropriate grid size should be selected according to specific situations.
The influence of the first-stage cell number determination error on the positioning error precision of the next-stage direct positioning method in the final cell grid search is also considered, and the condition that the final cell is 1000,800,600,400,200 is simulated respectively, as shown in fig. 8, it can be seen from the figure that the error caused by the cell number determination error is about one half of the final cell side length, which indicates that whether the first-stage cell number determination has a great influence on the positioning precision, so the training of the neural network and the selection of the sub-region side length lower limit T are of great importance.
Finally, the time complexity of the method proposed herein and the conventional DPD method is analyzed, and the total online positioning time is equal to the online positioning time + the DPD search time in the online sub-area is equal to the DPD search time in the online sub-area. And setting L receivers in total, wherein the side length of a positioning range is a, the side length of a grid is b, the total range is G (a/b)2 grids, the time for performing L-order eigenvalue decomposition once and finding out the maximum eigenvalue is T1, the time for finding out the maximum eigenvalue of the two eigenvalues is T2, and finally dividing n minimum subregions. The computational complexity of our proposed method is ((G/n) × T1+ (G/n-1) × T2)/(G × T1+ (G-1) × T2) ≈ 1/n of the conventional DPD method. In this experiment, n is between 256-400, so the proposed method effectively reduces the computational complexity of large-area DPD search.
In summary, in the method and system for positioning a multi-stage interference source of the present invention, in the first step, a neural network is used for performing preliminary positioning to obtain an area with a certain size where the interference source exists; and secondly, carrying out grid search in the primarily positioned area by using a DPD method. Simulation results show that the method has performance close to that of a DPD method, reduces complexity of on-line calculation, has positioning accuracy higher than that of a traditional two-step method, and is a brand new idea for positioning an outdoor interference source by using a neural network.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (9)
1. A multi-stage interference source positioning method is characterized by comprising the following steps:
s1, modeling the positioning problem of the interference source in the satellite navigation system, inputting the signal power received by all receivers as a neural network, using the number of the sub-region where the interference source is located as a training label, establishing a plurality of FNN neural networks corresponding to different regions in size, and training;
s2, halving the current positioning range into two sub-regions, inputting the signal power of all receivers into the FNN neural network trained in the step S1 to obtain the sub-region where the interference source is located, and reducing the current positioning range to the sub-region;
s3, judging the size of the current positioning range obtained in the step S2, and if the size of the obtained sub-region is smaller than the set lower limit T, calling the current region as a final sub-region;
s4, dividing grids in the final sub-area determined in the step S3, carrying out Fourier transform on the sampling signals of all receivers, and then calculating a target function corresponding to each grid;
and S5, comparing all the objective functions obtained in the step S4 to obtain a maximum value, taking a grid central point corresponding to the maximum value of the objective function as an estimated value of the position of the interference source, and obtaining the position of the interference source according to the estimated value.
2. The method according to claim 1, wherein step S1 is specifically:
s101, modeling an interference source positioning problem in a satellite navigation system, wherein L static receivers synchronous in frequency and time are provided, and sampling frequencies are fsThe location of the l-th receiver is pl＝(xl,yl) Determining the mixed signal r received by the ith receiver, wherein the position of the static interference source is q ═ x, yl(t) a model determining that a real satellite signal is embedded in the hybrid signal;
s102, fixing the position of a receiver, randomly generating the position of an interference source in regions in different ranges, dividing the current region into two sub-regions, generating a plurality of groups of training data sets in different ranges, wherein the training data are the received signal strength of the receiver, and the training labels are the numbers of the sub-regions where the interference source is located;
s103, training a plurality of FNN neural networks corresponding to different range areas, and then selecting a network structure capable of accurately distinguishing the numbers of the sub-areas where the interference sources are located according to the training results.
3. The method according to claim 2, wherein in step S101, the real satellite signal model is represented as:
wherein, Pl sRepresenting the power of the true satellite signal received by the ith receiver, C (t) representing the spreading codeD (t) represents navigation message data,
representing the time delay of the real satellite signal to the ith receiver, fcRepresenting the carrier frequency of the real satellite signal, fD,lWhich is indicative of the doppler shift frequency and,
indicating the initial phase of the carrier.
4. The method according to claim 2, wherein in step S102, the initial region is gradually divided into two regions with different ranges, and the number i, i ═ 1,2 is performed on each divided sub-region, and the interference source locations are randomly set multiple times in the regions with different sizes, so as to obtain the received signal power vector P and the sub-region numbers of multiple sets of receivers in one-to-one correspondence, and obtain the training data sets in the regions with different ranges.
5. The method according to claim 2, wherein in step S103, the FNN neural network has the input of the received signal strength power vectors P of the L receivers and the output of the received signal strength power vectors P is the sub-region number i, i ═ 1,2, and includes an input layer, an output layer and an implicit layer, and the number of nodes and layers of the implicit layer are continuously adjusted manually during training.
6. The method according to claim 1, wherein in step S3, if the size of the current positioning range obtained in step S2 is larger than the set sub-region size lower limit T, step S2 is repeated.
7. The method according to claim 1, wherein in step S4, the objective function L (r; p) related to the received signal vector r and the interference source location p is:
where the maximization of B is equivalent to the maximization of the maximum eigenvalue of B, L is the number of receivers, rlIn order to superimpose the complex signals,
for the frequency domain received signal, N is the received signal sampling point, blT is the time interval for which the path attenuation coefficient is unknown.
8. The method of claim 1, wherein in step S5, the estimate of the location of the interference source is determined
Comprises the following steps:
wherein, the maximization of B is equivalent to the maximization of the maximum characteristic value of B, and p is the coordinate of the center point of each grid.
9. A multi-stage interferer locating system, comprising:
the signal processing module is used for modeling the positioning problem of the interference source in the satellite navigation system, inputting the signal power received by all the receivers as a neural network, using the number of the sub-area where the interference source is positioned as a training label, and establishing and training a plurality of FNN neural networks corresponding to different areas in size;
the offline training module equally divides the current positioning range into two sub-regions, inputs the signal power of all receivers into the FNN neural network trained by the signal processing module to obtain the sub-region where the interference source is located, and reduces the current positioning range to the sub-region;
the online positioning module is used for judging the size of the current positioning range obtained by the offline training module, if the size of the obtained subregion is smaller than a set lower limit T, the region at the moment is called a final subregion, the Fourier transform is carried out on the sampling signals of all the receivers, and then a target function corresponding to each grid is calculated;
and the direct positioning module compares all the target functions obtained by the online positioning module to obtain a maximum value, takes a grid central point corresponding to the maximum value of the target function as an estimation value of the position of the interference source, and obtains the position of the interference source according to the estimation value.
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