CN109784318A - The recognition methods of Link16 data-link signal neural network based - Google Patents
The recognition methods of Link16 data-link signal neural network based Download PDFInfo
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Abstract
The invention discloses a kind of recognition methods of Link16 data-link signal neural network based, first input measured signal；Then the measured signal of input is pre-processed, the pretreatment includes noise reduction process and PCA dimension-reduction treatment；It will be identified in the neural network determined by pretreated measured signal input network structure and parameter again；Recognition result judgement is finally carried out according to the signal classification of neural network output.Robustness of the present invention is good, good for the Link16 data-link signal identification effect under the complex situations such as complex background, noise jamming；Time complexity of the present invention is low simultaneously, and mainly time flower in building neural network, the identification used time of last signal is few.
Description
Technical field
The present invention relates to a kind of recognition methods of Link16 data-link signal, and in particular to neural network based to one kind
The recognition methods of Link16 data-link signal.
Background technique
The characteristics of modern war be information-based combined operation and it is accurate fight, unit of entering a war is numerous, be distributed in land, sea,
Empty, day broad area, war rhythm is fast, is interleaved together between ourselves and the enemy, both sides at war's sharp fight information superiority.Link16
Data-link is to be currently being widely used a kind of tactical data link, and major function is the real-time exchange tactics letter between belligerent unit
Breath makes each belligerent unit grasp Real-time Battlefield situation and transmission Command Control Order.Link16 data-link is communicated with time division multiple acess
Mode work, each member of system works asynchronously all in accordance with unified system time-base, and frequency hopping, straight is used on signal
When expanding, jumping, many interference protection measures such as channel coding and pulsed operation, there is very strong counterreconnaissance and interference free performance.
It is to extract detection signal Higher Order Cumulants, square spectrum and frequency range etc. to the existing recognition methods of Link16 signal
Feature simultaneously identified, however in practical applications, the factors such as complex background, noise jamming, scale seriously affect the property of identification
Can, the traditional recognition method based on feature can not reach ideal recognition effect, on the one hand show under low signal-to-noise ratio
The recognition effect of signal is general, and on the other hand these methods will often take a substantial amount of time in the feature extraction of signal.
The machine learning method to grow up in recent years especially neural network is grinding for Link16 data-link signal identification
Study carefully and provides new thinking.Machine learning method has been successfully applied to the multiple fields such as images match, recognition of face, and
And achieve preferable experiment effect.Therefore, a kind of identification of Link16 data-link signal neural network based how is obtained
Method allows to effectively make up the deficiency of existing recognition methods, this is with important research significance and practical value.
Summary of the invention
It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, provide that a kind of recognition effect is good, time complexity
The recognition methods of low Link16 data-link signal neural network based.
The present invention specifically uses following technical scheme:
A kind of recognition methods of Link16 data-link signal neural network based comprising following steps:
S1, input measured signal；
S2, the measured signal of step S1 input is pre-processed, the pretreatment includes at noise reduction process and PCA dimensionality reduction
Reason；
S3, will by neural network that step S2 pretreated measured signal input network structure and parameter determine into
Row identification；
S4, recognition result judgement is carried out according to the signal classification of neural network output.
Further, in step S3, the neural network of input is BP neural network, RBF neural and PNN nerve net
Network.
Further, the determination process of the structure of the neural network and parameter are as follows: BP and RBF neural include three
Layer is successively input layer, hidden layer and output layer, and PNN neural network includes four layers, is input layer, mode layer, summation layer respectively
And output layer；BP neural network hidden layer neuron number is set as 20, and training algorithm is LM algorithm, maximum frequency of training regulation
10000 times, learning rate 0.1, training precision 0.00001；RBF neural mean square error is 0.0001, radial base
Expansion rate is 2, and maximum neuron number is training sample number；The expansion rate of the radial basis function of PNN network is 1；Record
Obtain BP, RBF, PNN network structure and parameter when best identified performance.
After adopting the above technical scheme, compared with the background technology, the present invention, having the advantages that
1, the present invention is based on the identification that Link16 data-link signal is carried out under the Larger Dynamic signal-to-noise ratio of neural network, robusts
Property is good, good for the Link16 data-link signal identification effect under the complex situations such as complex background, noise jamming.
2, time complexity of the present invention is low, mainly time flower in the building of neural network, the identification of last signal
Used time is few.
Detailed description of the invention
Fig. 1 is the flow chart that the present invention is implemented；
Fig. 2 is the structural schematic diagram of BP neural network in the embodiment of the present invention；
Fig. 3 is the structural schematic diagram of PNN neural network in the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Embodiment
Fig. 1 shows implementing procedure of the invention, refering to what is shown in Fig. 1, the invention discloses a kind of neural network based
The recognition methods of Link16 data-link signal comprising following steps:
S1, input measured signal；
S2, the measured signal of step S1 input is pre-processed, the pretreatment include noise reduction process and PCA (it is main at
Analysis) dimension-reduction treatment；
S3, will by neural network that step S2 pretreated measured signal input network structure and parameter determine into
Row identification；The neural network inputted herein includes three kinds, respectively BP neural network (back propagation), RBF nerve
Network (Radical Basis Function, also referred to as radial basis function neural network) and PNN neural network
(Probabilistic Neural Network, also referred to as probabilistic neural network).
The determination process of network structure and parameter are as follows: BP neural network and RBF neural have three layers, are successively inputs
Layer, hidden layer and output layer；PNN neural network has four layers, is input layer, mode layer, summation layer and output layer respectively.BP nerve
Network concealed layer neuron number is set as 20, and training algorithm is LM algorithm, and maximum frequency of training provides 10000 times, learning rate
It is 0.1, training precision 0.00001；RBF neural mean square error is 0.0001, and the expansion rate of radial base is 2, maximum
Neuron number is training sample number；The expansion rate of the radial basis function of PNN network is 1, and record obtains best identified performance
When BP, RBF, PNN network structure and parameter.
Specifically, with reference to the structure chart of BP neural network shown in Fig. 2, the algorithm of BP neural network can first random initializtion
Every connecting line weight and biasing, then in training set each input x and output y, BP algorithm can all first carry out before to
Transmission, sample convert by hidden layer from input layer, are transmitted to output layer and obtain predicted value, then according to true value and predicted value
Between error execute weight and every layer of preference that reverse feedback updates every connecting line in neural network.Stop in no arrival
Only repeated the above process in the case where condition.Forwards algorithms are as follows:
In formula (1), wijFor the weight between node i and node j, bjThreshold value between node；
The mean square error of output with the sample that classification is marked in training set is calculated, in the way of minimization error, instead
To the weight of layer-by-layer adjustment network, until reach setting maximum number of iterations or experimental error down to setting precision until.
The mean square error formula of back-propagation algorithm is as follows:
In formula (2), djFor all results of output；
The structure of RBF neural is similar to BP neural network, the difference is that its hidden layer neuron kernel function (effect
Function) it is Gaussian function, the transformation of space reflection is carried out to input information, the action function of output layer neuron is linear letter
Number exports, the output result as entire neural network after carrying out linear weighted function to the information of hidden layer neuron output.Gauss
The form of function are as follows:
In formula (3), cjFor function center vector, σjFor width vector；
For the structure of PNN neural network referring to shown in Fig. 3, first layer is input layer, is responsible for input feature value；Mode layer,
The corresponding center of each neuron, calculates the matching degree of each mode in input feature value and training set, that is, phase
Like degree, its distance is sent into Gaussian function and obtains the output of mode layer；Summation layer, is responsible for the pattern layer units of each class of connection；
It is finally output layer, that one kind of highest scoring in output summation layer.The jth neuron of the i-th quasi-mode is determined in mode layer
Input/output relationship be defined by the formula:
In formula (4), xijFor function center vector；
The neuron number of summation layer is equal with the number of data classification, it is the nerve for belonging to same class in mode layer
The output of member is weighted and averaged according to the following formula:
In formula (5), L indicates the neuron number of the i-th class, viIndicate the output of the i-th class classification.
S4, recognition result judgement is carried out according to the signal classification of neural network output: is exported according to three kinds of neural networks
Signal classification judges whether signal is Link16 data-link signal, is finally completed entire identification process.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (3)
1. a kind of recognition methods of Link16 data-link signal neural network based, it is characterised in that: the following steps are included:
S1, input measured signal；
S2, the measured signal of step S1 input is pre-processed, the pretreatment includes noise reduction process and PCA dimension-reduction treatment；
S3, will know in the neural network determined by the pretreated measured signal input network structure of step S2 and parameter
Not；
S4, recognition result judgement is carried out according to the signal classification of neural network output.
2. a kind of recognition methods of Link16 data-link signal neural network based according to claim 1, feature
Be: in step S3, the neural network of input is BP neural network, RBF neural and PNN neural network.
3. a kind of recognition methods of Link16 data-link signal neural network based according to claim 2, feature
It is: the structure of the neural network and the determination process of parameter are as follows: BP and RBF neural are successively inputs comprising three layers
Layer, hidden layer and output layer, PNN neural network include four layers, are input layer, mode layer, summation layer and output layer respectively；BP mind
20 are set as through network concealed layer neuron number, training algorithm is LM algorithm, and maximum frequency of training provides 10000 times, study speed
Rate is 0.1, training precision 0.00001；RBF neural mean square error is 0.0001, and the expansion rate of radial base is 2, most
Large neuron number is training sample number；The expansion rate of the radial basis function of PNN network is 1；Record obtains best identified
BP, RBF, PNN network structure and parameter when energy.
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Application publication date: 20190521 |