CN113469217A - Unmanned automobile navigation sensor abnormity detection method based on deep learning - Google Patents
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Abstract
The invention discloses a method for detecting the abnormity of a navigation sensor of an unmanned automobile based on deep learning, the method comprises collecting original multi-channel data measured by the navigation sensor of the unmanned vehicle in normal state and N abnormal states, directly inputting the original multi-channel data into the deep learning abnormal detection model, the input data layer automatically completes data preprocessing such as multi-channel data fusion, sample generation and the like, the feature extraction layer automatically completes feature mining and feature extraction, a dimensionality reduction and parameter reduction layer automatically completes a series of operations such as dimensionality transformation and dimensionality reduction and parameter reduction, a Softmax classifier is used for completing error back propagation iterative computation in a training phase of a model, a support vector machine is used in a testing phase of the model, and the whole detection process does not need any characteristic operation depending on prior knowledge of engineers, so that people can detect network attack or physical damage and other abnormalities of the unmanned automobile navigation sensor more conveniently and quickly.
Description
Technical Field
The invention relates to an intelligent driving technology, in particular to an unmanned automobile navigation sensor abnormity detection method based on deep learning.
Background
In the last decade, an unmanned automobile as a new vehicle has become an important component of an intelligent transportation system, and the unmanned automobile can automatically complete a series of manual operations such as autonomous driving, autonomous barrier, autonomous path planning and automatic parking, so that the operation time of a driver is effectively released, and traffic accidents such as fatigue driving, drunk driving, traffic jam and violation of regulations are effectively avoided and reduced; the unmanned vehicle is used as a new intelligent vehicle, the unmanned vehicle completely depends on accurate position and path data provided by a navigation sensor during running by reading and receiving road condition data from a roadbed unit, a traffic signal, a radar base station and other vehicles in real time by relying on a large number of sensors, however, the navigation sensor of the unmanned vehicle causes navigation position data abnormity or falsification due to malicious network attack or physical faults of the navigation sensor, and great threat and challenge are brought to safe running of the unmanned vehicle, so that research on abnormal detection of the navigation sensor of the unmanned vehicle is very necessary to guarantee normal running of the unmanned vehicle.
At present, researchers in the industry and scientific research institutes mainly focus on the realization of functions and performance improvement research of unmanned vehicles, and researches on network attack detection of unmanned vehicle navigation sensors are not known, some hacker attacks aiming at computer operating systems in the prior art often adopt computer virus intrusion or abnormal detection based on Kalman filter models, but the methods need to establish complex mathematical models, the detection effect depends on the model precision to a great extent, however, the establishment of high-precision mathematical models in practice is very difficult, the abnormal detection effect is very limited, and the application objects of the methods are not unmanned vehicles, so that the methods are not convenient for the real-time diagnosis and fault detection of the navigation sensor abnormity of the unmanned vehicles.
Disclosure of Invention
The invention aims to solve the technical problem of providing an unmanned automobile navigation sensor abnormity detection method based on deep learning aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: an unmanned vehicle navigation sensor abnormity detection method based on deep learning comprises the following steps:
1) collecting position measurement data of an unmanned automobile navigation sensor, wherein the position measurement data comprises normal state data and abnormal state data, and acquiring original abnormal state data;
2) performing multi-channel data fusion on the original abnormal state data;
3) performing data processing on the abnormal state data after the multi-channel data fusion to complete the abnormal state data expansion, and obtaining an expansion data set;
4) dividing the extended data set into a training data set, a verification data set and a test data set;
5) constructing a deep learning anomaly detection model;
the deep learning anomaly detection model includes: the system comprises an input data layer, a feature extraction layer, a dimension reduction and parameter reduction layer and a logic judgment output layer; the input data layer, the feature extraction layer, the dimension reduction and parameter reduction layer and the logic judgment output layer are sequentially connected;
the input data layer is used for reading the sample data of the training data set, the verification data set and the test data set and inputting the data into the feature extraction layer;
the feature extraction layer is a one-dimensional convolutional neural network model (1 DCNN); comprises a plurality of one-dimensional convolution layers, an activation layer and a one-dimensional pooling layer which are stacked in sequence;
the dimensionality reduction parameter reduction layer consists of 1X 1 one-dimensional convolution layer and 1 one-dimensional global mean pooling layer (1 DGAP);
the logic discrimination output layer consists of a Softmax classifier and a Support Vector Machine (SVM) classifier;
6) training the deep learning anomaly detection model to obtain weight parameters of a feature extraction layer, a dimension reduction and parameter reduction layer and a logic judgment output layer of the deep learning anomaly detection model;
training a deep learning abnormity detection model by adopting the training data set, performing cross validation on the deep learning abnormity detection model by adopting the validation data set, finishing model training until the accuracy of the validation data set reaches a set target value or training iteration times, and simultaneously storing weight parameter values of each layer of the model with the highest accuracy of the validation data set during model training;
7) and finishing final test by using the trained model to obtain a final abnormal detection result.
According to the scheme, the multi-channel data fusion in the step 2) is to convert multi-channel time sequence data from a plurality of navigation sensors into a long-time sequence two-dimensional characteristic map { T } of an original data setOriginal source；
According to the scheme, in the step 2), multi-channel time sequence data from a plurality of navigation sensors are converted into a long-time sequence original data set two-dimensional feature map; the method comprises the following specific steps:
the unmanned vehicle is provided with k navigation sensors, each sensor is provided with m data channels, each channel collects P data points, and a [ n, [ k, m ] is constructed under n state types],P]Is generated from the two-dimensional characteristic map { T } of the original dataset of the multidimensional tensor matrixOriginal source(ii) a Two-dimensional characteristic map { T } of the original data setOriginal sourceThe data ordering of (a) is set as: the channel data of the 1 st navigation sensor, the channel data of the 2 nd navigation sensor, …, and the channel data of the k-th navigation sensor are sequentially arranged.
According to the scheme, the data set is expanded in the step 3) to be a two-dimensional characteristic map { T } of the original data set of the long-time sequence multidimensional tensor matrixOriginal sourceGenerating TnumTraining samples { T } of two-dimensional feature map of extended total data set of short time sequencesGeneral assembly。
According to the scheme, the data processing in the step 3) comprises data standardization processing, data truncation processing and overlapped sampling data expansion processing which are sequentially carried out.
According to the scheme, in the step 3),
data ofNormalizing, namely compressing the magnitude of P data points of each channel to a value between 0 and 1, wherein a specific magnitude compression method adopts a maximum and minimum normalization method, and the mathematical expression of the method is as follows: { X } - (X)i-xmin)/(xmax-xmin) Wherein x isiIs the ith original data point, xminIs the data point of the minimum of the P original data points, xmaxIs the maximum data point of the P original data points, and { X } is P data points after standardization;
data truncation processing is carried out on an original data set two-dimensional characteristic map { T } of a long-time sequence multidimensional tensor matrix with the length of POriginal sourceIntercepting a plurality of short-time sequence two-dimensional feature maps { t } with the length p;
the expansion processing of the overlapped sampling data adopts a fixed sliding window W for adopting a sample data expansion method based on the overlapped sampling of a sliding window methodnumTwo-dimensional characteristic map { T } of original data set of multi-dimensional tensor matrix in long time sequenceOriginal sourceAt a fixed step SnumSliding along the direction of a time axis, generating and storing data points in a sliding window as a training sample { T } of a short-time-sequence two-dimensional feature map every time a step is moved, and accordingly generating a two-dimensional feature map { T } of an original data set of a long-time-sequence multi-dimensional tensor matrixOriginal sourceGenerating TnumTraining samples { T } of two-dimensional feature map of extended total data set of short time sequencesGeneral assembly。
According to the scheme, the feature extraction layer can be further arranged to be a cyclic neural network, a deep self-coding neural network or a deep belief neural network.
According to the scheme, the deep learning anomaly detection model is set to be in two combination forms including a training stage model combination and a testing stage model combination;
in the training stage of the deep learning anomaly detection model, the combination form of the models in the training stage is as follows:
after an input data layer, a feature extraction layer and a dimensionality reduction and parameter reduction layer are connected with a Softmax classifier in a logic discrimination output layer, a 1DCNN-1DGAP-Softmax model which is composed of a one-dimensional convolution neural network layer, a 1 multiplied by 1 one-dimensional convolution layer, a one-dimensional global mean pooling layer and the Softmax classifier is formed, and the combination is used for realizing parameter training and weight learning of neurons in each layer of the model through an error back propagation algorithm;
in the testing stage of the deep learning anomaly detection model, the combination form of the testing stage model is as follows:
and an SVM classifier is connected after the dimensionality reduction and parameter reduction layer to form a 1DCNN-1DGAP-SVM model which is formed by a one-dimensional convolutional neural network layer, a 1 multiplied by 1 one-dimensional convolutional layer, a one-dimensional global mean pooling layer and the SVM classifier together, error back propagation training is not carried out by the model, input samples are directly extracted by the one-dimensional convolutional neural network layer, the 1 multiplied by 1 one-dimensional convolutional layer and the one-dimensional global mean pooling layer and then input to the SVM classifier, and the SVM classifier finishes further promotion and output of final diagnosis results.
According to the scheme, in the training stage of the deep learning anomaly detection model in the step 6), while the model is trained, samples of a verification data set of the input data layer are used for carrying out real-time verification on the diagnosis accuracy of the 1DCNN-1DGAP-Softmax model in the training process, verifying the accuracy of the model on the verification set and checking whether overfitting occurs or not;
according to the scheme, the judgment of whether the 1DCNN-1DGAP-Softmax model is over-fitted in the training process in the step 6) is set as follows: if the number of iteration rounds of model training is increased, when the accuracy on the verification set and the accuracy on the training set continuously increase, the model training is normal, the model training is continued until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes the training, and the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored; if the number of iteration rounds of model training is increased, when the accuracy on a training set continuously increases and the accuracy on a verification set does not increase, and the difference between the accuracy of the training set and the accuracy of the verification set reaches a preset value, the model is diagnosed to be over-fitted, model training is stopped, the execution of skipping is carried out, the data of the training set is input into the deep learning abnormal detection model again, the super-parameters of the deep learning abnormal detection model are revised again, if the over-fitting does not occur during the re-verification, the model parameters are reasonable, the model is trained continuously until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes training, the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored, if the over-fitting occurs during the re-verification, the training is stopped, the parameters of the deep learning abnormal detection model are revised again, and the verification is continued, and repeating the steps until the verification is successful.
The invention has the following beneficial effects: according to the method, a traditional one-dimensional convolutional neural network model structure is improved, a 1 multiplied by 1 one-dimensional convolutional layer and a designed one-dimensional global mean value pooling layer (1D-GAP) are combined to replace a 2-3 layer full-connection layer structure of the traditional 1DCNN, the training parameters of the 1DCNN are effectively reduced, the diagnosis speed of the model is improved, a support vector machine is used to replace a Softmax classifier in a test stage, the diagnosis accuracy is further improved, meanwhile, data fusion can be carried out on multi-channel data collected under a plurality of navigation sensors of an automatic unmanned vehicle, and more accurate abnormity diagnosis is carried out on network attacks or physical abnormity of the navigation sensors of the unmanned vehicle by effectively utilizing the data of the channels.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a schematic diagram of multi-sensor multi-channel data fusion according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of sample data enhancement by sliding window overlap sampling method according to an embodiment of the present invention;
FIG. 4 is an algorithm diagram of a deep learning anomaly detection model according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a deep learning unmanned vehicle navigation sensor anomaly detection scheme according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the present invention provides a method for detecting an abnormality of a navigation sensor of an unmanned vehicle based on deep learning, comprising:
s101, collecting normal state data and N-type abnormal state multi-channel position measurement data of the unmanned automobile navigation sensor;
the multi-channel position measurement data of the unmanned automobile navigation sensor comprises 1-type normal state data and N-type abnormal state data, so that N-type (N is N +1) original abnormal state data is obtained.
And S102, performing multi-channel data fusion on the original abnormal state data.
Referring to FIG. 2, multi-channel data fusion is configured to transform multi-channel time series data from multiple navigation sensors into a long time series of raw data set two-dimensional feature maps { T }Original source。
In the present embodiment, assuming that the unmanned vehicle has k navigation sensors, each sensor has m data channels, each channel collects P data points, and one [ N, [ k, m ] can be constructed under N (N ═ N +1) state types],P]Is generated from the two-dimensional characteristic map { T } of the original dataset of the multidimensional tensor matrixOriginal source。
In this embodiment, two-dimensional feature map { T } of the original data setOriginal sourceThe data ordering of (a) is set as: the 1 st channel of the 1 st navigation sensor, the 2 nd channel of the 1 st navigation sensor, …, the mth channel of the 1 st navigation sensor, the 1 st channel of the 2 nd navigation sensor, the 2 nd channel of the 2 nd navigation sensor, …, the mth channel of the 2 nd navigation sensor, …, the 1 st channel of the kth navigation sensor, the 2 nd channel of the kth navigation sensor, …, the mth channel of the kth navigation sensor.
And S103, carrying out data sample expansion through data processing.
The data processing comprises data standardization processing, data truncation processing and overlapped sampling data expansion processing.
In this embodiment, in the data normalization process, in order to compress the magnitude of P data points of each channel to a value between 0 and 1, a specific magnitude compression method adopts a maximum and minimum normalization method, and a mathematical expression of the maximum and minimum normalization method is as follows: { X } - (X)i-xmin)/(xmax-xmin) Wherein x isiIs the ith original data point, xminIs the data point of the minimum of the P original data points, xmaxIs the maximum of the P raw data points, { X } is the P data points after normalization.
In the present embodiment, the data truncation process is set to two-dimensional feature map { T } of the original data set of the long-time-series multidimensional tensor matrix of length POriginal sourceAnd (4) cutting the two-dimensional feature map into a plurality of short-time sequence two-dimensional feature maps { t } with the length p.
In this embodiment, the overlap sampling data expansion process, as shown in FIG. 3, is configured to adopt a sample data expansion method based on overlap sampling of the sliding window method, which is configured to adopt a fixed sliding window WnumTwo-dimensional characteristic map { T } of the original data set of the long-time sequence multi-dimensional tensor matrixOriginal sourceAt a fixed step SnumSliding along the direction of a time axis, generating and storing data points in a sliding window as a training sample { T } of a short-time-sequence two-dimensional feature map every time a step is moved, and accordingly generating a two-dimensional feature map { T } of an original data set of a long-time-sequence multi-dimensional tensor matrixOriginal sourceGenerating TnumTraining samples { T } of two-dimensional feature map of extended total data set of short time sequencesGeneral assembly。
In this embodiment, the method for calculating the number of new training samples generated by the sample data enhancement method of the sliding window overlap sampling method includes: t isnum＝(Inum-Wnum+1)/Snum，InumNumber of data points representing input sample, WnumRepresents the length of the sliding window, SnumRepresenting the step size of the sliding window, TnumRepresenting the number of new fault samples, the calculation result decimal point is set to round up. In FIG. 3, it is assumed that each lane contains 2000 data points, i.e., InumBy overlap sampling data truncation, 6 training samples (sliding window length W) can be obtained 2000numStep size S is 400num300, i.e., an overlap ratio of 25%), and each fault sample has a size of m,400]. As can be seen from fig. 3, the rightmost sample is incomplete, and the incomplete sample cannot constitute a valid sample and is discarded.
And S104, dividing the data set.
Specifically, training samples { T }of two-dimensional feature map of the generated extended total data setGeneral assemblyThe method comprises the following steps of dividing a training data set, a verification data set and a test data set into: setting as training samples { T }of two-dimensional feature map of the extended total data set after preprocessingGeneral assemblyRandomly selecting 30% of samples in all samples of each state category as a test data set, randomly selecting 80% of the rest 70% of samples as a training data set, and randomly selecting 20% of samples as a verification data set;
and S105, constructing a deep learning abnormity detection model.
The deep learning anomaly detection model is provided with an input data layer, a feature extraction layer, a dimension reduction and parameter reduction layer and a logic discrimination output layer.
And the input data layer is used for reading the sample data of the training data set, the verification data set and the test data set and inputting the data into the feature extraction layer.
The feature extraction layer is a one-dimensional convolutional neural network model (1DCNN), and as shown in fig. 4 and 5, includes a plurality of one-dimensional convolutional layers and one-dimensional pooling layers stacked in sequence; the dimensionality reduction parameter reduction layer is set to be composed of 1 multiplied by 1 one-dimensional convolution layer and 1 one-dimensional global mean pooling layer (1 DGAP); the logic discrimination output layer consists of a Softmax classifier and a Support Vector Machine (SVM) classifier.
In this embodiment, as shown in fig. 4 and 5, the deep learning anomaly detection model is configured to include two modules, namely a training phase model combination and a testing phase model combination, in the training phase of the deep learning anomaly detection model, a Softmax classifier is connected after the dimensionality reduction and parameter reduction layer, so as to form a 1DCNN-1DGAP-Softmax model which is composed of a one-dimensional convolutional neural network layer, a 1 × 1 one-dimensional convolutional layer, a one-dimensional global mean pooling layer, and the Softmax classifier, and the combination is used for implementing parameter training and weight learning of neurons in each layer of the model through an error back propagation algorithm; in the testing stage of the deep learning anomaly detection model, an SVM classifier is connected after the dimensionality reduction and parameter reduction layer to form a 1DCNN-1DGAP-SVM model which is formed by a one-dimensional convolutional neural network layer, a 1 × 1 one-dimensional convolutional layer, a one-dimensional global mean pooling layer and the SVM classifier, error back propagation training is not carried out by the model, input samples are directly input to the SVM after characteristics are extracted by the one-dimensional convolutional neural network layer, the 1 × 1 one-dimensional convolutional layer and the one-dimensional global mean pooling layer, and the SVM finishes further promotion and output of a final diagnosis result.
And S106, training and verifying the deep learning abnormity detection model.
Training the deep learning abnormity detection model to obtain weight parameters of a feature extraction layer, a dimension reduction and parameter reduction layer and a logic discrimination output layer of the deep learning abnormity detection model, training the deep learning abnormity detection model by adopting a training data set, and performing cross validation on the deep learning abnormity detection model by adopting a validation data set. And during model training, using samples of a verification data set of the input data layer for carrying out real-time verification on the diagnosis accuracy of the 1DCNN-1DGAP-Softmax model in the training process, verifying the accuracy of the model on the verification set and checking whether overfitting occurs.
In the training process, the judgment of whether the 1DCNN-1DGAP-Softmax model is over-fitted is set as follows: if the number of iteration rounds of model training is increased, when the accuracy on the verification set and the accuracy on the training set continuously increase, the model training is normal, the model training is continued until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes the training, and the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored; if the number of iteration rounds of model training is increased, when the accuracy on a training set continuously increases and the accuracy on a verification set does not increase, and the difference between the accuracy of the training set and the accuracy of the verification set reaches a preset value, the model is diagnosed to be over-fitted, model training is stopped, the execution of skipping is carried out, the data of the training set is input into the deep learning abnormal detection model again, the super-parameters of the deep learning abnormal detection model are revised again, if the over-fitting does not occur during the re-verification, the model parameters are reasonable, the model is trained continuously until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes training, the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored, if the over-fitting occurs during the re-verification, the training is stopped, the parameters of the deep learning abnormal detection model are revised again, and the verification is continued, and repeating the steps until the verification is successful.
And S107, completing model training and storing the optimal model parameter weight.
In the training process, when the accuracy of the verification data set reaches a set target value or training iteration times, the model training is finished, and meanwhile, the weight parameter values of each layer of the model with the highest accuracy of the verification data set during the model training are stored.
And S108, finishing the final test to obtain a final diagnosis result.
And finally, inputting the sample of the test data set or the newly acquired real-time sample of the unmanned automobile navigation sensor into a deep learning anomaly detection model for anomaly detection, and outputting a final anomaly detection result.
In the present embodiment, the navigation sensor types include conventional navigation sensors such as Global Positioning System (GPS) sensor, laser detection and measurement sensor (LIDAR).
In this embodiment, in order to further illustrate the feasibility and effectiveness of the method provided by the present invention in diagnosing the abnormality of the navigation sensor of the unmanned vehicle, the present embodiment takes GPS and LIDAR navigation sensors as examples to verify the method provided by the present invention.
In this embodiment, position data of two navigation sensors, namely, a GPS and a LIDAR, are selected as experimental data, and both the position data and the LIDAR data include data of three channels, namely, x, y and an attitude angle θ, wherein 5 attacks are added to the GPS data, and no attack is added to the LIDAR as a reference signal. The sampling frequency of the two sensors is 10Hz, and the testing time is 1000 seconds, so that each channel of each type of abnormality can obtain a one-dimensional time sequence data segment containing 10000 points. In the experiment, different abnormal severity degrees and uncertainty of network attack are fully considered, so that various abnormal grades are set, and the detailed description is as follows:
1) normal state and fixed point attack: no grade;
2) x-direction offset attack: five grades of +/-1 m, +/-3 m, +/-5 m, +/-7 m and +/-10 m are respectively set;
3) y-direction offset attack: five grades of +/-5 m, +/-10 m, +/-20 m, +/-30 m and +/-50 m are respectively set;
4) and (3) attack of slow change drifting in the X direction: five slope grades of +/-0.2, +/-0.4 m, +/-0.6 m, +/-0.8 m and +/-1.0 are respectively set;
5) y-direction slowly-varying drifting attack: attack levels of five slopes of + -0.2, + -0.4 m, + -0.6 m, + -0.8 m and + -1.0 are set, respectively. The above sampling time for each anomaly level is 100 seconds.
First, the raw data of each abnormal state is subjected to data fusion in the input layer, in this embodiment, 6 channel data are obtained by 2 sensors, so as to construct a [10000,6] two-dimensional feature map, finally, a raw data set of [6,10000,6] can be obtained under 5 abnormal states and 1 normal state, and the first 6 represents 6 state types. Secondly, sample cutting is performed after data standardization processing is performed on all original data, sampling frequencies of a GPS and a LIDAR of the unmanned vehicle in the experiment are both 10Hz, therefore, the length of each training sample is set to be 10 in data truncation, the original data are equally divided, and 1000 training samples can be obtained for each state type, as shown in table 2. And finally, randomly selecting 70% of samples of each type of abnormality as a training set, using 30% of samples of each type of abnormality as a test set, randomly selecting 20% of samples of each type of abnormality in the training set for cross validation, using the training set for model training, using the test set for detecting the diagnosis accuracy of the algorithm, and finally establishing an abnormal data set as shown in table 1.
TABLE 1 unmanned vehicle navigation sensor anomaly data set
According to the deep learning anomaly detection model framework shown in fig. 4 and 5, a diagnostic model is established in this embodiment, as shown in table 2, in the model, the one-dimensional convolutional neural network layer includes 3 one-dimensional convolutional layers, 3 activation layers, and 1 one-dimensional pooling layer, the dimensionality reduction parameter reduction layer includes a 1 × 1 one-dimensional convolutional layer and 1 one-dimensional global mean pooling layer, and the logical discrimination output layer includes a Softmax classification layer and 1 SVM classification layer. All the zero filling modes of the convolution kernel and the pooling kernel are set as Padding being equal to 'Same', and 200 rounds of training are carried out on 64 samples in each batch by adopting an Adam adaptive learning rate optimizer and a mini-batch training method.
TABLE 2 Hyperparameter List of diagnostic models
In the conventional 1DCNN algorithm, a 2-3 layer full-connection structure is adopted at the end of the model, in order to further verify the improvement effect of the algorithm provided by the invention, the embodiment tests a 1DCNN model with 3 full-connection layers, the number of hidden nodes of the 3 full-connection layers is respectively 256-128-6, the embodiment trains the conventional 1DCNN algorithm by using the same data set, the final diagnosis results obtained by the two models are shown in table 3, and the number of the training parameters of the two diagnosis models is shown in table 4.
TABLE 3 comparison of diagnostic results for two algorithms
TABLE 4 comparison of parameter values for two diagnostic algorithms
As can be seen by comparing the results of table 3 and table 4:
1) in the aspect of diagnosis accuracy, the accuracy of the traditional 1DCNN algorithm is 98.94%, while the diagnosis accuracy of the algorithm provided by the invention is improved to 99.78%, and the identification degree of the algorithm on a normal state, a fixed point attack, a Y-direction offset and a Y-direction gradual attack reaches 100%, so that the method has higher diagnosis accuracy compared with the traditional 1DCNN method.
2) In terms of model parameters, the total parameters of a traditional 1DCNN algorithm comprising 3 layers of fully-connected layers are 108134, and the total parameters of a 3-layer fully-connected network are 74886; the model parameters improved by adopting the one-dimensional global mean pooling provided by the invention are only 33488, compared with the traditional 1DCNN algorithm, the parameter quantity is reduced by 74304, which accounts for about 70% of the total parameter quantity, and the model parameters are effectively reduced.
3) In the aspect of diagnosis time, the training time and the testing time of the traditional method are 204.81 seconds and 0.372 seconds respectively, the training time and the testing time of the improved algorithm are 163.57 seconds and 0.223 seconds respectively, and particularly in the aspect of testing time, the algorithm provided by the invention effectively reduces the diagnosis time by about 43%, provides more alarm troubleshooting time for unmanned vehicle network attack detection, and has important practical significance for real-time diagnosis of network attacks.
The invention has the following beneficial effects: according to the method, a traditional one-dimensional convolutional neural network model structure is improved, firstly, a 1 multiplied by 1 one-dimensional convolutional layer and a designed one-dimensional global mean pooling layer are combined to replace a 2-3-layer full-connection layer structure of a traditional 1DCNN, the training parameters of the 1DCNN are effectively reduced, the diagnosis speed of the model is improved, then a support vector machine is adopted to replace a Softmax classifier in a test stage, the diagnosis accuracy is further improved, meanwhile, the method can automatically perform data fusion on multi-channel data collected under a plurality of navigation sensors of the unmanned vehicle, and more accurate abnormity diagnosis is performed on network attacks or physical abnormity of the navigation sensors of the unmanned vehicle by effectively utilizing the data of the plurality of channels.
The invention provides a deep learning-based abnormality detection method for an unmanned automobile navigation sensor, which automatically performs multi-channel data fusion on multi-channel position measurement data of the unmanned automobile navigation sensor, directly inputs original multi-channel measurement data of the unmanned automobile navigation sensor into a constructed deep learning abnormality detection model, automatically completes a series of operations of multi-channel data fusion, data standardization processing, data truncation, sample data enhancement, sample generation, feature extraction, dimension transformation, dimension reduction, parameter reduction and the like in an input data layer, does not need any feature operation depending on the priori knowledge of engineers in the whole detection process, has excellent self-adaptability and flexibility universality in an end-to-end algorithm structure, has higher detection speed and higher diagnosis accuracy, is more suitable for network attack or abnormal real-time diagnosis and rapid detection of physical damage of the unmanned automobile navigation sensor, the abnormity detection of the pilotless automobile navigation sensor by people is more convenient and quicker.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (10)
1. A method for detecting abnormality of a navigation sensor of an unmanned vehicle based on deep learning is characterized by comprising the following steps:
1) collecting position measurement data of an unmanned automobile navigation sensor, wherein the position measurement data comprises normal state data and abnormal state data, and acquiring original abnormal state data;
2) performing multi-channel data fusion on the original abnormal state data;
3) performing data processing on the abnormal state data after the multi-channel data fusion to complete the abnormal state data expansion, and obtaining an expansion data set;
4) dividing the extended data set into a training data set, a verification data set and a test data set;
5) constructing a deep learning anomaly detection model;
the deep learning anomaly detection model includes: the system comprises an input data layer, a feature extraction layer, a dimension reduction and parameter reduction layer and a logic judgment output layer; the input data layer, the feature extraction layer, the dimension reduction and parameter reduction layer and the logic judgment output layer are sequentially connected;
the input data layer is used for reading the sample data of the training data set, the verification data set and the test data set and inputting the data into the feature extraction layer;
the dimension reduction parameter reduction layer comprises a 1 x 1 one-dimensional convolution layer and a one-dimensional global mean value pooling layer;
the logic discrimination output layer consists of a Softmax classifier and a Support Vector Machine (SVM) classifier;
6) training the deep learning anomaly detection model to obtain weight parameters of a feature extraction layer, a dimension reduction and parameter reduction layer and a logic judgment output layer of the deep learning anomaly detection model;
training a deep learning abnormity detection model by adopting the training data set, performing cross validation on the deep learning abnormity detection model by adopting the validation data set, finishing model training until the accuracy of the validation data set reaches a set target value or training iteration times, and simultaneously storing weight parameter values of each layer of the model with the highest accuracy of the validation data set during model training;
7) and finishing final test by using the trained model to obtain a final abnormal detection result.
2. The method for detecting the abnormality of the navigation sensor of the unmanned vehicle based on the deep learning as claimed in claim 1, wherein the multi-channel data fusion in the step 2) is to convert multi-channel time series data from a plurality of navigation sensors into a long time series two-dimensional feature map { T } of a raw data setOriginal source。
3. The method for detecting the abnormality of the navigation sensor of the unmanned vehicle based on the deep learning of claim 1, wherein the step 2) converts the multi-channel time series data from a plurality of navigation sensors into a long time series two-dimensional feature map of the original data set; the method comprises the following specific steps:
the unmanned vehicle is provided with k navigation sensors, each sensor is provided with m data channels, each channel collects P data points, and a [ n, [ k, m ] is constructed under n state types],P]Is generated from the two-dimensional characteristic map { T } of the original dataset of the multidimensional tensor matrixOriginal source(ii) a Two-dimensional characteristic map { T } of the original data setOriginal sourceThe data ordering of (a) is set as: the channel data of the 1 st navigation sensor, the channel data of the 2 nd navigation sensor, …, and the channel data of the k-th navigation sensor are sequentially arranged.
4. The method for detecting the abnormality of the sensor for navigating the unmanned vehicle based on the deep learning as claimed in claim 1, wherein the step 3) is to expand the data set into a two-dimensional feature map { T } of an original data set of a long time series multidimensional tensor matrixOriginal sourceGenerating TnumTraining samples { T } of two-dimensional feature map of extended total data set of short time sequencesGeneral assembly。
5. The method for detecting the abnormality of the sensor for unmanned vehicle navigation based on deep learning of claim 1, wherein the data processing in step 3) comprises sequentially performing data standardization processing, data truncation processing and overlapped sampling data expansion processing.
6. The method for detecting the abnormality of the navigation sensor of the unmanned vehicle based on the deep learning as claimed in claim 1, wherein in the step 3), the data processing is specifically as follows:
data standardization processing is carried out, namely the magnitude of P data points of each channel is compressed to a value between 0 and 1;
data truncation processing is carried out on an original data set two-dimensional characteristic map { T } of a long-time sequence multidimensional tensor matrix with the length of POriginal sourceIntercepting a plurality of short-time sequence two-dimensional feature maps { t } with the length p;
the expansion processing of the overlapped sampling data adopts a fixed sampling data expansion method based on the overlapped sampling of a sliding window methodFixed sliding window WnumTwo-dimensional characteristic map { T } of original data set of multi-dimensional tensor matrix in long time sequenceOriginal sourceAt a fixed step SnumSliding along the direction of a time axis, generating and storing data points in a sliding window as a training sample { T } of a short-time-sequence two-dimensional feature map every time a step is moved, and accordingly generating a two-dimensional feature map { T } of an original data set of a long-time-sequence multi-dimensional tensor matrixOriginal sourceGenerating TnumTraining samples { T } of two-dimensional feature map of extended total data set of short time sequencesGeneral assembly。
7. The unmanned aerial vehicle navigation sensor abnormality detection method based on deep learning of claim 1, wherein the feature extraction layer is a one-dimensional convolutional neural network model; comprises a plurality of one-dimensional convolution layers, an activation layer and a one-dimensional pooling layer which are stacked in sequence.
8. The unmanned vehicle navigation sensor abnormality detection method based on deep learning of claim 1, wherein the deep learning abnormality detection model is set to include two combination forms of a training phase model combination and a testing phase model combination;
in the training stage of the deep learning anomaly detection model, the combination form of the models in the training stage is as follows:
after an input data layer, a feature extraction layer and a dimensionality reduction and parameter reduction layer are connected with a Softmax classifier in a logic discrimination output layer, a 1DCNN-1DGAP-Softmax model which is composed of a one-dimensional convolution neural network layer, a 1 multiplied by 1 one-dimensional convolution layer, a one-dimensional global mean pooling layer and the Softmax classifier is formed, and the combination is used for realizing parameter training and weight learning of neurons in each layer of the model through an error back propagation algorithm;
in the testing stage of the deep learning anomaly detection model, the combination form of the testing stage model is as follows:
and an SVM classifier is connected after the dimensionality reduction and parameter reduction layer to form a 1DCNN-1DGAP-SVM model which is formed by a one-dimensional convolutional neural network layer, a 1 multiplied by 1 one-dimensional convolutional layer, a one-dimensional global mean pooling layer and the SVM classifier together, error back propagation training is not carried out by the model, input samples are directly extracted by the one-dimensional convolutional neural network layer, the 1 multiplied by 1 one-dimensional convolutional layer and the one-dimensional global mean pooling layer and then input to the SVM classifier, and the SVM classifier finishes further promotion and output of final diagnosis results.
9. The deep learning based unmanned vehicle navigation sensor abnormality detection method of claim 8, wherein in the step 6), in the training phase of the deep learning abnormality detection model, during model training, samples of the verification dataset of the input data layer are used for performing real-time verification on the diagnosis accuracy of the 1DCNN-1DGAP-Softmax model during training, verifying the accuracy of the model on the verification set and checking whether overfitting occurs.
10. The method for detecting the abnormality of the sensor for the unmanned vehicle navigation based on the deep learning of claim 9, wherein the step 6) of determining whether the 1DCNN-1DGAP-Softmax model is over-fitted during the training process is performed by: if the number of iteration rounds of model training is increased, when the accuracy on the verification set and the accuracy on the training set continuously increase, the model training is normal, the model training is continued until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes the training, and the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored; if the number of iteration rounds of model training is increased, when the accuracy on a training set continuously increases and the accuracy on a verification set does not increase, and the difference between the accuracy of the training set and the accuracy of the verification set reaches a preset value, the model is diagnosed to be over-fitted, model training is stopped, the execution of skipping is carried out, the data of the training set is input into the deep learning abnormal detection model again, the super-parameters of the deep learning abnormal detection model are revised again, if the over-fitting does not occur during the re-verification, the model parameters are reasonable, the model is trained continuously until the accuracy on the training set and the accuracy on the verification set reach the set number of iteration rounds, the model finishes training, the weight parameter values of each layer of the model with the highest accuracy of the verification data set in the training process are stored, if the over-fitting occurs during the re-verification, the training is stopped, the parameters of the deep learning abnormal detection model are revised again, and the verification is continued, and repeating the steps until the verification is successful.
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