Patent Publication Number: US-11651290-B2

Title: Data management and bootstrapping processing for machine learning and classification development

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to radiofrequency (RF) systems and, more particularly, to implementing machine learning in RF systems using labeled and unlabeled data sets. 
     2. Description of the Related Art 
     Machine learning in the radiofrequency (RF) field is difficult to perform due to the lack of sufficiently robust labeled data sets that can be used to train machine learning algorithms. In the absence of sufficient training data, the results of machine learning are prone to error and cannot be reliably used for the processing and interpretation of new data. 
     Accordingly, there is a need in the art for an approach that can allow small data sets to be developed so that machine learning can be used for classification and autonomous mission support. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention is a system for developing machine learning for use in the radiofrequency domain that can produce a robust set of training data for machine learning from a small set of training data that is bootstrapped with electromagnetic environment data. The system includes a database containing a first labelled data set and data reflecting a real electromagnetic environment and a processor programmed to retrieve the labelled data and the data reflecting the real electromagnetic environment from the database. The processor is further programmed to prepare a raw signal set from the labeled data and to separately process the raw signal set for any electromagnetic environment and interference signals as well as for a primary signal by applying the data reflecting the real electromagnetic environment to generate a second labeled data set that is larger than the first labelled data set. The processor is also programmed to perform a summation of any electromagnetic environment and interference signals and any primary signal of the raw signal set and then a feature extraction of the summed electromagnetic environment and interference signals and primary signal. The processor is also programmed to label the feature extraction from the raw signal sets. The processor is also programmed use the labelled feature extraction as training data for machine language classification. The system further includes radiofrequency hardware programmed to use the trained machine language classification to interpret new data. The processor is also programmed to perform the feature extraction using MFRF VM. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
       The present invention will be more fully understood and appreciated by reading the following Detailed Description in conjunction with the accompanying drawings, in which: 
         FIG.  1    is a schematic of a system for characterizing RF data for machine language applications according to the present invention; 
         FIG.  2    is a schematic of a system for characterizing RF data along with real electromagnetic emissions for machine language applications according to the present invention; 
         FIG.  3    is a schematic of a system for using characterizing RF data along with the real electromagnetic environment for machine language based programming of RF hardware according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to the figures, wherein like numerals refer to like parts throughout, there is a seen in  FIG.  1    a schematic of a machine learning RF system  10  according to the present invention. More specifically, RF system  10  builds a sufficiently robust set of training data using both labelled and unlabeled data to develop machine learning for RF devices in support of various RF functions such as multifunction radiofrequency and cognitive radiofrequency classifications. RF system  10  involves the collection and labeling of device characterization data (DCD), bootstrapping of the instantiations of the DCD by applying channel interference, adding combinations of other labeled and unlabeled interfering signals, and real electromagnetic environment (EME) conditions to create a large data labeled data set, and applying supervised learning to train classifiers that are robust to channel interference and EME conditions. RF system  10  thus allows for the use of real EME in training and the machine learning can be performed through feature space domains and through raw In-Phase/Quadrature (I/Q) processing. RF system  10  further includes replay and hardware in the loop evaluation and performance estimation. 
     Referring to  FIG.  1   , RF system  10  comprises a data library  12  containing labeled data  14 , unlabeled data  16  (e.g., EME and collections of unknown but “not-of-interest” environments are an example of a type of unlabeled data that would be used. Unlabeled data can also be applied for applying unsupervised learning techniques for identification of relevant feature manifolds), and real electromagnetic environment (EME). Labeled data  14  is populated from a bandpass device via I/Q collection  20  using conventional RF data processing software such as X-Midas. Unlabeled data  16  is obtained from other data sources  22 . Real EME  18  is obtained from EME in the real environment  24 . Real EME data  18  can come from anywhere. It could be collected in urban environments, or across a large diversity in environments to assist in generalizing/teaching the algorithms to be robust to environmental effects. 
     As seen in  FIG.  1   , characterization data may be retrieved by and then processed by a high performance computing cluster (HPC) or in the cloud  40 . Labeled data  14  is used to prepare a series of raw signal sets  42 ; it is data manipulation consisting of extracting labeled signals for growth and recombination in subsequent processing stages. The raw signal sets  42  are separately processed to determine channel, noise, carrier frequency (fc), and Doppler shift/spread (f Doppler ) for EME and interference signals  44  as well as the channel, noise, carrier frequency (fc), and Doppler shift/spread (f Doppler ) for the primary signal  46 . The results of the EME and interference signal processing  44  and the primary signal processing  46  are then summed  48  and subjected to feature extraction  52 , which include direct-to-deep learning extraction at the raw I/Q sample level. The feature extraction data  52  is then compared to the data labels through standard machine learning techniques and classification training procedures. 
     As explained above, the smaller set of initial labelled data may not be sufficient for robust machine learning development, so the present invention provides for data growth and by combination and bootstrapping of the labelled data with real EME and other leveled data. Referring to  FIG.  2   , the approach of  FIG.  1    may be performed using labeled data  16  from bandpass device I/Q collection (xMidas)  20  as well as EME data  18  from the real environment  24 . The real data is, in essence, combined to try to obscure the known signals and then used to confirm that the known signals were properly identified; it is also to train the classification networks to learn to de-interleave and classify signals that are simultaneously present. In this scenario, real EME data  18  is used in EME and interference signal processing  44  as well as in primary signal processing  46 . The resulting feature extraction  50  is used for labeling of raw signal sets  52  for machine language classification and training  54 . 
     Referring to  FIG.  3   , the machine language classification and training is used to program the relevant hardware  60 , such as an RF transceiver (uXCVR), using applicable programming techniques such as a General Forwarding Element (GFE) and Classifier  62 . 
     As described above, training data is generated through bootstrapping labeled data with itself and with other random processes to generate a large volume of data. This approach involves the use of combinations of signals at differing fc and S/(ΣI+N) levels. Channel processes are applied to S/(ΣI+N) include multipath conditions, which includes time delay spread and frequency selective fading effects, an infinite number of channel conditions, and Doppler shift/spread, (Doppler. Generalized unlabeled EME thus can be mixed with labeled data. In the present invention, Device I/Q Samples will need to capture all possible modalities in the waveform. 
     As described above, the present invention may be a system, a method, and/or a computer program associated therewith and is described herein with reference to flowcharts and block diagrams of methods and systems. The flowchart and block diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer programs of the present invention. It should be understood that each block of the flowcharts and block diagrams can be implemented by computer readable program instructions in software, firmware, or dedicated analog or digital circuits. These computer readable program instructions may be implemented on the processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine that implements a part or all of any of the blocks in the flowcharts and block diagrams. Each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that each block of the block diagrams and flowchart illustrations, or combinations of blocks in the block diagrams and flowcharts, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.