Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
  • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
  • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
  • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
  • G01S7/415—Identification of targets based on measurements of movement associated with the target
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks

Definitions

• This invention relates to information processors and, more particularly, to a method and apparatus for classifying time varying data.
• Classifying of complex time-varying data poses a number of difficult problems for conventional information processors.
• the task of classification typically involves recognizing patterns typical of known classes from large amounts of two-dimensional data. Where the patterns to be recognized have subtle variations between the known classes, traditional classifiers often fail to correctly distinguish between the classes. This is due, in part, to the strong assumptions which must be made concerning the underlying distributions of the input data. Algorithms must then be developed to extract these features and to match known features with the input features for classification.
• classification problems include classifying time varying signals from various sources such as speech, image data, radar, sonar, etc.
• conventional information processor are generally not fault tolerant, and cannot handle certain variations in the input signals such as changes in the orientation of a visual pattern, or differences in speakers, in the case of speech recognition.
• neural nets are capable of recognizing a pattern and producing a desired output even where the input is incomplete or hidden in background noise. Also, neural nets exhibit greater robustness, or fault tolerance, than Von Neumann sequential computers because there are many more processing nodes, each with primarily local connections. Damage to a few nodes or links need not impair overall performance significantly.
• neural net models utilizing various topologies, neuron characteristics, and training, or learning, algorithms. Learning algorithms specify an internal set of weights and indicate how weights should be adapted during use, or training, to improve performance.
• some of these neural net models include the Perceptron, described in U.S. Patent No. 3,287,649 issued to F. Rosenblatt; the Hopfield Net, described in U.S. Patent Nos. 4,660,166 and 4,719,591 issued to J. Hopfield; the Hamming Net and Kohohonen self-organizing maps, described in R.
• the time-varying data is complex and involves large quantities of data
• a major problem is in developing a technique for representing the data to the neural network for processing.
• the minimum amount of data required to adequately represent classifications may involve, for example, fifty time slices of sixteen frequency bands of the doppler data.
• One way to present this data to a neural net processor would be to utilize a neural network with 800 (50 + 16) input neurons and to present each of the 800 input neurons with one sample of doppler data.
• the disadvantage of this approach is that such a large number of input neurons and the corresponding large number of total neurons and interconnections would result in a neural network that is very complex and expensive. Further, such a complex network takes a greater period of time to process information and to learn.
• a processor for classifying time-varying data with a minimum of preprocessing and requiring a minimum of algorithm and software development. It would also be desirable to provide a classification processor that is not based on explicit assumptions but instead can adapt by training to recognize patterns. It would also be desirable ⁇ to provide a means for representing time-varying data to an adaptive processor in a simplified ⁇ manner which reduces the total number of input values presented to the processor.
• an adaptive network is provided with at least N--+ 1 input neurons, where N equals the number of values ⁇ in a first domain associated with a given value in a second domain.
• the processor receives one of each of the N values in the first domain in the input neurons, and receives a single associated value from a second domain in the remaining input neuron.
• the network is trained using known training data to produce an output that serves to classify the known data.
• the network training is repeated for each value in the second domain by presenting that value together with each of the N values in the first domain as input. Once trained, the adaptive network will produce an output which classifies an unknown input when that input is from a class the adaptive network was trained to recognize.
• FIG. 1 (A-D) are representative doppler signatures from four classes of multiple moving objects
• FIG. 2 is a diagram of the adaptive network in accordance with the teachings of the present invention
• FIG. 3 is a representation of doppler data for four classes of objects
• FIG. 4 is a drawing of an additional embodiment of the present invention.
• the two-dimensional data can be derived from a variety of signal sources such as infrared, optical, radar, sonar, etc.
• the data may ' be raw, that is unprocessed, or it may be processed.
• One example of such processing is doppler procfessing, wherein the difference in frequency between an outgoing and an incoming signal is analyzed.
• doppler procfessing wherein the difference in frequency between an outgoing and an incoming signal is analyzed.
• FIGS. l(A-D) four doppler signatures from four different classes of objects are shown.
• the doppler frequency that is, the shift in frequency in the returning object or objects is represented along the horizontal axis.
• Time is represented, along the vertical axis.
• FIGS. l(A-D) each have a characteristic shape or pattern- The fact that the pattern changes from the lower portion of each figure to the upper portion, indicates changes in the detected doppler frequencies over time. This would indicate changes in the motion of multiple objects in the particular instance for each of the four classes of objects.
• FIG. 3 there is shown a representative simplified doppler signature for four different classes of objects.
• the horizontal axis represents the doppler frequency and the vertical axis represents time.
• Each horizontal line 10 in FIG. 3 represents the doppler frequencies received at a given time.
• the doppler signals in FIG. 3 are divided by means of vertical lines into four classes; a first class 12, a second class 14, a third class 15 and a fourth class 18.
• FIG. 3 represent doppler signals from four different types of objects and each have a pattern that is characteristic of that object, or objects. Even though FIG. 3 represents much more simplified doppler data than that shown in FIGS. l(A-D), representation of the four patterns in FIG. 3 to a neural network would still involve a large amount of data.
• each time slice 10 in each class is drawn from doppler frequencies from 16 frequency bins. There are 32 time slices 10 for each class. Consequently, there would be 512 individual pieces of information for each class.
• a neural network having 512 input neurons might be required to process all of the information in each class shown in FIG. 3.
• FIG. 3 the data shown in FIG. 3 may be represented as indicated by FIG. 2.
• an adaptive network 20 in accordance with the preferred embodiment of the present invention is shown.
• the adaptive network 20 utilizes a conventional neural network architecture known as a multilayer perceptron. It will be appreciated by those skilled in the art that a multilayer perceptron utilizes a layer of input neurons 22, one or more layers of inner neurons 24 and a layer of output neurons 26. Each neuron in each layer is connected to every neuron in the adjacent layer by means of synaptic connections 27, but neurons in the same layer are not typically connected to each other.
• Each neuron accepts as input either a binary or a continuous-valued input and produces an output which is some transfer function of the inputs to that neuron.
• the multilayer perceptron shown in FIG. 2 may be trained bythe conventional back propagation technique as is know ⁇ in the art. This technique is described in detail in the above-mentioned article by D. E. Rumelhart and J. L. McClelland, which is incorporated herein by reference.
• the adaptive network 20 is configured so that it has a particular number of input neurons 22 determined by the input data.
• the doppler data contains seven frequency bins. It will be appreciated that, for example, in FIG. 3 there will be 16 frequency bins, and that the number of doppler frequency bins will depend on the particular data to be analyzed, and the desired complexity of the adaptive network 20.
• the doppler frequency curve 28, like the doppler frequency curves in FIG. 3, represents one time slice of doppler data. That is, it represents the doppler frequencies received at a given time. It is preferred that the range of frequencies be normalized so that they may be represented by a signal within a range that is acceptable to the input neurons 22. For example, the doppler frequencies may be normalized to have particular relative values between zero and one. As shown in FIG. 2, seven input neurons each receive a single doppler frequency value from the doppler frequency curve 28. An eighth input neuron 30 receives a signal which is representative of the time at which the doppler frequency curve 28 was received.
• the magnitude of the signal used for the time input neuron 30 may be normalized so that the entire range of time values falls within the acceptable range for the input neuron 22.
• the doppler frequency curve 28 together with the time is transmitted to the input neurons 22 ⁇ nd 30, the adaptive network 20 will produce some output state at its output neurons 26.
• the learning algorithm known as backward error propagation may be used.
• the adaptive network 20 will be trained to produce an output corresponding to the class of the doppler frequency curve. For example, assuming that the training input is from a first class, the desired output may be to have the first two output neurons 26 produce binary ones and all the other output neurons produce binary zero values. After repeated training procedures the adaptive network 20 will adapt the weights of the synaptic connections 27 until it produces the desired output state. Once the adaptive network 20 is trained with the first doppler frequency curve 28 at a first time slice, it may then be trained for all the successive time slices. For example, the adaptive network 20 may be trained for each of the 32 doppler frequency curves 10 in FIG. 3 to produce an output indicating the first class.
• an unknown set of doppler frequency curves and times may be transmitted to the adaptive network 20. If the unknown doppler signature has the general characteristics of that of the first class, the adaptive network 20 will produce an output state for each .time slice corresponding the first class.
• the adaptive network 20 may be trained to 1 recognize multiple classes of doppler signatures. It To accomplish this, the steps used to train the -adaptive network 20 to recognize the first class of doppler frequency curves is simply repeated for the second, third and fourth classes. As shown in FIG. 2, tjhe _ adaptive network 20 may be trained to indicate the second, third and fourth classes by producing binary ones in the output neurons 26 associated with those classes as indicated in FIG. 2. The number of classes which the adaptive network 20 may be trained to recognize will depend on a number of variables such as the complexity of the doppler signals, and the ' number of neurons, layers and interconnections in the adaptive network 20.
• an adaptive network 20 in accordance with the present invention is shown. This embodiment is similar to the one shown in FIG. 2, except that it utilizes 18 input neurons 22, 24 inner neurons 24 and 26 output neurons 26. It will be appreciated that with a larger number of neurons and synaptic connections 27, time-varying data of greater complexity can be classified.
• the adaptive network 20 Once the adaptive network 20 has been trained it could be reproduced an unlimited number of times by making a copy of the adaptive network 20.
• the copies may have identical, but fixed weight values for the synaptic connections 27. In this way, mass production of adaptive networks 20 is possible without repeating the training process.

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• 1990
  • 1990-08-09 JP JP2511554A patent/JPH04501328A/ja active Pending
  • 1990-08-09 WO PCT/US1990/004487 patent/WO1991002323A1/en not_active Application Discontinuation
  • 1990-08-09 EP EP90912233A patent/EP0438569A1/en not_active Withdrawn

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