Method and apparatus for identifying behavior of target, and radar system

1. A method comprising:

receiving a radar echo signal from a target;

processing the radar echo signal to obtain time-frequency domain data;

processing the time-frequency domain data to obtain signal attribute feature data representing a first feature of a radar echo signal attribute, wherein the signal attribute feature data comprises one or more of a maximum frequency value corresponding to the time-frequency domain data, a standard deviation of amplitude value corresponding to the time-frequency domain data, a mean absolute error of amplitude value corresponding to the time-frequency domain data, an amplitude value quartile, an amplitude value interquartile range, and a spectral entropy;

processing the time-frequency domain data to obtain linear prediction coefficient (LPC) feature data representing a second feature of the radar echo signal, wherein processing the time-frequency domain data comprises:

re-arranging the time-frequency domain data to obtain a one-dimensional row vector; and

inputting the re-arranged time-frequency domain data into an LPC function to obtain the LPC feature data;

inputting the signal attribute feature data and the LPC feature data into a behavior identification model; and

obtaining, from an output of the behavior identification model, behavior information of the target.

2. The method of claim 1 , wherein before processing the time-frequency domain data, the method further comprises performing a dimension reduction on the time-frequency domain data.

3. The method of claim 2 , further comprising performing the dimension reduction on the time-frequency domain data based on a principal component analysis (PCA) algorithm.

4. The method of claim 1 , wherein the behavior identification model is a support-vector machines (SVM) classifier model.

5. The method of claim 1 , wherein the behavior identification model is a neural network model.

6. An apparatus comprising:

a receiver configured to receive a radar echo signal from a target;

a processor coupled to the receiver and configured to:

process the radar echo signal to obtain time-frequency domain data;

process the time-frequency domain data to obtain signal attribute feature data representing a first feature of a radar echo signal attribute, wherein the signal attribute feature data comprises one or more of a maximum frequency value corresponding to the time-frequency domain data, a standard deviation of amplitude value corresponding to the time-frequency domain data, a mean absolute error of amplitude value corresponding to the time-frequency domain data, an amplitude value quartile, an amplitude value interquartile range, and a spectral entropy;

process the time-frequency domain data to obtain linear prediction coefficient (LPC) feature data representing a second feature of the radar echo signal, wherein in a manner to process the time-frequency domain data, the processor is further configured to:

re-arrange the time-frequency domain data to obtain a one-dimensional row vector; and

input the re-arranged time-frequency domain data into an LPC function to obtain the LPC feature data;

input the signal attribute feature data and the LPC feature data into a behavior identification model; and

obtain, from an output of the behavior identification model, behavior information of the target.

7. The apparatus of claim 6 , wherein the processor is further configured to perform a dimension reduction on the time-frequency domain data.

8. The apparatus of claim 7 , wherein the processor is further configured to perform the dimension reduction on the time-frequency domain data based on a principal component analysis (PCA) algorithm.

9. The apparatus of claim 6 , wherein the behavior identification model is a support-vector machines (SVMs) classifier model.

10. The apparatus of claim 6 , wherein the behavior identification model is a neural network model.

11. A radar system comprising:

a signal transmitting apparatus configured to transmit a radar signal;

a signal receiving apparatus configured to receive a radar echo signal reflected from a target when the radar signal contacts the target; and

a signal processing apparatus coupled to the signal transmitting apparatus and the signal receiving apparatus and configured to:

process the radar echo signal to obtain time-frequency domain data;

process the time-frequency domain data to obtain signal attribute feature data representing a first feature of a radar echo signal attribute, wherein the signal attribute feature data comprises one or more of a maximum frequency value corresponding to the time-frequency domain data, a standard deviation of amplitude value corresponding to the time-frequency domain data, a mean absolute error of amplitude value corresponding to the time-frequency domain data, an amplitude value quartile, an amplitude value interquartile range, and a spectral entropy;

process the time-frequency domain data to obtain linear prediction coefficient (LPC) feature data representing a second feature of the radar echo signal, wherein in a manner to process the time-frequency domain data, the processor is further configured to:

re-arrange the time-frequency domain data to obtain a one-dimensional row vector; and

input the re-arranged time-frequency domain data into an LPC function to obtain the LPC feature data;

input the signal attribute feature data and the LPC feature data into a behavior identification model; and

obtain, from an output of the behavior identification model, behavior information of the target.

12. The radar system of claim 11 , wherein the signal processing apparatus is further configured to perform a dimension reduction on the time-frequency domain data based on a principal component analysis (PCA) algorithm.

13. The radar system of claim 11 , wherein the behavior identification model is a support-vector machines (SVM) classifier model.

14. The radar system of claim 11 , wherein the behavior identification model is a neural network model.

15. The radar system of claim 11 , wherein the signal processing apparatus is further configured to perform a dimension reduction on the time-frequency domain data.

16. A computer program product comprising computer-executable instructions stored on a non-transitory computer-readable storage medium that, when executed by a processor, cause an apparatus to:

receive a radar echo signal from a target;

process the radar echo signal to obtain time-frequency domain data;

process the time-frequency domain data to obtain signal attribute feature data representing a first feature of a radar echo signal attribute, wherein the signal attribute feature data comprises one or more of a maximum frequency value corresponding to the time-frequency domain data, a standard deviation of amplitude value corresponding to the time-frequency domain data, a mean absolute error of amplitude value corresponding to the time-frequency domain data, an amplitude value quartile, an amplitude value interquartile range, and a spectral entropy;

process the time-frequency domain data to obtain linear prediction coefficient (LPC) feature data representing a second feature of the radar echo signal, wherein in a manner to process the time-frequency domain data, the processor is further configured to:

re-arrange the time-frequency domain data to obtain a one-dimensional row vector; and

input the re-arranged time-frequency domain data into an LPC function to obtain the LPC feature data;

input the signal attribute feature data and the LPC feature data into a behavior identification model; and

obtain behavior information of the target from an output of the behavior identification model.

17. The computer program product of claim 16 , wherein before processing the time-frequency domain data, the computer-executable instructions further cause the apparatus to perform a dimension reduction on the time-frequency domain data.

18. The computer program product of claim 17 , wherein the computer-executable instructions further cause the apparatus to perform, based on a principal component analysis (PCA) algorithm, the dimension reduction on the time-frequency domain data.

19. The computer program product of claim 16 , wherein the behavior identification model is a support-vector machines (SVMs) classifier model.

20. The computer program product of claim 16 , wherein the behavior identification model is a neural network model.