Land mine detector

1. A forwarding looking ground penetrating mine detection apparatus, comprising:

a movable vehicle;

a radiation source comprising dual transmitting antennas mounted on said movable vehicle for irradiating a sample of ground suspected of containing at least one mine with a plurality of frequency swept ground penetrating radar signals, each of said radar signals having a discrete frequency, the discrete frequencies being evenly spaced over a predetermined frequency range; and

a detector comprising a plurality of evenly spaced-apart receiving antennas mounted on said movable vehicle for receiving target signals backscattered from said ground responsive to said radar signal, said detector including a time-frequency analyzer, said time-frequency analyzer transforming said target signals into a time-frequency image representation (TFR);

wherein said transmitting antennas operate sequentially in irradiating the sample, and wherein said receiving antennas operate simultaneously in receiving the target signals.

2. The apparatus of claim 1 , wherein said detector further comprises a wavelet packet transformer (WPT), said WPT extracting time-frequency localized information from said TFR.

3. The apparatus of claim 2 , wherein said time-frequency localized information comprises a feature set constructed from a wavelet table provided by said WPT, said feature set having a first data dimensionality, further comprising a data dimensionality reducer, said dimensionality reducer selecting features to form a feature subset from said feature set, said feature subset having a reduced dimensionality as compared to said first dimensionality.

4. The apparatus of claim 3 , wherein said dimensionality reducer implements a sequential forward selector (SFS) with a cost function to compile said feature subset.

5. The apparatus of claim 4 , wherein said cost function comprises a linear discriminant analysis (LDA) cost function and said SFS comprises sequential floating forward selector.

6. The apparatus of claim 5 , wherein said data dimensionality reducer selects said feature subset based on reference to a training data set.

7. The apparatus of claim 4 , further comprising a multilayer neural network classifier based on said feature subset.

8. The apparatus of claim 7 , wherein said neural network is a trained network.

9. The apparatus of claim 8 , wherein said training comprises minimizing a cross entropy function.

10. The apparatus of claim 7 , wherein said neural network implements a boosting based adaptive feature selector.

11. The apparatus of claim 1 , wherein said time-frequency analyzer implements a Choi-Williams Distribution (CWD).

12. A mine detection method, comprising the steps of:

irradiating a sample of ground suspected of containing at least one mine with a plurality of frequency swept ground penetrating radar signals using dual transmitting antennas mounted on a movable vehicle, said transmitting antennas being operated sequentially to irradiate the sample with frequency swept ground penetrating radar signals, each of said radar signals having one of a plurality of discrete frequencies evenly spaced over a predetermined frequency range;

receiving at a plurality of evenly spaced-apart receiving antennas mounted on said movable vehicle a signal backscattered from a ground penetrating swept radar radiated ground sample;

generating a time-frequency representation based on said backscattered signal, and

analyzing said time-frequency representation to indicate whether a mine is in said ground.

13. The method of claim 12 , further comprising the step of wavelet packet transforming to extract time-frequency localized information from said TFR, said time-frequency localized information comprising a feature set constructed from a wavelet table provided by said wavelet packet transforming.

14. The method of claim 12 , further comprising the step of data dimensionality reducing said feature set to provide a reduced dimensionality feature subset.

15. The method of claim 14 , wherein said dimensionality reducing comprises a sequential forward selector (SFS) with a cost function.

16. The method claim 15 , wherein said cost function comprises a linear discriminant analysis (LDA) cost function and said SFS comprises sequential floating forward selector.

17. The method of claim 16 , further comprising extracting time-frequency localized information from said TFR based on reference to a training data set.

18. The method of claim 14 , further comprising the step of configuring a multilayer neural network classifier based on said feature subset.

19. The method of claim 18 , further comprising the step of training said neural network to a surrounding environment adjacent to said ground.

20. The method of claim 19 , wherein said neural network implements a boosting based adaptive feature selector.

21. The apparatus of claim 1 , wherein said radar signals operate over 1024 discrete frequencies and wherein the discrete frequencies are evenly spaced at 2.5 MHz intervals over a frequency range from 442.5 MHz to 3 GHz.

22. The apparatus of claim 21 , wherein the apparatus irradiates a sample and receives backscattered signals at 2 meter intervals as the moveable vehicle advances.

23. The apparatus of claim 22 , further comprising a global positioning system to measure the location of the apparatus at each 2 meter interval.