The DPS5000 is a vaporware detector ( well some bits of it have solidified 
) It uses a sinc(x) = sin(x) / x pulse waveform which produces a wideband frequency pulse where each frequency component is of equal amplitude and coherant phase between nearly DC and some higher frequency dependant on what numbers are plugged into the DSP algorithm. ( eg 48 Khz on my test system )
There are some scope shots at geotech1.com at DPS5000
... below is a functional block of the DPS5000 ... one of the tricky bits I have been pondering is how to process up to thousands of data points in a single sample ( each data value is a complex variable ie Amplitude and Phase ) and each can be processed as an elementary metal detector. This equates to thousands of parallel descrimination points.
The question is how to produce one output signal that indicates a good target or not.
After some preliminary testing of code on the back end ( where the Phase and Amplitude is pumped out of the processing engine ) I believe I have found an answer .....
A neural network .... using the same principle as the meatware on your shoulders ... a neural network can not only process thousands of parallel data inputs at the same time but it can also adapt ( or learn ) .... This would mean that from the thousands of amplitude / phase inputs to the NN there would be say several outputs ranging from ( for example ) dig, dont dig, hotrock, ferrite, dont know, run away etc.
Additionally the user could 'train' the detection system to recognise new 'unknown' targets if they are worth digging ( like coins ) or avoiding (like landmines )
Historically neural networks have very high performance for pattern recognition and the ability to 'lift' a wanted pattern from high levels of interferance ( eg ground clutter ). Most NNs can achieve hit ratios above 90% in this type of application .. ( ie pattern recognition ).
[attachment 137668 DPS5000_functional.jpg]
) It uses a sinc(x) = sin(x) / x pulse waveform which produces a wideband frequency pulse where each frequency component is of equal amplitude and coherant phase between nearly DC and some higher frequency dependant on what numbers are plugged into the DSP algorithm. ( eg 48 Khz on my test system )There are some scope shots at geotech1.com at DPS5000
... below is a functional block of the DPS5000 ... one of the tricky bits I have been pondering is how to process up to thousands of data points in a single sample ( each data value is a complex variable ie Amplitude and Phase ) and each can be processed as an elementary metal detector. This equates to thousands of parallel descrimination points.
The question is how to produce one output signal that indicates a good target or not.
After some preliminary testing of code on the back end ( where the Phase and Amplitude is pumped out of the processing engine ) I believe I have found an answer .....
A neural network .... using the same principle as the meatware on your shoulders ... a neural network can not only process thousands of parallel data inputs at the same time but it can also adapt ( or learn ) .... This would mean that from the thousands of amplitude / phase inputs to the NN there would be say several outputs ranging from ( for example ) dig, dont dig, hotrock, ferrite, dont know, run away etc.
Additionally the user could 'train' the detection system to recognise new 'unknown' targets if they are worth digging ( like coins ) or avoiding (like landmines )
Historically neural networks have very high performance for pattern recognition and the ability to 'lift' a wanted pattern from high levels of interferance ( eg ground clutter ). Most NNs can achieve hit ratios above 90% in this type of application .. ( ie pattern recognition ).
[attachment 137668 DPS5000_functional.jpg]