The successful detection and discrimination of mines is very difficult in areas of high soil mineralization. In these areas,
the soil can make a significant contribution to the received signal that causes false detections or masks the true mine
response. To address this problem, Minelab has developed a continuous wave (CW) multi-frequency digital detector
(MFDD). It transmits four frequencies (between 1 kHz and 45 kHz) and each has a high dynamic range that approaches
120 dB. The mineralized soil with high magnetic susceptibility affects the characteristics of the sensor-head, in particular
the inductance of the transmitting and receiving windings. These in turn affect the front-end electronics and measuring
circuits and can lead to excessive ground noise that makes detection difficult. Minelab has modeled the effect that the
soil has on the sensor-head and developed methods to monitor these effects. By having a well calibrated detector, which
is demonstrated by the tight agreement of raw ground signals with theoretical ground models, the tasks of ground
balance and discrimination become much more reliable and robust.
Several motion detection schemes are considered and their responses
to noisy signals investigated. The detection schemes include the
Reichardt correlation detector, shunting inhibition neuron and the
Horridge template model. These schemes are directionally selective
and independent to the change in contrast. They essentially function
by using spatial information and comparing it at successive time
intervals. Using the detectors, the phenomenon of stochastic
resonance (SR) is employed. SR is characterised by an improvement in
response to a nonlinear system when noise is added to the input
signal. Two types of SR are also considered, namely, subthreshold
aperiodic and suprathreshold. Using stochastic resonance, the
schemes are subjected to signals in an effort to improve the
detectors responses with the addition of noise. We found that
although added noise only further degrades the detectors response,
an improvement can be gained by using some of the techniques from
suprathreshold stochastic resonance.
In certain dynamic systems, the addition of nose can assist the detection of a signal and not degrade it as normally expected. This is possible via a phenomenon termed stochastic resonance (SR). The response of a nonlinear system to a sub-threshold periodic input signal is optimal for some non-zero value of noise intensity. Using the signal-to-noise ratio (SNR) we can characterize SR - as the noise increases the SNR rises sharply, which is followed by a gradual decrease. We investigate the SR phenomenon in several circuits and numerical simulations. In particular, the effect that the system linearity has on the amount of gain introduced by SR and the effect of varying the input signal strength. We demonstrate, for a thresholding system, as much as a 20 dB improvement in SNR, which may be increased by further investigation. Although SR occurs in many disciplines, the sinusoidal signal itself is not information bearing. To greatly enhance the practical applications of SR, we require operation with an aperiodic broadband signal. Hence, we introduce aperiodic stochastic resonance (ASR) where noise can enhance the response of a nonlinear system to a weak aperiodic signal. As the input signal is aperiodic, an alternative quantitative measure is required rather than the SNR used with periodic signals. We can characterize ASR by the use of cross-correlation-based- measures. Using this measure, the ASR in a simple threshold system and in a FitzHugh-Nagumo neuronal model are compared using numerical simulations. Using both weak periodic and aperiodic signal we show that the response of a nonlinear system is enhanced, regardless of the signal.
Passive millimeter-wave detection is advantageous for detection of objects obscured by rain, steam or other aerosols. This coupled together with collision avoidance techniques, based on biologically inspired insect vision models, promises compact low-cost solutions that do not require hardware-intensive image processing. This paper examines a number of possible future directions by identifying trade-offs between different integrated antenna strategies. Signal processing issues are also briefly discussed.