These are challenges 16-20 identified by EPSRC and DSTL as part of their Signal Processing Call. The closing date is 29th October 2008.
CHALLENGE #16: To distinguish between man-made echo sounding pulses and those made by marine mammals.
Military sonar systems, fish-finding sonar, echo-sounders and marine mammals all use pulses for detection of objects underwater. Military intercept sonar systems need to distinguish between these different sources of noise. This is difficult because they often have similar characteristics in terms of frequency and pulse length. Furthermore distortion can be introduced during propagation through the ocean. The challenge is to identify properties of the man-made and biological signals that enable them to be separated.
CHALLENGE #17: Techniques for identification of spatial clustering in data streams.
In a linear search, where a searcher is travelling back and forth along the same track or parallel tracks in a ladder pattern, the challenge is to develop a method of signal processing such that spatial clustering of signals within the data streams may be identified. The signal tends to be weak and/or ill-defined against a background of clutter and therefore may be hard to distinguish.
CHALLENGE #18: Processing techniques for automatically identifying within a data stream spurious steps or transients of varying characteristics.
It is not uncommon for time series data to contain spurious “steps” or transients due to sensor/hardware problems or other external factors. The initial challenge is to identify these features by some form of automated algorithm. A “step” in this case is described as a rise then fall (or vice versa) in the DC level of the time series from the background level of the data. The gradient of the steps vary from case to case but generally settle at the new level in less than 0.25 seconds for data sampled at 100Hz. The duration that the data is at the increased or decreased level also varies for different features, but generally occurs for less than 60 seconds. The levels of the DC shifts also vary for different features and are typically small so they cannot be seen without zooming in on the specific regions of data. The aim is to automatically identify the whole time period of a step. A typical transient feature can be described as a spike, or a step feature with a DC level shift that does not return back to its original level, or just gradually returns back to its original level (not via a 2nd edge in the opposite direction).
CHALLENGE #19: Methods for correction or handling data drop-outs, spikes or DC shifts.
For time series data which contain data dropouts, spikes/glitches or DC shifts, identify solutions for correcting the data without compromising the statistical characteristics & spectral content of the data. It is desirable to remove or amend these sections for statistical analysis or for improving the signal to noise ratio, however the solution should have minimal impact on the rest of the data.
CHALLENGE #20: To develop signal processing that can identify and track the acoustic micro-Doppler signature from a moving target against the interference from other moving non-targets and natural environment.
How can moving targets be tracked using acoustic micro-Doppler? The difficulty in utilising the micro-Doppler signature is the resolution that is available and the duration of individual components. This in turn makes it difficult to separate the micro-Doppler from similar characteristics that come from other moving non-targets and the natural environment and to characterise the target. The micro-Doppler may come from the vibrations or movement of a structure, such as a helicopter or the swinging arms and legs of walking person, or a slowmoving swimmer. Characterisation of the micro-Doppler or differences between signatures (if available) could be used to discriminate between individuals, identify faults or even to predict failure or intent.