These are challenges 1-5 identified by EPSRC and DSTL as part of their Signal Processing Call. The closing date is 29th October 2008.
CHALLENGE #1: To produce a performance metric (e.g. minimum variance bound) for a general non-linear filtering problem that fully characterises the accuracy of the algorithm.
For a non-linear filtering problem, the complete posterior density of the state to be determined is as a function of time. The best achievable error performance is most often given in the form of a theoretical Cramer-Rao lower bound which provides a lower bound for second order (mean-squared) error only. For non-Gaussian posterior densities this is insufficient to characterise the accuracy of the algorithm. Many real-life problems are non-Gaussian and thus a more appropriate approach is clearly needed. Further, the new approach should be applicable to the particularly challenging case of a static target but with evolving signal information.
CHALLENGE #2: To develop novel new techniques which can be used for the calculation of the posterior distribution of a spiky signal distribution in space-time, given very limited processing power and a correlated noise structure.
If the posterior distribution of a signal is very spiky or 'witches hat' with few peaks it is notoriously difficult to obtain a good estimate of it, particularly when the noise level is low relative to the size of the peaks. This problem is further complicated by the fact that very often inference needs to be made in processor poor environments. Novel new approaches are sought to solve this problem using as little processing power as possible.
CHALLENGE #3: To develop a fast and efficient algorithm to simulate nonstationary, spatially and temporally correlated noise.
Consider a set of measurements made by many sensors placed in a noisy environment, the noise is both temporally and spatially correlated and has time varying statistics. Given a limited set of measurements of this environment, the challenge is to replicate this noise signal synthetically. In particular it should be possible to place (synthetic) new sensors anywhere within the environment or remove them.
CHALLENGE #4: To extract a weak signal from non-stationary, spatially and temporally correlated noise.
Consider a set of measurements made by many sensors placed in a noisy environment, the noise is both temporally and spatially correlated and has time varying statistics. Given this environment, characterised by spatial and temporal scales of correlation, the challenge is to detect the presence of a weak, stationary signal described by smaller scales of temporal and spatial correlation.
CHALLENGE #5: To detect, identify and analyse signals in the presence of similar signals.
In electronic surveillance, many current and future challenges involve detection of signals in the presence of other, similar, signals. The signal environment is extremely busy and thus the traditional process of detection of a signal buried in noise at reducing signal to noise ratio is no longer sufficient. Signals of interest may be at high SNR but need to be detected, classified, isolated and analysed as close to real time as is possible. All interfering signals are potentially signals of interest and all overlap in time and frequency.