These are challenges 21-25 identified by EPSRC and DSTL as part of their Signal Processing Call. The closing date is 29th October 2008.

CHALLENGE #21: To develop signal processing that can identify and decode periodic information in incomplete and noisy signals.

How can weak and multiple periodicity be identified in noisy signals? The difficulty is in identifying weak and/or multiple periodic information that is degraded or distorted by the background. Acoustic signals contain more information than their frequency content: temporal characteristics and periodicity. Many animals depend on reliable identification of this periodicity in the acoustic signals they perceive in their natural environment in order to navigate and communicate. Periodicity can be utilised to discriminate between man-made and natural objects or events (targets) but the background or natural environment can contain many persistent non-targets that appear target-like and confuse existing solutions.

CHALLENGE #22: To exploit environmental and contextual information in image processing to detect and identify objects observed in a variety of conditions.

How can context information be utilised to improve image classification? Clues that can help recognise objects are often found in the background or clutter. The difficulty is in incorporating this information into an image processing scheme when the correlation between object and clutter is unknown and the statistics of the environment are poorly characterised. High frequency sonars are now a prime choice for high resolution imaging of the seabed. Detectors conventionally look at threshold detection or a highlight/shadow dichotomy. This approach works extremely well for high resolution images in very benign and easy environments. Automatic classification rates in these cases are comparable with operator classification rates. However, in moderate to difficult environments, detection and classification rates fall considerably and human performance far exceeds computer aided classification. The hypothesis is that most detectors and classification schemes fail to tackle issues such as high clutter density, camouflage and sand ripples. In many cases, the human eye can easily detect and classify these targets; however, automatic target recognition (ATR) fails.

CHALLENGE #23: To identify an optimal solution to the problem of failed array modules.

How can synthetic array processing infill failed modules? The difficulty is in the different scales of correlation that exist between the signal and noise across the array. Submarine sonar systems, comprising several thousand hydrophones, have a major problem coping with failed hydrophone channels, modules and multiplexers. In one current system, nearly half of the hydrophones in the array are unavailable due to failure of power distribution pods. Repair work to recover the availability of these modules would require the submarine to return to dry dock, taking the submarine out of service for several months at a cost of several million pounds. In another case the system documentation states that processing will be stopped if two out of four modules fail. The loss of capability could cause the mission to fail or even to be abandoned. The idea of synthetic infill is to develop a method of synthesising data from the failed module - this would predict the signal of failed modules from the phase corrected signals of neighbouring modules at a time when the existing modules were in the physical position of the failed modules. While the concept is intuitively simple the implementation of the idea has some important issues such as nearfield/farfield corrections and undefined absolute array positioning which would need to be addressed.

CHALLENGE #24: To develop new signal processing that can offer robust detection and identification of objects or obstacles where the specific object to be detected is not pre-determined.

How can undefined objects be reliably identified and recognised? There is a difficulty in recognising an object that is not pre-determined or whose characteristics are changing but is known to effect a fault or some other impact such as an improvised explosive device. Detecting novel events not in the modelling or training data is important for any effective processor. Existing methods (e.g. pattern recognition, Markov models, neural networks) require either prior knowledge about various novelty conditions or models of the monitored system. New ideas borrowed from immunology could offer a more robust method that detects any unacceptable (unseen) change rather than looking for specific (known) abnormal or novel activity. This has already found application in fault detection and structural and biological monitoring. The same immune-based features e.g. positivenegative selection, partial matching, learning and memory, would apply to any other area of signal processing using system monitoring or any target detection problem where the specific object to be detected is not pre-determined.

CHALLENGE #25: To develop signal processing that is tolerant to global changes in the environment to identify and locate low observable objects whose characteristics depend on the surrounding environment.

How can low observable objects be identified in a non-stationary environment? The difficulty is that the background against which the object is to be detected is changing and characteristics of the object can depend on these changes. There may also be intermittent information available as the background obscures or occludes the object. Such a problem is faced by mine clearance operations in the surf and beach zone. This is a hostile environment that moves between aquatic and terrestrial settings; the environment includes the beach which can change between dry and wet conditions, and levels of saturated sand, due to tides and water surge, and the surf zone which extends from the beach out to very shallow water. Future operations should transition seamlessly from deep water through very shallow water (VSW) and the surf zone (SZ) to the beach zone (BZ). This supports the operational requirement to conduct rapid landing on defended beaches. Accurate identification and location would enable quicker and safer mine clearance. There is also a requirement for safe stand-off detection of (metallic and non-metallic) weapons carried on persons under clothing in a range of changing environments: school and government buildings, transportation terminals, and other public places. The difficulty remains that limited discriminatory information is available when the object is concealed.