These are challenges 11-15 identified by EPSRC and DSTL as part of their Signal Processing Call. The closing date is 29th October 2008.

CHALLENGE #11: To develop techniques enabling electronically scanned forward looking radars on fast jets to detect slow low cross section targets in difficult clutter.

A major advantage of electronically scanned active antenna arrays is that they can form the basis of multiple function radars by combining different sub-arrays in different ways. In the case of fast jets the radars are installed in a nose cone and are forward looking. Such radars are usually used in an air to air mode or for ground target detection and targeting. However, in the case of a forward looking radar on a fast jet almost all moving targets can be viewed as ‘slow’ in comparison with the speed of the jet. Hence target discrimination against a stationary clutter background using differential Doppler frequency shifts is a difficult problem especially when the target is stealthy. The challenge is to devise ways of giving such radars the capability of detecting small slowly moving targets such as small vehicles, mobile missile launchers and unmanned aerial vehicles and cruise missiles at low altitudes against ground clutter. An approach to this problem is to use Space-Time Adaptive Processing (STAP) techniques in conjunction with the multiple channels afforded by sub-arrays of the electronically scanned active array to provide the required discrimination. However, for a forward looking radar, the clutter spectrum is range dependent in such a way as to lead to range ambiguities. The performance of a STAP technique is likely to be degraded under these circumstances. On the other hand for a forward looking radar all clutter Doppler frequencies are positive. Hence targets with negative frequencies do not compete with the clutter and this may be an advantage for a STAP technique.

CHALLENGE #12: To devise methods for SAR processing when there are zeros in the range and azimuth antenna beam patterns and/or in the transmitted chirp spectrum and/or the chirp centre frequency is not constant.

Modern Synthetic Aperture Radar (SAR) systems use active antennas comprising hundreds or thousands of active transmit/receive modules in a 2-D array. One benefit of this is that it is possible in principle to phase the signals transmitted and received by the modules in order to dynamically change the beam pattern as the platform carrying the antenna passes a point on the ground. Hence, in principle, notches can be placed in the beam pattern in both azimuth and range in order to attenuate the reception of signals emitted from a set of specific points on the ground. In addition, zero bands can be introduced into the transmitted range chirp spectrum in order to avoid transmitting in specific spectral bands. An extension of this is active ‘frequency hopping’ where the centre frequency of the transmitted band is varied in time. The use of conventional SAR processing on the signals resulting from the above techniques results in distortions (such as interference fringes, phase discontinuities, local loss of coherence etc) in the resulting images. This challenge is therefore aimed at devising practical methods for processing the signals which result from the above techniques to form SAR images with low distortion. It is expected that this work would also provide insight on the limitations on antenna beam shape and discontinuous spectra which can be used for SAR systems.

CHALLENGE #13: To develop general algorithms for distributed signal fusion in a network of sensors.

In a spatially distributed wireless network of sensors where transmission or power constraints limit the range of broadcast of signal, there may be no single node at which fusion can occur. Furthermore, no sensor can see all the transmissions from all the other nodes. In this environment sensors are forced to perform local fusion using the signals they have direct access to, in combination with the (indirectly transmitted) outputs from other sensors’ local fusion. The challenge is to develop generic algorithms which can perform fusion under these constraints.

CHALLENGE #14: To automate the assimilation and analysis of the audio and visual representation of acoustic data.

Submarine passive sonar operators use a combination of visual and audio data to detect, track and identify noise sources. The visual display shows energy as a function of bearing and time such that noise sources appear as tracks whose appearance on the screen depends on their motion relative to the sonar. The audio information is the acoustic data in the direction selected by the operator. The operational process is for the operator to listen to tracks that he can see on the display to identify the noise source. Operators also use spectral information to assess parameters when the noise source has been identified as a merchant vessel. The challenge is to produce an algorithm capable of identification and classification of merchant vessels and biological sources which exploits the information used by the human analyst.

CHALLENGE #15: To derive a beam-former with a greatly reduced computational load (factor of 10).

Submarine sonar arrays comprise several thousand hydrophone channels being sampled at over 10kHz using a 24bit analogue to digital converter. The beam-former is required to manipulate this data to provide wide azimuthal coverage using multiple beams known as fans. This is a major computational task requiring substantial processing capability. Limitation in processing power precludes simultaneous formation of beams in all the directions that are potentially available. A case for consideration is an array 64 elements square with a 10 cm separation, a maximum acoustic frequency of 7.5kHz and a sampling rate of 18kHz. The challenge is to derive an alternative approach to beamforming that is more efficient than the existing time-domain beamformers.