Development of Radar Pulse Compression Techniques Using Computational Intelligence Tools

Pulse compression techniques are used in radar systems to avail the benefits of large range detection capability of long duration pulse and high range resolution capability of short duration pulse. In these techniques a long duration pulse is used which is either phase or frequency modulated before transmission and the received signal is passed through a filter to accumulate the energy into a short pulse. Usually, a matched filter is used for pulse compression to achieve high signal-to-noise ratio (SNR). However, the matched filter output i.e. autocorrelation function (ACF) of a modulated signal is associated with range sidelobes along with the mainlobe. These sidelobes are unwanted outputs from the pulse compression filter and may mask a weaker target which is present nearer to a stronger target. Hence, these sidelobes affect the performance of the radar detection system. In this thesis, few investigations have been made to reduce the range sidelobes using computational intelligence techniques so as to improve the performance of radar detection system. In phase coded signals a long pulse is divided into a number of sub pulses each of which is assigned with a phase value. The phase assignment should be such that the ACF of the phase coded signal attain lower sidelobes. A multiobjective evolutionary approach is proposed to assign the phase values in the biphase code so as to achieve low sidelobes. Basically, for a particular length of code mismatch filter is preferred over matched filter to get better peak to sidelobe ratio (PSR). Recurrent neural network (RNN) and recurrent radial basis function (RRBF) structures are proposed as mismatch filters to achieve better PSR values under various noise conditions, Doppler shift and multiple target environment

One Tone, Two Ears, Three Dimensions: An investigation of qualitative echolocation strategies in synthetic bats and real robots

Institute of Perception, Action and BehaviourThe aim of the work reported in this thesis is to investigate a methodology for studying perception by building and testing robotic models of animal sensory mechanisms. Much of Artificial Intelligence studies agent perception by exploring architectures for linking (often abstract) sensors and motors so as to give rise to particular behaviour. By contrast, this work proposes that perceptual investigations should begin with a characterisation of the underlying physical laws which govern the specific interaction of a sensor (or actuator) with its environment throughout the execution of a task. Moreover, it demonstrates that, through an understanding of task-physics, problems for which architectural solutions or explanations are often proposed may be solved more simply at the sensory interface - thereby minimising subsequent computation. This approach is applied to an investigation of the acoustical cues that may be exploited by several species of tone emitting insectivorous bats (species in the families Rhinolophidae and Hipposideridae) which localise prey using systematic pinnae scanning movements. From consideration of aspects of the sound filtering performed by the external and inner ear or these bats, three target localisation mechanisms are hypothesised and tested aboard a 6 degree-of-freedom, binaural, robotic echolocation system.In the first case, it is supposed that echolocators with narrow-band call structures use pinna movement to alter the directional sensitivity of their perceptual systems in the same whay that broad-band emitting bats rely on pinnae morphology to alter acoustic directionality at different frequencies.Scanning receivers also create dynamic cues - in the form of frequency and amplitude modulations - which very systematically with target angle. The second hypothesis investigated involves the extraction of timing cues from amplitude modulated echo envelopes