216 research outputs found

    Bio-inspired processing of radar target echoes

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    Echolocating bats have evolved the ability to detect, resolve and discriminate targets in highly challenging environments using biological sonar. The way bats process signals in the receiving auditory system is not the same as that of radar and sonar and hence investigating differences and similarities might provide useful lessons to improve synthetic sensors. The Spectrogram Correlation And Transformation (SCAT) receiver is an existing model of the bat auditory system that takes into account the physiology and the neural organisation of bats that emit broadband signals. In this study, the authors present a baseband receiver equivalent to the SCAT that allows an analysis of target echoes at baseband. The baseband SCAT (BSCT) is used to investigate the output of the bat-auditory model for two closely spaced scatterers and to carry out an analysis of range resolution performance and a comparison with the conventional matched filter. Results firstly show that the BSCT provides improved resolution performance. It is then demonstrated that the output of the BSCT can be obtained with an equivalent matched-filter based receiver. The results are verified with a set of laboratory experiments at radio frequencies in a high signal-to-noise ratio

    Biologically inspired processing of radar and sonar target echoes

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    Modern radar and sonar systems rely on active sensing to accomplish a variety of tasks, including detection and classification of targets, accurate localization and tracking, autonomous navigation and collision avoidance. Bats have relied on active sensing for over 50 million years and their echolocation system provides remarkable perceptual and navigational performance that are of envy to synthetic systems. The aim of this study is to investigate the mechanisms bats use to process echo acoustic signals and investigate if there are lessons that can be learned and ultimately applied to radar systems. The basic principles of the bat auditory system processing are studied and applied to radio frequencies. A baseband derivative of the Spectrogram Correlation and Transformation (SCAT) model of the bat auditory system, called Baseband SCAT (BSCT), has been developed. The BSCT receiver is designed for processing radio-frequency signals and to allow an analytical treatment of the expected performance. Simulations and experiments have been carried out to confirm that the outputs of interest of both models are “equivalent”. The response of the BSCT to two closely spaced targets is studied and it is shown that the problem of measuring the relative distance between two targets is converted to a problem of measuring the range to a single target. Nearly double improvement in the resolution between two close scatterers is achieved with respect to the matched filter. The robustness of the algorithm has been demonstrated through laboratory measurements using ultrasound and radio frequencies (RF). Pairs of spheres, flat plates and vertical rods were used as targets to represent two main reflectors

    Applications of FM Noise Radar Waveforms: Spatial Modulation and Polarization Diversity

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    Two possible radar application spaces are explored through the exploitation of highdimensional nonrecurrent FM-noise waveforms. The first involving a simultaneous dual-polarized emission scheme that provides good separability with respect to co- and cross-polarized terms and the second mimicking the passive actuation of the human eye with a MIMO emission. A waveform optimization scheme denoted as pseudorandom optimized (PRO) FM has been shown to generate FM-noise radar waveforms that are amenable to high power transmitters. Each pulse is generated and optimized independently and possesses a non-repeating FM-noise modulation structure. Because of this the range sidelobes of each pulse are unique and thus are effectively suppressed given enough coherent integration. The PRO-FM waveform generation scheme is used to create two independent sets of FM-noise waveforms to be incorporated into a simultaneous dual-polarized emission; whereby two independent PRO-FM waveforms will be transmitted simultaneously from orthogonal polarization channels. This effectively creates a polarization diverse emission. The random nature of these waveforms also reduce cross-correlation effects that occur during simultaneous transmission on both channels. This formulation is evaluated using experimental open-air measurements to demonstrate the effectiveness of this high-dimensional emission. This research aims to build upon previous work that has demonstrated the ability to mimic fixational eye movements (FEM) employed by the human eye. To implement FEM on a radar system a MIMO capable digital array must be utilized in conjunction with spatial modulation beamforming. Successful imitation of FEM will require randomized fast-time beamsteering from a two-dimensional array. The inherent randomness associated with FEM will be paired with the PRO-FM waveforms to create an emission possessing randomness in the space and frequency domains, called the FEM radar (FEMR). Unlike traditional MIMO, FEMR emits a coherent and time varying beam. Simulations will show the inherent enhancement to spatial resolution in two-dimensional space (azimuth and elevation) relative to standard beamforming using only the matched filter to process returns

    Biologically Inspired Sensing and MIMO Radar Array Processing

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    The contributions of this dissertation are in the fields of biologically inspired sensing and multi-input multi-output: MIMO) radar array processing. In our research on biologically inspired sensing, we focus on the mechanically coupled ears of the female Ormia ochracea. Despite the small distance between its ears, the Ormia has a remarkable localization ability. We statistically analyze the localization accuracy of the Ormia\u27s coupled ears, and illustrate the improvement in the localization performance due to the mechanical coupling. Inspired by the Ormia\u27s ears, we analytically design coupled small-sized antenna arrays with high localization accuracy and radiation performance. Such arrays are essential for sensing systems in military and civil applications, which are confined to small spaces. We quantitatively demonstrate the improvement in the antenna array\u27s radiation and localization performance due to the biologically inspired coupling. On MIMO radar, we first propose a statistical target detection method in the presence of realistic clutter. We use a compound-Gaussian distribution to model the heavy tailed characteristics of sea and foliage clutter. We show that MIMO radars are useful to discriminate a target from clutter using the spatial diversity of the illuminated area, and hence MIMO radar outperforms conventional phased-array radar in terms of target-detection capability. Next, we develop a robust target detector for MIMO radar in the presence of a phase synchronization mismatch between transmitter and receiver pairs. Such mismatch often occurs due to imperfect knowledge of the locations as well as local oscillator characteristics of the antennas, but this fact has been ignored by most researchers. Considering such errors, we demonstrate the degradation in detection performance. Finally, we analyze the sensitivity of MIMO radar target detection to changes in the cross-correlation levels: CCLs) of the received signals. Prior research about MIMO radar assumes orthogonality among the received signals for all delay and Doppler pairs. However, due to the use of antennas which are widely separated in space, it is impossible to maintain this orthogonality in practice. We develop a target-detection method considering the non-orthogonality of the received data. In contrast to the common assumption, we observe that the effect of non-orthogonality is significant on detection performance

    Biologically-inspired radar sensing

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    The natural world has an unquantifiable complexity and natural life exhibits remarkable techniques for responding to and interacting with the natural world. This thesis aims to find new approaches to radar systems by exploring the paradigm of biologically-inspired design to find effective ways of using the flexibility of modern radar systems. In particular, this thesis takes inspiration from the astonishing feats of human echolocators and the complex cognitive processes that underpin the human experience. Interdisciplinary research into human echolocator tongue clicks is presented before two biologically-inspired radar techniques are proposed, developed, and analyzed using simulations and experiments. The first radar technique uses the frequency-diversity of a radar system to localize targets in angle, and the second technique uses the degrees-of-freedom accessible to a mobile robotic platform to implement a cognitive radar architecture for obstacle avoidance and navigation

    In pursuit of high resolution radar using pursuit algorithms

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    Radar receivers typically employ matched filters designed to maximize signal to noise ratio (SNR) in a single target environment. In a multi-target environment, however, matched filter estimates of target environment often consist of spurious targets because of radar signal sidelobes. As a result, matched filters are not suitable for use in high resolution radars operating in multi-target environments. Assuming a point target model, we show that the radar problem can be formulated as a linear under-determined system with a sparse solution. This suggests that radar can be considered as a sparse signal recovery problem. However, it is shown that the sensing matrix obtained using common radar signals does not usually satisfy the mutual coherence condition. This implies that using recovery techniques available in compressed sensing literature may not result in the optimal solution. In this thesis, we focus on the greedy algorithm approach to solve the problem and show that it naturally yields a quantitative measure for radar resolution. In addition, we show that the limitations of the greedy algorithms can be attributed to the close relation between greedy matching pursuit algorithms and the matched filter. This suggests that improvements to the resolution capability of the greedy pursuit algorithms can be made by using a mismatched signal dictionary. In some cases, unlike the mismatched filter, the proposed mismatched pursuit algorithm is shown to offer improved resolution and stability without any noticeable difference in detection performance. Further improvements in resolution are proposed by using greedy algorithms in a radar system using multiple transmit waveforms. It is shown that while using the greedy algorithms together with linear channel combining can yield significant resolution improvement, a greedy approach using nonlinear channel combining also shows some promise. Finally, a forward-backward greedy algorithm is proposed for target environments comprising of point targets as well as extended targets

    Biologically inspired radar and sonar target classification

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    Classification of targets is a key problem of modern radar and sonar systems. This is an activity carried out with great success by echolocating mammals, such as bats, that have evolved echolocation as a means of detecting, selecting and attacking prey over a period of more than 50 million years. Because they have developed a highly sophisticated capability on which they depend for their survival, it is likely that there is potentially a great deal that can be learnt from understanding how they use this capability and how this might be valuably applied to radar and sonar systems. Bat-pollinated plants and their flowers represent a very interesting class of organisms for the study of target classification as it is thought that co-evolution has shaped bat-pollinated flowers in order to ease classification by bats. In this thesis, the strategy that underpins classification of flowers by bats is investigated. An acoustic radar has been developed to collect data to perform a floral echoes analysis. Results show that there is a relative relevance of specific parts of the flower in displaying information to bats and show that there are different characteristics in the flowers' echo fingerprints, depending on age and stage of maturity, that bats might use to choose the most suitable flowers for pollination. We show that, as suggested by the oral echoes analysis, a more intelligent way to perform target classification can result in improved classification performance and, investigate biologically inspired methods and ideas that might become important tools for the study and the development of radar and sonar target classification

    Design of large polyphase filters in the Quadratic Residue Number System

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    Temperature aware power optimization for multicore floating-point units

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