No description available.

No description available.

This thesis is concerned with the processing of time domain signals received by a single sensor. An example of such signals is the radar return, which is used in one way or another to estimate the power spectral density a frequency representation of the power of the signal in order that we can pick up and track the moving targets. since the POWER SPECTRAL DENSITY ESTIMATION is a fundamental tool in digital signal processing, the theory of the different approaches to PSDE is given in the Literature review chapter. The aim of this research is to develop a technique for the Power Spectral Density Estimation (PSDE) of multiple signals in white noise, which has high resolution capability and less frequency estimation errors. Hence, the various techniques mentioned above are tested for their detection, resolution capabilities and performance. Finally the different parameters affecting the resolution and detection capabilities of the Eigen Vector Decomposition Techniques (EVDT) for PSDE are studied in some depth.

No description available.

No description available.

No description available.

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

No description available.

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

Thesis (Ph. D.)--Ohio University, November, 1990. / Title from PDF t.p.