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Inverse synthetic aperture radar processing using parametric time-frequency estimators Phase I
This report summarizes the work performed for the Office of the Chief of Naval Research (ONR) during the period of 1 September 1997 through 31 December 1997. The primary objective of this research was aimed at developing an alternative time-frequency approach which is recursive-in-time to be applied to the Inverse Synthethic Aperture Radar (ISAR) imaging problem discussed subsequently. Our short term (Phase I) goals were to: 1. Develop an ISAR stepped-frequency waveform (SFWF) radar simulator based on a point scatterer vehicular target model incorporating both translational and rotational motion; 2. Develop a parametric, recursive-in-time approach to the ISAR target imaging problem; 3. Apply the standard time-frequency short-term Fourier transform (STFT) estimator, initially to a synthesized data set; and 4. Initiate the development of the recursive algorithm. We have achieved all of these goals during the Phase I of the project and plan to complete the overall development, application and comparison of the parametric approach to other time-frequency estimators (STFT, etc.) on our synthesized vehicular data sets during the next phase of funding. It should also be noted that we developed a batch minimum variance translational motion compensation (TMC) algorithm to estimate the radial components of target motion (see Section IV). This algorithm is easily extended to recursive solution and will probably become part of the overall recursive processing approach to solve the ISAR imaging problem. Our goals for the continued effort are to: 1. Develop and extend a complex, recursive-in-time, time- frequency parameter estimator based on the recursive prediction error method (RPEM) using the underlying Gauss- Newton algorithms. 2. Apply the complex RPEM algorithm to synthesized ISAR data using the above simulator. 3. Compare the performance of the proposed algorithm to standard time-frequency estimators applied to the same data sets
Window Functions and Their Applications in Signal Processing
Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada
Statistical assessment on Non-cooperative Target Recognition using the Neyman-Pearson statistical test
Electromagnetic simulations of a X-target were performed in order to obtain its Radar Cross
Section (RCS) for several positions and frequencies. The software used is the CST MWS©. A 1 : 5
scale model of the proposed aircraft was created in CATIA© V5 R19 and imported directly into
the CST MWS© environment. Simulations on the X-band were made with a variable mesh size
due to a considerable wavelength variation. It is intended to evaluate the Neyman-Pearson (NP)
simple hypothesis test performance by analyzing its Receiver Operating Characteristics (ROCs)
for two different radar detection scenarios - a Radar Absorbent Material (RAM) coated model,
and a Perfect Electric Conductor (PEC) model for recognition purposes.
In parallel the radar range equation is used to estimate the maximum range detection for the
simulated RAM coated cases to compare their shielding effectiveness (SE) and its consequent
impact on recognition. The AN/APG-68(V)9’s airborne radar specifications were used to compute
these ranges and to simulate an airborne hostile interception for a Non-Cooperative Target
Recognition (NCTR) environment. Statistical results showed weak recognition performances
using the Neyman-Pearson (NP) statistical test. Nevertheless, good RCS reductions for most of
the simulated positions were obtained reflecting in a 50:9% maximum range detection gain for
the PAniCo RAM coating, abiding with experimental results taken from the reviewed literature.
The best SE was verified for the PAniCo and CFC-Fe RAMs.Simulações electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS)
para várias posições e frequências. O software utilizado é o CST MWS©. O modelo proposto à
escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho
CST MWS©. Foram efectuadas simulações na banda X com uma malha de tamanho variável
devido à considerável variação do comprimento de onda. Pretende-se avaliar estatisticamente
o teste de decisão simples de Neyman-Pearson (NP), analisando as Características de Operação
do Receptor (ROCs) para dois cenários de detecção distintos - um modelo revestido com material
absorvente (RAM), e outro sendo um condutor perfeito (PEC) para fins de detecção.
Em paralelo, a equação de alcance para radares foi usada para estimar o alcance máximo de
detecção para ambos os casos de modo a comparar a eficiência de blindagem electromagnética
(SE) entre os diferentes revestimentos. As especificações do radar AN/APG-68(V)9 do F-16 foram
usadas para calcular os alcances para cada material, simulando uma intercepção hostil num
ambiente de reconhecimento de alvos não-cooperativos (NCTR). Os resultados mostram performances
de detecção fracas usando o teste de decisão simples de Neyman-Pearson como detector
e uma boa redução de RCS para todas as posições na gama de frequências selecionada. Um ganho
de alcance de detecção máximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com
os resultados experimentais da bibliografia estudada. Já a melhor SE foi verificada para o RAM
CFC-Fe e PAniCo
Window Functions and Their Applications in Signal Processing
Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada
Active and Passive Multi-Sensor Radar Imaging Techniques Exploiting Spatial Diversity
The work here presented reports several innovative SAR and ISAR radar imaging techniques exploiting the spatial diversity offered by multi-sensor systems in order to improve the performance with respect to the conventional, single channel cases. Both the cases of dedicated transmitters and exploitation of opportunity transmitters are considered
Active and Passive Multi-Sensor Radar Imaging Techniques Exploiting Spatial Diversity
The work here presented reports several innovative SAR and ISAR radar imaging techniques exploiting the spatial diversity offered by multi-sensor systems in order to improve the performance with respect to the conventional, single channel cases. Both the cases of dedicated transmitters and exploitation of opportunity transmitters are considered
On estimating the hurst parameter from least-squares residuals. Case study: Correlated terrestrial laser scanner range noise
Many signals appear fractal and have self-similarity over a large range of their power spectral densities. They can be described by so-called Hermite processes, among which the first order one is called fractional Brownian motion (fBm), and has a wide range of applications. The fractional Gaussian noise (fGn) series is the successive differences between elements of a fBm series; they are stationary and completely characterized by two parameters: the variance, and the Hurst coefficient (H). From physical considerations, the fGn could be used to model the noise of observations coming from sensors working with, e.g., phase differences: due to the high recording rate, temporal correlations are expected to have long range dependency (LRD), decaying hyperbolically rather than exponentially. For the rigorous testing of deformations detected with terrestrial laser scanners (TLS), the correct determination of the correlation structure of the observations is mandatory. In this study, we show that the residuals from surface approximations with regression B-splines from simulated TLS data allow the estimation of the Hurst parameter of a known correlated input noise. We derive a simple procedure to filter the residuals in the presence of additional white noise or low frequencies. Our methodology can be applied to any kind of residuals, where the presence of additional noise and/or biases due to short samples or inaccurate functional modeling make the estimation of the Hurst coefficient with usual methods, such as maximum likelihood estimators, imprecise. We demonstrate the feasibility of our proposal with real observations from a white plate scanned by a TLS
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