1.1 Multipath Signal Recovery in the Presence of Very Large Noise
By: Irina Gladkova
City College of New York | Abstract: In this paper, we present a method for resolving a return multipath signal in the presence of very large noise. The method basically consists of choosing a function, whose correlation with the transmitted signal is either zero, or of an easily analyzed form. Hence, correlating such a function with the received signal will single out the noise component. An interesting feature of our approach is that large noise is not inherently disadvantageous. The paper describes the general method and then illustrates its application in some cases of general interest. |
1.2 Benefits of High Resolution SAR for ATR of Targets in Proximity
By: Peter Bajcsy
National Center for Supercomputing Applications
and: Anirban R. Chaudhuri
Indian Statistical Institute, India | Abstract: In this work we present a new extraction and matching algorithms that enable to perform automatic target recognition (ATR) in high-resolution synthetic aperture radar (SAR) data and targets in proximity. Our motivation was to show benefits of high-resolution SAR for ATR and extend the current capabilities of ATR algorithms for targets in extended operating conditions (EOCs), for example, targets in proximity. We develop a new extraction algorithm for target signatures represented by a point pattern. Each point pattern is extracted using a resolution independent SAR peak model. Test and prototype target signatures are compared with a new matching algorithm. The matching algorithm is capable of identifying multiple signatures in a test point pattern. An experimental evaluation of ATR performance for targets in proximity at multiple data resolution is conducted. The contribution of this work is in (a) developing a peak extraction algorithm that uses a resolution independent SAR peak model, (b) designing a new matching algorithm that can identify multiple signatures in a single test pattern, (c) evaluating ATR performance for targets in proximity at multiple data resolutions. |
1.3 A New Complementary Waveform Technique for Radar Signals
By: Peter Zulch and Michael C. Wicks
Air Force Research Laboratory
and: Bill Moran, Sofia Suvorova, and Jim Byrnes
Prometheus, Inc. | Abstract: A new phase coding technique for radar signals is introduced which uses novel complementary waveforms constructed to have optimal sidelobe performance. The waveforms are constructed using a modification of the Prometheus Orthonormal Set (PONS) technique. An advantage of a PONS matrix is that it allows for many complementary pairs of waveforms to choose from as well as allowing for multiple pairs to be used simultaneously. It will be shown that sets of waveforms, which are complementary in quartets, can also be applied for more flexibility. Results showing improved ambiguity properties versus other radar waveform coding techniques will be given. |
1.4 Generation of Rejection Method Bounds for Spherically Invariant Random Vectors
By: Andrew D. Keckler and Donald D. Weiner
Syracuse University | Abstract: Based upon the central limit theorem, random clutter returns are commonly modeled as Gaussian. Nevertheless, many situations arise in practice where the data are clearly non-Gaussian, as is seen with "spiky" radar clutter. Spherically invariant random vectors (SIRV's) are especially attractive for modeling correlated non-Gaussian clutter. This paper discusses the computer simulation of SIRV's for Monte Carlo purposes using the rejection method. A key requirement of the rejection method is the ability to find a tight bound of the probability density function, from which random samples can be readily generated. An automated technique for generating this bound for the SIRV probability density function is presented. |
1.5 DSP Hardware Implementation of BAVQ Encoding for SAR Raw Data
By: Leyu Zhu, Wen Homg, Jun Wang, and Yunneng Yuan
Beijing University of Aeronautics and Astronautics | Abstract: This paper describes the available compression algorithms for SAR (Synthetic aperture Radar) raw data. The compression performances of three typical algorithms have been compared and the BAVQ algorithm is selected in view of the trade-off between the performance and the complexity. The hardware implementation scheme of BAVQ encoder has been designed and the DSP hardware simulation result proves the feasibility of the DSP implementation. |
1.6 Nonhomogeneity Detection and the Multistage Wiener Filter
By: William C. Ogle, Hien Nguyen, and J. Scott Goldstein
Science Applications International Corp.
and: Peter A. Zulch and Michael C. Wicks
Air Force Research Laboratory | Abstract: This paper introduces the multistage Wiener filter for radar space-time adaptive processing, combined with the generalized innerproduct as a preprocessor in nonhomogeneous environments. By using recorded data from the Multichannel Airborne Radar Measurement program, the performance of the multistage Wiener filter and sample matrix inversion are assessed both with and without the preprocessor. The constant false-alarm rate test statistic is computed for each range bin and the performance metric used in this analysis is the ratio of the target value to the root mean square value of the noise values. Both high and low sample-support environments are considered. The reduced-rank multistage Wiener filter is demonstrated to outperform full rank sample matrix inversion, even with the generalized inner-product preprocessor. Additionally, the multistageWiener filter is shown to have its largest impact when used in conjunction with the preprocessor in the low sample-support environment. In this case, it nearly achieves the performance obtained by the full-rank and high sample-support case. |
1.7 Interface Identification using a GPR Signal: A Monte Carlo Markov Chain Approach
By: Arnaud Coatanhay and Jean-Jacques Szkolnik
ENSIETA, France | Abstract: This paper presents a new signal processing method to improve the identification of interface between different layered media, using a Ground Penetrating Radar (GPR) recording. Our methodological approach is based on Monte Carlo Markov Chain (MCMC) model. The deconvolution of the GPR signal is obtained in considering a stochastic estimation related to a maximum a posteriori criterion. The only known elements are the signal recorded from the GPR backscattering (one dimension approximation), and the order of the ARMA signal model for the emitted pulse. |
1.8 Technique for Frequency Analysis of Unevenly Sampled Radar Data
By: Matthew G. House and Paul D. Mountcastle
XonTech, Inc. | Abstract: A new technique is described that generalizes the usefulness of the Discrete Fourier Transform for spectral analysis of radar data to applications where the discrete data points to be analyzed are not sampled at regular intervals and/or do not have equal statistical weight. This method finds a frequency-domain representation which best represents the given time-domain data in the least-square-residual sense. The theoretical implications of such an approach are discussed, and an example of the technique applied to a Doppler-processing task is given. The merits of the approach relative to other spectral analysis techniques are discussed. |
1.9 The Optimality in Neyman-Pearson Sense in the Distributed CFAR Detection with Multisensor
By: Guan Jian, Meng Xiang-Wei, and He You
Naval Aeronautical Engineering Academy, PR China
and: Peng Ying-Ning
Tsinghua University | Abstract: The Optimality in Neyman-Pearson (NP) Sense in distributed CFAR detection with a multisensor is discussed in this paper. Most of the existing analysis of optimization of distributed CFAR detection in the NP sense is done under the limitation of binary local decision and no communication among local processors. We find that the real optimization in NP sense can not be realized under this limitation. If local test statistics (LTS) are used and fused in fusion, the real optimal NP test could be implemented by likelihood ratio test (LRT). |