Ram M. Narayanan

Design, performance, and applications of a coherent ultra-wideband random noise radar

Ram M. NarayananYi XuPaul D. HoffmeyerUniversity of Nebraska—LincolnCenter for Electro-OpticsDepartment of Electrical EngineeringLincoln, Nebraska 68588-0511E-mail: rnarayanan@unl.eduJohn O. CurtisU.S. Army Waterways Experiment StationEnvironmental LaboratoryVicksburg, Mississippi 39180-6199Abstract. A novel coherent ultra-wideband radar system operating inthe 1- to 2-GHz frequency range has been developed recently at theUniversity of Nebraska. The radar system transmits white Gaussiannoise. Detection and localization of buried objects is accomplished bycorrelating the reflected waveform with a time-delayed replica of thetransmitted waveform. Broadband dual-polarized log-periodic antennasare used for transmission and reception. A unique signal-processingscheme is used to inject coherence into the system by frequency trans-lation of the ultrawideband signal by a coherent 160-MHz phase-lockedsource prior to performing heterodyne correlation. The system coher-ence allows the extraction of a target’s polarimetric amplitude and phasecharacteristics. This paper describes the unique design features of theradar system, develops the theoretical foundations of noise polarimetry,provides experimental evidence of the polarimetric and resolution capa-bilities of the system, and demonstrates results obtained in subsurfaceprobing applications.

Noise estimation in remote sensing imagery using data masking

Estimation of noise contained within a remote sensing image is essential in order to counter the effects of noise contamination. The application of convolution data-masking techniques can effectively portray the influence of noise. In this paper, we describe the performance of a developed noise-estimation technique using data masking in the presence of simulated additive and multiplicative noise. The estimation method employs Laplacian and gradient data masks, and takes advantage of the correlation properties typical of remote sensing imagery. The technique is applied to typical textural images that serve to demonstrate its effectiveness. The algorithm is tested using Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) imagery. The algorithm compares favourably with existing noise-estimation techniques under low to moderate noise conditions.

Three-dimensional interferometric ISAR imaging for target scattering diagnosis and modeling

Two-dimensional (2-D) inverse synthetic aperture radar (ISAR) imaging has been widely used in target scattering diagnosis, modeling and target identification. A major shortcoming is that a 2-D ISAR image cannot provide information on the relative altitude of each scattering center on the target. In this paper, we present an interferometric inverse synthetic aperture radar (IF-ISAR) image processing technique for three-dimensional (3-D) target altitude image formation. The 2-D ISAR images are obtained from the signature data acquired as a function of frequency and azimuthal angle. A 3-D IF-ISAR altitude image can then be derived from two 2-D images reconstructed from the measurements by antennas at different altitudes. 3-D altitude image formation examples from both indoor and outdoor test range data are demonstrated on complex radar targets.

Integrated spectral and spatial information mining in remote sensing imagery

Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.

Doppler estimation using a coherent ultrawide-band random noise radar

The University of Nebraska has developed an ultrawide-band (UWB) coherent random noise radar operating over the 1-2 GHz frequency range. The system achieves phase coherence by using heterodyne correlation of the received signal with a time-delayed frequency-shifted replica of the transmit waveform. Knowledge of the phase of the received signal and its time dependence due to target motion permits the extraction of the mean Doppler frequency from which the target speed can be inferred. Theoretical analysis, simulation studies, and laboratory measurements using a microwave delay line showed that it was possible to estimate the Doppler frequency from targets with linear as well as rotational motion. Field measurements using a photonic delay line demonstrated the success of this technique at a range of about 200 m at target speeds of up to 9 m/s. Analysis shows that the accuracy with which the Doppler frequency can be estimated depends not only on the phase performance of various components within the system, but also upon the random nature and bandwidth (BW) of the transmit waveform, and the characteristics of unsteady target motion.

Through-wall radar imaging using UWB noise waveforms

This paper examines the results of our research on the use of ultrawideband noise waveforms for imaging objects behind walls. The advantages of using thermally generated noise as a probing signal are introduced. The technique of heterodyne correlation used to inject coherence in the random noise probing signal and to collapse the wideband reflected signal into a single frequency are presented. Central to successful imaging through building walls is the characterization of the wideband propagation properties of wall materials and these are discussed. The basic concepts of synthetic aperture radar image formation using noise waveforms and the unique problems associated with the random nature of the transmit waveform are analyzed. We also address issues related to locating, detection, and tracking humans behind walls, using new tools for human activity characterization, namely the Hilbert-Huang Transform approach. The results indicate that noise radar technology combined with modern signal processing approaches is indeed a viable technique for covert high-resolution imaging of obscured stationary and moving targets.

A review of wetlands remote sensing and defining new considerations

Significant progress has been made in using remote sensing as a means of acquiring information about wetlands. This research provides a brief review of selected previous works, which address the issues of wetland identification, classification, biomass measurement, and change detection. Suggested new research emphases include compiling basic spectral‐reflectance characteristics for individual wetland species by means of close‐range instrumentation, analyzing canopies architectures to facilitate species identification, and assessing the impact on composite spectral signatures of wet soils and variable depths of standing water beneath emergent canopies. These research foci are justifiable when considered in the context of environmental change / variability and the production of trace gases.

A shape-based approach to change detection of lakes using time series remote sensing images

Shape analysis has not been considered in remote sensing as extensively as in other pattern recognition applications. However, shapes such as those of geometric patterns in agriculture and irregular boundaries of lakes can be extracted from the remotely sensed imagery even at relatively coarse spatial resolutions. This paper presents a procedure for efficiently retrieving and representing shapes of interesting features in remotely sensed imagery using supervised classification, object recognition, parametric contour tracing, and proposed piecewise linear polygonal approximation techniques. In addition, shape similarity can be measured by means of a computationally efficient metric. The study was conducted on a time series of radiometric and geometric rectified Landsat Multispectral Scanner (MSS) images and Thematic Mapper (TM) images, covering the scenes containing lakes in the Nebraska Sand Hills region. The results validate the effectiveness of the proposed processing chain in change detection of lake shapes and show that shape similarity is an important parameter in quantitatively measuring the spatial variations of objects.

Ultrawideband Random Noise Radar Design for Through-Wall Surveillance

We have developed an ultrawideband (UWB) random noise radar for through-wall surveillance applications. The operating frequency is in the ultrahigh frequency range, and the entire system is built around the concept of software defined radio. The radar receiver performance is statistically evaluated using both simulation studies and actual measurement results. We also discuss the phenomena of interference level and radar cross section (RCS) of the human target using the receiver operating characteristics (ROC). Experimental results presented show that random noise radars are useful for detecting and tracking humans obscured by building walls.

FOPEN SAR imaging using UWB step-frequency and random noise waveforms

The detection and identification of targets obscured by foliage have been topics of great interest. Several synthetic aperture radar (SAR) experiments have demonstrated promising images of terrain and man-made objects obscured by dense foliage, by using either linear frequency modulation (LFM) or step-frequency waveforms. We present here the methodology and results of a comparative study on foliage penetration (FOPEN) SAR imaging using ultrawideband (UWB) step-frequency and random noise waveforms. A statistical-physical foliage transmission model is developed for simulation applications. The foliage obscuring pattern is analyzed by means of the technique of paired echoes. The results of the comparative study demonstrates the ability of a UHF band UWB random noise radar to be used as a FOPEN SAR. Advantages of the random noise radar system include covert detection and immunity to radio frequency interference (RFI).