Advanced spread spectrum linear frequency modulated (LFM) waveforms are being developed for advanced capability synthetic aperture radar (SAR) and ground moving target indication (GMTI) applications. We have demonstrated by analysis and simulation the feasibility of these new type waveforms and are now in the process of implementing them in hardware. The basic approach is to combine a traditional LFM radar waveform with a direct sequence spread spectrum (DSSS) waveform, and then on receive to de-spread the return and capture the resultant LFM return for traditional matched filter processing and enhanced SAR and GMTI. We show the analysis, simulation and some preliminary hardware results.
Wideband radar waveforms that employ spread-spectrum techniques were investigated and experimentally tested. The waveforms combine bi-phase coding with a traditional LFM chirp and are applicable to joint SAR-GMTI processing. After de-spreading, the received signals can be processed to support simultaneous GMTI and high resolution SAR imaging missions by airborne radars. The spread spectrum coding techniques can provide nearly orthogonal waveforms and offer enhanced operations in some environments by distributing the transmitted energy over a large instantaneous bandwidth. The LFM component offers the desired Doppler tolerance. In this paper, the waveforms are formulated and a shift-register approach for de-spreading the received signals is described. Hardware loop-back testing has shown the feasibility of using these waveforms in experimental radar test bed.
We devise a segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme is based on finding a threshold for the probability density function of the summing average field over a neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). A rigorous stochastic analysis is used to derive an exact expression for the cumulative density function of the likelihood of the averaging sum image. Based on this, an accurate probability of error is derived and the performance of the scheme is analyzed. The segmentation performs reasonably well for both simulated and real images. The LRFM scheme is also compared with standard edge detection methods to quantify the significant gains obtained from the optimized edge detector. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Nonvisual quantification and misclassification in speckled images, such as synthetic aperture radar and medical ultrasound, is relatively new and is of interest to remote sensing human observers and clinicians.
Order-Statistic Constant False-Alarm Rate (OS-CFAR) processing provides an adaptive threshold to distinguish targets from clutter returns in radar detection. In traditional OS-CFAR, ordered statistics from a fixed-size reference window surrounding the cell under test (CUT) provide an estimate of the mean clutter power. We investigate adapting the reference window size as a function of the observed data in order to obtain robust detection performance in nonhomogeneous clutter environments. Goodness-of-fit tests are used to select the adaptive reference window size. Unlike traditional OS-CFAR, computationally e±cient multiscale OS-CFAR based on this approach must be modified to include the CUT in the reference window. The effects of CUT inclusion are investigated. Preliminary results suggest that CUT-inclusive OS-CFAR with adaptive window size performs well in nonhomogeneous clutter environments of varying size. These results point to the feasibility of computationally efficient multi-scale OS-CFAR.
We consider the problem of monitoring the concentration and dispersion of pollutants in the atmosphere using a collection of randomly scattered sensors. The sensors are capable of indicating only that the concentration has exceeded a randomly selected threshold and providing this information to a central hub. We consider the case when the dispersion occurs in a general wind velocity field. In this case, the dispersion is modelled by a PDE which in general does not have a closed form solution. We find
the maximum likelihood estimate of the concentration as well as the time and location of the pollutant source. Frechet derivatives are used to optimize the cost function. The wind velocity field is estimated as a nuisance parameter.
Coherent Pulse-Doppler radar systems, whether used for synthetic aperture imaging or surveillance purposes, generally transmit a coherent pulse train made up of identical pulses. While these pulses may contain complex modulation---for example, linear FM chirps or frequency and phase coding---the fact remains that these pulses are usually identical. In this paper, we consider the potential advantages of pulse-trains made up of pulses that are distinctly different from pulse-to-pulse. In particular, we investigate a signal processing algorithm that provides increased resolution and discrimination through delay-Doppler sidelobe suppression in the region surrounding the mainlobe of the delay-Doppler response.
Detection and identification of objects in images formed by coherent imaging systems are complicated by the presence of speckle. Speckle not only complicates these problems for human observers, but also for machine detection and identification algorithms. We investigate optimal statistical tests for object discrimination and orientation determination in speckle and compare their performance to that of human observers for the same problems. We formulate maximum likelihood tests for determining the orientation of an object and for discriminating among a set of known objects in a speckled image. We then analyze the performance of these tests to study the system requirements for reliable object discrimination and orientation determination. Next we generalize these tests and their corresponding pertormance analyses into three broad classes of pattern recognition problems, corresponding to orthogonal, antipodal, and biorthogonal signal problems in statistical communications theory. These generalizations make the design and analysis of a broad range of object discrimination and orientation determination straightforward. Finally we compare the performance of these tests to the results of Korwar and Pierce for human interpretation of objects in speckled images. We note that for fixed image contrast, number of looks, and image size in pixels, object shape has no effect on machine detection performance. This is not true for the human observer.