We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions.

Power law spectrums are frequently used to model complex natural processes. Estimation of power law behavior can be severely hampered by temporal gaps in measured data, which can occur frequently for data sets spanning many years. The author presents an algorithm for iterative gap filling through the use of an autoregressive model. This technique is validated with a simulation study, and its benefits are demonstrated through application to measured data sets.

Power law spectrums are frequently used to model complex nonlinear systems, with their parameters estimated empirically from measured data. This letter derives a maximum likelihood estimator and Cramér-Rao bound for power law spectrums. An empirical study illustrates that the maximum likelihood estimator outperforms traditional Fourier transform based estimators, and achieves the Cramér-Rao bound. A simple correction to the periodogram linear regression is proposed and is shown to nearly match the maximum likelihood estimator's performance.

Synthetic aperture radar (SAR) is a popular tool for long-range imaging of stationary ground objects. Moving targets in the imaged scene will have a mismatch to the matched filter in the image formation process, thus degrading target image quality. In this letter, the impact of uncompensated target rotation in SAR imagery is studied. An efficient algorithm for image-quality-based target rotation correction is proposed.

This paper discusses the effect of motion measurement errors (MMEs) on measured bistatic synthetic aperture radar (SAR) phase history data that has been motion compensated to the scene origin. We characterize the effect of low-frequency MMEs on bistatic SAR images, and, based on this characterization, we derive limits on the allowable MMEs to be used as system specifications. Finally, we demonstrate that proper orientation of a bistatic SAR image during the image formation process allows application of monostatic SAR autofocus algorithms in postprocessing to mitigate image defocus.

This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets.

The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. Though so-called "tap-leakage" modifications of LMS have been proposed to mitigate this drift, they inevitably introduce parameter bias which degrades mean-squared error performance. In this letter, we propose a novel algorithm which leaks only in the unexcited modes, thus introducing insignificant bias, while still retaining the low computational complexity of LMS.