Constant false-alarm rate algorithm based on test cell information

A new constant false-alarm rate (CFAR) detection algorithm, designated as switching CFAR (S-CFAR), is proposed and analysed. The S-CFAR algorithm selects CFAR reference samples using the magnitude of the sample in the cell under test, which is an information that has not been exploited in any other existing CFAR detectors. S-CFAR closed-form analysis is presented, and comparisons with other representative CFAR algorithms are given. An S-CFAR detector can be tuned such that it has a small CFAR loss when operating on a homogeneous background while achieving improved robustness in the presence of interfering targets and clutter power transition. The S-CFAR detector is also simple to design and implement since no sample ordering is required. - Reference

Fundamental resolution limits of closely spaced random signals

Fundamental limitations on estimation accuracy are well known and include a variety of lower bounds including the celebrated Cramer-Rao lower bound. However, similar theoretical limitations on resolution have not yet been presented. The authors exploit results from detection theory for deriving fundamental limitations on resolution. In this correspondence the authors discuss the case of zero mean random Gaussian signals with a general and predefined covariance matrix observed with additive white Gaussian noise. The results are general and are not based on any specific resolution technique and therefore hold for any method and for any probability of successful resolution. The resolution limit is a simple expression of the observation interval, the pre-specified resolution success probability and the second derivative of the covariance matrix. As an example, the authors discuss the bearing resolution of two emitters with closely spaced direction of arrival, impinging on an array of sensors. The theoretical limits are compared with empirical performance of the model order selection criteria known as the Akaike information criterion and the minimum description length. - Reference

The Detection of Buried Pipes From Time-of-Flight Radar Data

Ultrawideband radar is commonly used in the frequency range of 50–500 MHz to detect buried pipes at a depth of about 1–2 m depending on the soil characteristics. The typical feature used to locate the pipes is the hyperbolic pattern of the time of flight generated by a linear scan of the antenna above the surface. When the pipes are close together, the hyperbolas overlap, and a straightforward least squares fit is not possible. The Hough transform provides one possible solution. This paper extends the Hough transform by introducing a weighting factor depending on the differentials of the unknown parameters with respect to the experimental errors, namely, the probe position error and the time-of-flight error. This enables optimally placed sets of data pairs to be given greater weight than “ill-conditioned” sets, as for example when all data pairs lie near one end of the arc. The result is a decrease in the background amplitude with respect to the maximum of the peaks in the Hough accumulator space. It is shown that this improvement persists even when many arcs are present. A mathematical analysis with analytical results is given for the case of four unknowns: pipe radius $R$ , pipe center position ($Y$, $Z$), and soil propagation velocity $V$. The results are presented through simulations introducing controlled uncertainties in the probe position, the time of flight, and its bin size. The simulations demonstrate the correlations that occur between the radius, depth, and velocity for given experimental uncertainties. - Reference

Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences

In this paper, we address the problem of estimating 3-D motions of a stratified atmosphere from satellite image sequences. The analysis of 3-D atmospheric fluid flows associated with incomplete observation of atmospheric layers due to the sparsity of cloud systems is very difficult. This makes the estimation of dense atmospheric motion field from satellite image sequences very difficult. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into cloud layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of 3-D wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete 3-D space, using a multilayer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences. - Reference

A Two-Dimensional Spectrum Model for General Bistatic SAR

This paper derives a 2-D spectrum model for general bistatic synthetic aperture radar (SAR). By introducing some new parameters such as equivalent monostatic parameters, bistatic factor, and weighted-equivalent range, the 2-D spectrum of general bistatic SAR can be expressed in the form of monostatic SAR even when the transmitter and receiver move along unparallel trajectories with different velocities. The result formulates bistatic SAR into an equivalent monostatic SAR model and would be useful for developing efficient bistatic SAR algorithms in frequency-domain or hybrid-domain processing. Simulation results are given to validate the performance of the model. For special bistatic SAR configurations, the model can be simplified. Compared to other similar models, the proposed model is clearer and much more concise. - Reference

Information Theory-Based Approach for Contrast Analysis in Polarimetric and/or Interferometric SAR Images

We propose a new approach for evaluating the contribution of the different channels of polarimetric and interferometric synthetic aperture radar (PolInSAR) images. For that purpose, we demonstrate that the Bhattacharyya distance between the probability density functions of neighboring regions in the image provides an efficient scalar contrast measure. We show that the analysis of this contrast measure allows one to precisely characterize the contribution of each channel for different system configurations, including intensity, polarimetric, and interferometric images. We illustrate this approach using a real synthetic aperture radar image to compare several polarimetric system architectures. Since PolInSAR imaging configurations can correspond to complex and expensive systems, the proposed method can be helpful in system imaging optimization. - Reference