Search

In this paper, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data. [ABSTRACT FROM AUTHOR]

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps. [ABSTRACT FROM AUTHOR]

This paper develops a receiver for a multiple access scenario obtained by simply overlaying transmitted signals from different users, with (essentially) the same carrier, same modulation format, etc., without the benefit of conventional multiple access signaling. A joint a posteriori probability detector is derived for co-received signals. The receiver structure follows the familiar general model for turbo processing, employing a soft-in-soft-out joint detector followed by error correction decoding, which feeds back soft information in the form of extrinsic probabilities. Since the receiver is intended to function with an existing transmission system with no modifications to the transmitter, channelization methods and/or interleavers which would normally be designed into a multi-access system are absent. The general structure is demonstrated using a system employing LDPC decoding and simulated performance results are presented. [ABSTRACT FROM PUBLISHER]

The Sudoku puzzle is a discrete constraint satisfaction problem, as is the error correction decoding problem. We propose here an algorithm for solution to the Sinkhorn puzzle based on Sinkhorn balancing. Sinkhorn balancing is an algorithm for projecting a matrix onto the space of doubly stochastic matrices. The Sinkhorn balancing solver is capable of solving all but the most difficult puzzles. A proof of convergence is presented, with some information theoretic connections. A random generalization of the Sudoku puzzle is presented, for which the Sinkhorn-based solver is also very effective. [ABSTRACT FROM AUTHOR]

The equations for iteratively decoding low-density parity-check (LDPC) codes are generalized to compute joint probabilities of arbitrary sets of codeword bits and parity checks. The standard iterative LDPC decoder, which computes single variable probabilities, is realized as a special case. Another specialization allows pairwise joint posterior probabilities of pairs of codeword bits to be computed. These pairwise joint probabilities are used in an expectation-maximization (EM) based blind channel estimator that is ignorant of the code constraints. Channel estimates are input to a turbo equalizer that exploits the structure of the LDPC code. Feeding pairwise posterior probabilities back to the channel estimation, eliminates the need to average across time for channel estimator. Therefore, this scheme can be used to equalize very long codewords, even when the channel is time varying. This blind turbo equalizer is evaluated through computer simulations and found to perform as well as a channel-informed turbo equalizer but with approximately twice the number of turbo iterations. [ABSTRACT FROM AUTHOR]

Laguerre filters have infinite impulse responses (IIRs) but with finite tapped delay-line parameterizations. This paper investigates subspace-based blind identification of Laguerre filter tap coefficients, the internal filter state, and the input, given only noisy observations of the output. This paper deals only with single-input, multiple-output (SIMO) Laguerre models. A state space model for the SIMO Liguerre system is derived from which blind estimation algorithms are developed. As in the finite impulse response (FIR) case, the Laguerre filter taps coefficients can be estimated from the column space of a certain Hankel matrix constructed from noisy output observations, whereas the internal state and input can be estimated from the row space by exploiting state space structure. While not exactly uniquely identifiable, conditions are given for which the tap coefficients, the internal state, and the input can be determined to within a multiplicative scalar factor. [ABSTRACT FROM AUTHOR]

A primary purpose of this paper is to provide a focused tutorial on basic concepts in differential geometry in order to explain the rationale for “natural gradient” adaptation algorithms. A vectorized notation is employed to provide a new derivation of the natural gradient as a pullback. Based on this foundation, contravariant adaptation on structured matrices, such as Bezout, Toeplitz, orthogonal, or Vandermonde, is presented. A target application, with simulations demonstrating results, is blind source separation. [Copyright &y& Elsevier]

Presents information on a study which described a recursive blind equalizer that estimates the transmitted symbols of multiple cochannel signals in the presence of intersymbol interference. Description of the development of recursive blind equalizer; Incorporation of convolutional code structure into the equalizer; Conclusions.

Provides information on a study which proposed a method for the application of kernel structure to the problem of blind equalization of cochannel signals. Suggested approach to the blind equalization problem; Information on kernel structure theory for Hankel matrices that commonly arise in signal processing problems; Investigation into formulas for updating and downdating the kernel parameters.

Kurtosis is known to be effective at estimating signal timing and carrier phase offset when the processing is performed in a "burst mode," that is, operating on a block of received signal in an offline fashion. In this paper, kurtosis-based estimation is extended to provide tracking of timing and carrier phase, and frequency offsets. The algorithm is compared with conventional PLL-type timing/phase estimation and shown to be superior in terms of speed of convergence, with comparable variance in the matched filter output symbols. [ABSTRACT FROM AUTHOR]

The need to successfully navigate in the absence of GNSS has grown in recent years. In particular, light aircraft such as UAVs are growing in popularity for a variety of applications vulnerable to GPS denial. The research presented here develops a GPS-denied navigation scheme for light aircraft employing images formed from a synthetic aperture radar system. Past research has explored the utility of radar telemetry in GPS-denied systems. This research advances previous work by exploiting radar images to obtain range and cross-range position measurements. Images are formed using the Range-Doppler Algorithm, an efficient image formation algorithm ideal for the sometimes limited processing packages available to light aircraft. An inertial navigation and radar processing system is implemented using both real and simulated radar images to aid in estimating an aircraft’s state in a GPS-denied environment. The results show that navigation in the absence of GPS using synthetic aperture radar is feasible with converging and bounded estimation errors. [ABSTRACT FROM AUTHOR]

Presents information on a study which focused on the use of antenna array in blind equalization and resolution of overlapping echoes with unknown shape. Data model; Application of Fourier transforms to develop a frequency domain model of the data; Simulation; Conclusions.

The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation. [ABSTRACT FROM AUTHOR]

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation. [ABSTRACT FROM AUTHOR]

We present two lemmas relating polynomials occurring in the derivations of the Welch-Berlekamp (WB) key equation for Reed- Solomon codes. The lemma provides the connection between the conventional Welch-Berlekamp form of the key equation and the nulispace-based derivation of Dabiri and Blake. The second lemma allows the error values for the Dahiri Blake formulation to be computed based on the error values for the Welch-Berlekamp form. [ABSTRACT FROM AUTHOR]

An iterative least square method is presented for estimating the solution to the lidar equation. The method requires knowledge of the backscatter values at a boundary point for all channels and a priori defined relationships between backscatter, extinction, and mass-fraction concentration for all scattering components. The lidar equation is formulated in vector form, and a solution is computed using an iterative least square technique. The solution is stable for signals with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary point. The solution can be applied to lidar signals with an arbitrary number of wavelengths and scattering components. [ABSTRACT FROM AUTHOR]

The eigenmessage approach to iterative decoding introduces a degree of nonlocality into a belief propagation decoder by representing an entire set of messages around a cycle of the Tanner graph as a linear operator. The eigenvector for the operator represents a fixed point of the belief propagation algorithm around a cycle, with incident messages fixed. A multiple eigenmessage approach is also presented, in which messages around several cycles are simultaneously expressed. The eigenmessage approach may be applied to any graph with cycles, but we demonstrate its use on LDPC decoding. In this setting, computational results compare eigenmessage methods with conventional belief propagation decoding showing, using simulation and EXIT charts, that the eigenmessage approaches slightly reduce the number of decoder iterations compared to belief propagation decoding while preserving the probability of error of conventional decoding. [ABSTRACT FROM AUTHOR]

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs. [ABSTRACT FROM AUTHOR]

Long-wave infrared (LWIR) hyperspectral imagers (HSI) image a scene by collecting high-resolution spectra at each pixel. The data are similar to a camera image but have a large number of narrow spectral bands rather than the familiar broad three bands of red-green-blue in a traditional digital camera. Materials in LWIR are emissive (rather than reflective) and have unique spectra that can be used for material detection and identification. The measured spectra are a convolution of the material spectra (emissivity), the black body temperature (Planck curve), other interacting environmental spectral sources, and measurement error. One approach to material identification is temperature-emissivity separation (TES), which separates or deconvolves the material spectra from the temperature curve. To accomplish this task, we develop a unique flexible model which combines the mathematical model of the physical processes within a Bayesian nonparametric framework. In addition to offering interpretable estimates of model parameters, this model is able to identify material emissivity spectra and cluster pixels into appropriate material groups. We demonstrate our method using both a synthetic and measured dataset. The online supplementary material contains an appendix of the details of the sampling algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This work addresses management of water for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, it employs a data-driven approach to developing canal flow prediction models using the relevance vector machine (RVM), a probabilistic kernel-based learning machine. A search is performed across model attributes including input set, kernel scale parameter and model update scheme for models providing superior prediction capability using the RVM. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to 5 days. [Copyright &y& Elsevier]