Arnab Shaw

Wright State University, Electrical Engineering, Faculty Member

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Papers by Arnab Shaw

Moving Target Detection in K-Distributed Clutter using Unit Circle Toeplitz Rectification with Limited Secondary Data

2022 56th Asilomar Conference on Signals, Systems, and Computers

Exploiting Unit Circle Roots for Moving Target Detection in Low-Rank Clutter with Low Sample Support

2022 56th Asilomar Conference on Signals, Systems, and Computers

Moving Target Detection using Distributed MIMO Radar in Non-Homogeneous Clutter with Limited Training Data

2020 54th Asilomar Conference on Signals, Systems, and Computers

This paper concerns detection of a moving target in compound-Gaussian clutter with inverse-Gamma ... more This paper concerns detection of a moving target in compound-Gaussian clutter with inverse-Gamma distributed power using a distributed active MIMO radar network. Adaptive detection requires an estimate of the clutter covariance matrix from a finite set of, possibly non-homogeneous, target-free training data. If the number of available training data are limited, conventional covariance matrix estimators will fail to provide adequate detection performance. This motivates the use of structured estimators which do not rely upon large amounts of training data. In this paper, a clutter covariance matrix estimator based non-negative linear least-squares is applied to the problem of adaptive moving target detection in the context of distributed MIMO radar. For each transmit-receive pair of the distributed radar network, an improved estimate of the clutter covariance matrix is computed using the non-negative least-squares estimator. Then, we apply the improved estimates in conjunction with a detector derived using inverse-Gamma distributed clutter power. Simulation examples are used to demonstrate the performance improvement of the constrained least-squares method as compared to a conventional technique. As a baseline, we a compare the performance of both the conventional and constrained least-squares methods against the clairvoyant detector.

Wideband directions of arrival estimation of chirp sources using compressive sensing

Radar Sensor Technology XXII

Classical Directions of Arrival (DoA) algorithms estimate the time delays associated with the sig... more Classical Directions of Arrival (DoA) algorithms estimate the time delays associated with the signal received at an array of sensors through phase information. Most existing wideband algorithms decompose the signals received by an antenna array into multiple narrowband frequencies, and then the wideband DOAs are estimated by coherent or incoherent combination of signal and noise subspace information at multiple source frequencies. A novel algorithm for finding the direction of arrival (DoA) for wide-band chirp sources is introduced in this study, where frequency shift rather than phase shift is utilized to estimate the signal time delays between sensors, eliminating many limitations due to phase ambiguity like spatial sampling, leading to finer angular resolution between multiple sources. The proposed algorithm processes the data using Discrete Chirp Fourier Transform (DCFT) that invokes the exact chirp model in the signal leading to more precise estimates compared to general wideband DoA methods that do not exploit the chirp model. Use of Compressed Sensing (CS) enables exploitation of the sparsity in the DCFT-domain data for highly accurate DoA estimation. Reduced number of measurements are required for CS optimization processing by making use of the sparsity of the DCFT coefficients. The proposed approach eliminates the need for correlation, iterations, and time-frequency analysis needed by many classical chirp signal parameter estimation algorithms. Theoretical derivation is given and simulation results of the new algorithm for single and multiple wide-band chirp sources show significant performance enhancement even in highly noisy environment.

A new algorithm for optimal estimation of plant parameters from input-output data

[1992] Proceedings of the 31st IEEE Conference on Decision and Control

Abstract A theoretical and algorithmic framework is proposed for optimal identification of ration... more Abstract A theoretical and algorithmic framework is proposed for optimal identification of rational transfer function parameters of discrete-time linear systems from input-output (IO) data. The nonlinear optimization criterion is theoretically decoupled into a purely linear problem for estimating the optimal numerator and nonlinear problem for the optimal denominator. An iterative computational algorithm is given for estimation of the denominator. Once the denominator is estimated, the numerator is obtained with only a single step of ...

Improved automatic target recognition using singular value decomposition

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)

Abstract A new algorithm is presented for automatic target recognition (ATR) where the templates ... more Abstract A new algorithm is presented for automatic target recognition (ATR) where the templates are obtained via singular value decomposition (SVD) of high range resolution (HRR) profiles. SVD analysis of a large class of HRR data reveals that the range-space eigenvectors corresponding to the largest singular value accounts for more than 90% of the target energy. Hence, it is proposed that the range-space eigenvectors be used as templates for classification. The effectiveness of data normalization and Gaussianization of profile ...

A New Approach to Moving Target Detection using Unit Circle Roots Constrained Adaptive Matched Filter

2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021

In this paper, we derive a novel variation of the adaptive matched filter (AMF) which enforces th... more In this paper, we derive a novel variation of the adaptive matched filter (AMF) which enforces the unit circle roots property by exploiting polynomial conjugate-symmetry. Motivated by the theoretical necessity of this property for the minimum-variance distortionless response (MVDR) beamformer and based on the proportionality of the AMF and MVDR solutions, we apply our approach to the problem of radar moving target detection from a stationary platform. The proposed unit-circle roots constrained AMF (UCRC-AMF) shows improved detection performance with limited secondary data compared to the conventional AMF. The improved performance of the UCRC-AMF is demonstrated using simulation examples and compared to the conventional AMF as well as the known-covariance case.

A general overlay/underlay analytic expression representing cognitive radio waveform

2007 International Waveform Diversity and Design Conference, 2007

Abstract Several studies have revealed that spectrum congestion is primarily due to the inefficie... more Abstract Several studies have revealed that spectrum congestion is primarily due to the inefficient use of spectrum versus unavailability. Cognitive radio (CR) and ultra wide band (UWB) technologies have been proposed as candidates to address this problem. Currently, a CR determines unused frequency bands and transmits overlay waveforms in these bands, while UWB transmits underlay waveforms that span the entire frequency band while coexisting with primary users. This suggests that most of the spectrum occupied by ...

Optimal identification of discrete multivariable systems from noisy impulse response data

[1991] Proceedings of the 30th IEEE Conference on Decision and Control

Abstract The authors consider the problem of identification of transfer function matrices of disc... more Abstract The authors consider the problem of identification of transfer function matrices of discrete-time multivariable systems. The proposed technique obtains an optimal approximation from the given measured impulse response data. It is assumed that the measured impulse response data correspond to a system with a strictly proper transfer function matrix. Based on the proposed theoretical basis, an efficient computational algorithm is developed and illustrated by means of several examples

Optimal design of 2-D IIR filters-strictly proper case

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992

Abstract The problem of optimal design of a class of two-dimensional (2-D) digital infinite impul... more Abstract The problem of optimal design of a class of two-dimensional (2-D) digital infinite impulse response (IIR) filters from spatial impulse response data is addressed. The denominator of the desired strictly proper 2-D filter is assumed to be separable. The filter coefficients are iteratively estimated by maintaining the t 2-norm of the error between the prescribed and the estimated spatial domain responses. The complete subspace orthogonal to the 2-D model fitting error is identified. It is shown that by appropriate choice of the ...

MVDR Beamformer Design by Imposing Unit Circle Roots Constraints for Uniform Linear Arrays

IEEE Transactions on Signal Processing, 2021

A 2004 paper had offered a theoretical proof that ideally the roots of the minimum variance disto... more A 2004 paper had offered a theoretical proof that ideally the roots of the minimum variance distortionless response (MVDR) beamformer array polynomial lie on the unit circle (UC). However, existing MVDR methods fail to exploit this fundamental property adequately. This paper proposes a new adaptive beamforming design via UC roots optimization for uniform linear arrays (ULA). The proposed method starts with the sample matrix inversion (SMI) roots and optimizes the MVDR criterion for each root separately by splitting the $N$-dimensional MVDR problem into multiple 1-D optimization problems. Conjugate symmetry is imposed on individual 1st-order factors of the MVDR polynomial during optimization that guarantees UC roots. The proposed UC Roots Constrained MVDR (UCRC-MVDR) method is non-iterative and has a closed-form solution. It also works well with limited number of snapshots. UCRC-MVDR is computationally efficient and parallelizable as all roots can be optimized concurrently. In extensive simulation studies, UCRC-MVDR exhibits consistently superior performance over existing MVDR approaches, and its performance is closer to the ideal clairvoyant case than existing MVDR methods.

An optimal method for identification of pole-zero transfer functions

[Proceedings] 1992 IEEE International Symposium on Circuits and Systems

Abstract An optimal algorithm for estimation of the parameters of rational transfer functions fro... more Abstract An optimal algorithm for estimation of the parameters of rational transfer functions from prescribed impulse response data is presented. The proposed method is based on the minimization of the l 2-norm of the true fitting error and is uniformly applicable for rational models with arbitrary numbers of poles and zeros. Existing methods either modify the true nonlinear error criterion in the theoretical derivation or require the transfer function model to have exactly one less number of zeros than poles. The multidimensional nonlinear error ...

A novel cyclic algorithm for maximum likelihood frequency estimation

IEEE International Conference on Systems Engineering, 1991

A new algorithm is presented for estimation of frequencies of narrow-band sources from noisy obse... more A new algorithm is presented for estimation of frequencies of narrow-band sources from noisy observation data. For Gaussianly distributed noise, the algorithm produces maximum likelihood estimates, otherwise least-squares estimates are obtained. The proposed algorithm is iterative and at each step of iteration, the Optimization is w.r.t. a single frequency only and hence, simple hardware/software (using FFT, e.g.) is sufficient for implementation. The performance of the algorithm has been compared with the theoretical Cramer-Rao bounds.

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Algorithm to overcome atmospheric phase errors in SAL data

Applied Optics, 2019

Synthetic aperture ladar is an emerging sensor technology providing high-resolution imagery of ta... more Synthetic aperture ladar is an emerging sensor technology providing high-resolution imagery of targets from long standoff ranges. Atmospheric turbulence corrupts the collected phase history data with spatially variant phase perturbations, impacting resolution and contrast of reconstructed imagery. We explore the efficacy of model-based reconstruction algorithms with model error corrections to mitigate the deleterious effects of atmospheric turbulence and restore image quality. We present results from model error correction techniques utilizing spatially invariant, spatially variant, and a model-based atmospheric phase error correction. We quantify the performance of all algorithms using an atmospheric ray-trace simulation.

An algorithm for pole-zero modeling and spectral analysis

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986

An explicit connection between fitting exponential models and pole-zero models to observed data i... more An explicit connection between fitting exponential models and pole-zero models to observed data is made. The fitting problem is formulated as a constrained nonlinear minimization problem. This problem is then solved using a simplified iterative algorithm. The algorithm is applied to simulated data, and the performance of the algorithm is compared to previous results.

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Improved Wideband DOA Estimation Using Modified TOPS (mTOPS) Algorithm

IEEE Signal Processing Letters, 2016

Test of orthogonality of projected subspaces (TOPS) estimates directions of arrival of wideband s... more Test of orthogonality of projected subspaces (TOPS) estimates directions of arrival of wideband sources by exploiting orthogonality between signal and noise subspaces in spectral domain. TOPS performs well at mid signal-to-noise ratio (SNR) range, but fares poorly at high SNR and noise-free cases. The TOPS pseudospectrum often exhibits spurious peaks at all SNR levels. This paper attempts to explain the cause of poor performance, and proposes suitable modifications to extend the effectiveness of TOPS from low SNR to noise-free case. The proposed modified-TOPS (mTOPS) achieves reduction in spurious peaks by incorporating signal-subspace projection instead of null-space projection used in TOPS. It also uses trace as performance metric, instead of loss of rank used in TOPS. The effectiveness of mTOPS has been studied via simulations.

Order recursive parametric bispectrum estimation

International Conference on Acoustics, Speech, and Signal Processing, Apr 14, 1991

Abstract Order-recursive computation of autoregressive (AR) parameters from cumulants is addresse... more Abstract Order-recursive computation of autoregressive (AR) parameters from cumulants is addressed. If the cumulant matrix is neither Toeplitz nor symmetric, it is shown that using the Frolenius-Schur block matrix inversion formula the inverse of the p-dimensional cumulant matrix can be updated from the (p-1)-dimensional inverse with O (p 2) operations. When compared to batch mode computation, the proposed algorithm reduces the computational requirement for order-recursive calculation of the AR parameters. When the cumulant ...

DFT preprocessing for high-resolution frequency estimation

Asilomar Conference on Signals, Systems and Computers, Oct 31, 1994

Abstract This work considers the use of the DFT of the autocorrelation (AC) matrix (DFT-of-AC) fo... more Abstract This work considers the use of the DFT of the autocorrelation (AC) matrix (DFT-of-AC) for extracting the signal and noise subspaces. It is shown that when DFT preprocessed data is incorporated within the frameworks of minimum-norm (MNM) or Prony&amp;#x27;s methods, improved high-resolution estimates are obtained. Furthermore, if the signal-subspace part of the DFT-of-AC vectors are used in place of eigenvectors in MNM, the high-resolution performance is further enhanced. Theoretical perturbation analysis of the DFT-based ...

Hyperspectral target detection using multiple platform cuing

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009

Hyperspectral imaging has been demonstrated to achieve unresolved object detection through use of... more Hyperspectral imaging has been demonstrated to achieve unresolved object detection through use of the spectral information. However, in many cases, these demonstrations have been in near ideal situations where the use of laboratory spectra with pristine data has lead to success. Complexities introduced in real-world situations such as a cluttered urban environment make successful detection challenging. One approach to improving performance is to use the synergistic effects of multiple sensors surveying a common area. These multiple sensors can be used to cue each other and enhance detection or tracking of objects. For maximum robustness, however one would want to minimize the complexity of processing algorithms such as those used to compensate for atmospheric and illumination effects. This paper investigates the limits of the use of spectra observed under one set of conditions to be used to detect an object under a different set of conditions. The results indicate good performance can be achieved across a reasonable range of illumination and viewing conditions.

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Design of 2-D recursive digital filters in the spatial domain

IEEE International Symposium on Circuits and Systems

Abstract A class of least-squares algorithms for designing two-dimensional digital filters from s... more Abstract A class of least-squares algorithms for designing two-dimensional digital filters from space-domain data is presented. The proposed algorithms iteratively estimate the filter coefficients by minimizing the true squared error between the given and the estimated space-domain responses. The algorithms are essentially generalizations of the optimal 1-D design algorithm given by AG Evans and R. Fischl (1973). The error criterion is simultaneously optimized with respect to the coefficients in both dimensions. Design algorithms are given ...

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