1.
Viberg, Mats, et al.
(författare)
Array Processing in Correlated Noise Fields Using Instrumental Variables and Subspace Fitting
1992
Ingår i: The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers. - : IEEE. - 0818631600 ; , s. 1147-1151
Konferensbidrag (refereegranskat) abstract
An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region.
2.
Ottersten, Björn, 1961-, et al.
(författare)
Performance Analysis of the Total Least Squares ESPRIT Algorithm
1991
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:5, s. 1122-1135
Tidskriftsartikel (refereegranskat) abstract
The asymptotic distribution of the estimation error for the total least squares (TLS) version of ESPRIT is derived. The application to a uniform linear array is treated in some detail, and a generalization of ESPRIT to include row weighting is discussed. The Cramer-Rao bound (CRB) for the ESPRIT problem formulation is derived and found to coincide with the asymptotic variance of the TLS ESPRIT estimates through numerical examples. A comparison of this method to least squares ESPRIT, MUSIC, and Root-MUSIC as well as to the CRB for a calibrated array is also presented. TLS ESPRIT is found to be competitive with the other methods, and the performance is close to the calibrated CRB for many cases of practical interest. For highly correlated signals, however, the performance deviates significantly from the calibrated CRB. Simulations are included to illustrate the applicability of the theoretical results to a finite number of data.
3.
Viberg, Mats, et al.
(författare)
Sensor Array Processing Based on Subspace Fitting
1991
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:5, s. 1110-1121
Tidskriftsartikel (refereegranskat) abstract
Algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem. The methods considered are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace method. These methods are formulated in a subspace-fitting-based framework, which provides insight into their algebraic and asymptotic relations. It is shown that by introducing a specific weighting matrix, the multidimensional signal subspace method can achieve the same asymptotic properties as the ML method. The asymptotic distribution of the estimation error is derived for a general subspace weighting, and the weighting that provides minimum variance estimates is identified. The resulting optimal technique is termed the weighted subspace fitting (WSF) method. Numerical examples indicate that the asymptotic variance of the WSF estimates coincides with the Cramer-Rao bound. The performance improvement compared to the other techniques is found to be most prominent for highly correlated signals.
4.
Ottersten, Björn, 1961-, et al.
(författare)
Asymptotic Robustness of Sensor Arrary Processing Methods
1990
Ingår i: Proceedings of the 1990 International Conference on Acoustics, Speech and Signal Processing. - Linköping : IEEE Signal Processing Society. ; , s. 2635-2638
Konferensbidrag (refereegranskat) abstract
Methods for estimating the parameters of narrowband signals arriving at an array of sensors are analyzed. Asymptotic results for several estimators have recently appeared in the literature. With few exceptions, the previous analysis requires the incident signal waveforms to be Gaussian random variables. These results are shown to be valid under much more general conditions, i.e. the actual distribution of the signal waveforms does not affect the asymptotic properties of the parameter.
5.
Viberg, Mats, et al.
(författare)
Source Localization in the Presence of Model Uncertainties
1992
Ingår i: Proceedings of the 2nd Workshop on Adaptive Algorithms in Communications.
Konferensbidrag (refereegranskat) abstract
In many signal processing applications high accuracy signal parameter estimation from sensor array data is a significant problem. Much of the recent work in array processing has focused on methods for high-resolution location estimation. Model based estimation techniques requre accurate knowledge of the so-called array manifold. In practise, the array response is often determined by measuring the array response when only one emitter is radiating and the signal parameters of which are allowed to vary in a known way. This paper addresses some of the practical issues that arise in generating the so-called array manifold from a finite collection of caibration vectors. For high-resolution signal parameter estimation techniques to be successful, the interpolated array manifold has to satisfy certain smoothness conditions. A paradigm for generating an array model from noise corrupted calibration vectors is developed. The key idea is to use a local parametric model of the sensor responses. The potential improvement using the suggested scheme rather than an ideal array model is demonstrated on real data collected from a full-scale hydro-acoustic array.
6.
Ottersten, Björn, 1961-, et al.
(författare)
Direction-of-Arrival Estimation for Wideband Signals using the ESPRIT Algorithm
1990
Ingår i: IEEE Transactions on Acoustics, Speech and Signal Processing. - : IEEE Signal Processing Society. - 0096-3518. ; 38:2, s. 317-327
Tidskriftsartikel (refereegranskat) abstract
A novel direction-of-arrival estimation algorithm is proposed that applies to wideband emitter signals. A sensor array with a translation invariance structure is assumed, and an extension of the ESPRIT algorithm for narrowband emitter signals is obtained. The emitter signals are modeled as the stationary output of a finite-dimensional linear system driven by white noise. The array response to a unit impulse from a given direction is represented as the impulse response of a linear system. The measured data from the sensor array can then be seen as the output of a multidimensional linear system driven by white noise sources and corrupted by additive noise. The emitter signals and the array output are characterized by the modes of the linear system. The ESPRIT algorithm is applied at the poles of the system, the power of the signals sharing the pole is captured, and the effect of noise is reduced. The algorithm requires no knowledge, storage, or search of the array manifold, as opposed to wideband extensions of the MUSIC algorithm. This results in a computationally efficient algorithm that is insensitive to array perturbations. Simulations are presented comparing the wideband and ESPRIT algorithm to the modal signal subspace method and the coherent signal subspace method.
7.
Viberg, Mats, et al.
(författare)
Detection and Estimation in Sensor Arrays using Weighted Subspace Fitting
1991
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:11, s. 2436-2449
Tidskriftsartikel (refereegranskat) abstract
The problem of signal parameter estimation of narrowband emitter signals impinging on an array of sensors is addressed. A multidimensional estimation procedure that applies to arbitrary array structures and signal correlation is proposed. The method is based on the recently introduced weighted subspace fitting (WSF) criterion and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton-type method is presented for solving the multidimensional WSF and maximum-likelihood optimization problems. The global and local properties of the search procedure are investigated through computer simulations. Most methods require knowledge of the number of coherent/noncoherent signals present. A scheme for consistently estimating this is proposed based on an asymptotic analysis of the WSF cost function. The performance of the detection scheme is also investigated through simulations.
8.
Wahlberg, Bo, et al.
(författare)
4SID Linear Regression
1994
Ingår i: Proc. IEEE 33rd Conf.on Decision and Control. - : IEEE conference proceedings. - 0780319680 ; , s. 2858-2863
Konferensbidrag (refereegranskat) abstract
State-space subspace system identification (4SID) has been suggested as an alternative to more traditional prediction error system identification, such as ARX least squares estimation. The aim of this note is to analyse the connections between these two different approaches to system identification. The conclusion is that 4SID can be viewed as a linear regression multistep ahead prediction error method, with certain rank constraints. This allows us to analyse 4SID methods within the standard framework of system identification and linear regression estimation. For example, it is shown that ARX models have nice properties in terms of 4SID identification. From a linear regression model, estimates of the extended observability matrix are found. Results from an asymptotic analysis are presented, i.e. explicit formulas for the asymptotic variances of the pole estimation error are given. From these expressions, some difficulties in choosing user specified parameters are pointed out in an example.
9.
Gustavsson, Jan-Olof, et al.
(författare)
A simultaneous maximum likelihood estimator based on a generalized matched filter
1994
Ingår i: ICASSP-94. - Piscataway, NJ : IEEE Communications Society. - 0780317750 ; , s. 481-484
Konferensbidrag (refereegranskat) abstract
The paper discusses parameter estimation and detection in Laplace distributed noise. The received signal is modeled as r(·)=As(·,&thetas;)+n(·), where A is an unknown amplitude, &thetas; is the parameter vector to be estimated and n(·) is independent Laplace distributed noise. The simultaneous maximum likelihood estimator of (A,&thetas;) is derived. The derived estimator is based on a combination of a weighted median filter [Astola and Nuevo, 1992] and a generalized form of the ordinary matched filter [Gustavsson and Borjesson, 1992]. Examples of performance for four different detectors are given for a case of binary detection, when the amplitude A or the signal shape s(·,&thetas;) are varied. Simulations indicate that the performance of detectors based on the generalized matched filter is not particularly dependent on either the estimate of the amplitude A or the signal shape
10.
Ottersten, Björn, 1961-, et al.
(författare)
Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
1992
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 40:3, s. 590-600
Tidskriftsartikel (refereegranskat) abstract
It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance. The asymptotic analysis of the maximum likelihood (ML) and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e. independent of the actual signal waveforms. Conclusions concerning the modeling aspect of the sensor array problem are drawn.
