1. 
 Kaiser, Thomas, et al.
(författare)

When will smart antennas be ready for the market? : Part II  results
 2005

Ingår i: IEEE signal processing magazine (Print).  10535888. ; 22:6, s. 174176

Tidskriftsartikel (övrigt vetenskapligt)abstract
 The aim of this twopart forum is to shed more light on the future of smartantennas (SA) through discussions among a balanced group of experts from academia and industry. In part I, which appeared in the March 2005 issue of IEEE Signal Processing Magazine, each of the experts stated his own opinion after exchanging some thoughts by email. Then, a panel session took place at ICASSP'05 and a public poll followed. Now, in part II, the results are summarized by the experts. The central topic of the forum was the expectedmarket breakthrough of SA.


2. 
 Kristensson, Martin, et al.
(författare)

Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
 2001

Ingår i: IEEE Transactions on Signal Processing.  IEEE Signal Processing Society.  1053587X. ; 49:12, s. 29622974

Tidskriftsartikel (refereegranskat)abstract
 Subspacebased methods for parameter identification have received considerable attention in the literature. Starting with a scalarvalued process, it is well known that subspacebased identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a lowrank vectorvalued process. MUSIC and ESPRITlike estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markovlike procedure for this class of methods has been proposed. Herein, the Markovlike procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established


3. 
 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.  1053587X. ; 40:3, s. 590600

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 CramerRao 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.


4. 
 Ottersten, Björn, 1961, et al.
(författare)

DirectionofArrival Estimation for Wideband Signals using the ESPRIT Algorithm
 1990

Ingår i: IEEE Transactions on Acoustics, Speech and Signal Processing.  IEEE Signal Processing Society.  00963518. ; 38:2, s. 317327

Tidskriftsartikel (refereegranskat)abstract
 A novel directionofarrival 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 finitedimensional 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.


5. 
 Ottersten, Björn, 1961, et al.
(författare)

Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
 1993

Ingår i: Radar Array Processing.  Berlin ; New York : Springer Berlin/Heidelberg.  3540552243 (Berlin : acidfree paper)  9783540552246 (Berlin : acidfree paper)  0387552243 (New York : acidfree paper)  9780387552248 (New York : acidfree paper) ; s. 99151

Bokkapitel (övrigt vetenskapligt)abstract
 Sensor array signal processing deals with the problem of extracting information from a collection of measurements obtained from sensors distributed in space. The number of signals present is assumed to be finite, and each signal is parameterized by a finite number of parameters. Based on measurements of the array output, the objective is to estimate the signals and their parameters. This research area has attracted considerable interest for several years. A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array.


6. 
 Parkvall, Stefan, et al.
(författare)

Asynchronous nearfar resistant DSCDMA receivers without a priori synchronization
 1999

Ingår i: IEEE Transactions on Communications.  00906778. ; 47:1, s. 7888

Tidskriftsartikel (refereegranskat)abstract
 In this paper, several receivers for data demodulation in an asynchronous directsequence codedivision multiple access (DSCDMA) system operating without prior knowledge of the propagation delays are proposed and compared. Special attention is paid to the nearfar problem, and the proposed schemes are numerically shown to he nearfar resistant, The nearfar resistance is obtained by estimating the a priori unknown propagation delay using a subspacebased technique. Quantities obtained in the estimation procedure are used to design a filter used for suppression of interference, according to the minimum mean square error criterion. Either a decision feedback technique or a simple twostate Viterbi algorithm is subsequently used for the data demodulation in the uncoded case, By extending the trellis used in the Viterbi algorithm, error correcting coding is easily implemented.


7. 
 Parkvall, Stefan, et al.
(författare)

Sensitivity Analysis of Linear DSCDMA Detectors to Propagation Delay Estimation Errors
 1995

Ingår i: Proceedings IEEE Global Telecommunications Conference. ; s. 18721876

Konferensbidrag (refereegranskat)abstract
 An asynchronous directsequence code division multiple access (DSCDMA) communication system operating over an additive white Gaussian noise (AWGN) channel is considered. In many applications, the nearfar problem can be the limiting factor. Several nearfar resistant receivers have therefore been proposed (e.g., the decorrelating receiver). These receivers assume perfect knowledge of the propagation delay from all users to the receiver. In practice, the delays are estimated and therefore subject to estimation errors. The performance penalty these errors impose on linear detectors, especially the decorrelating detector, is evaluated


8. 
 ROY, R, et al.
(författare)

ESPRIT and Uniform Linear Arrays
 1989

Ingår i: Proceedings of the 33rd SPIE International Technical Symposium : Advanced Algorithms and Architectures for Signal Processing IV. ; s. 370380

Konferensbidrag (refereegranskat)abstract
 ESPRIT is a recently developed and patented technique for highresolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but otherwise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in highresolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in highresolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the rootMUSIC algorithm (applicable only to the case of uniform linear arrays of omnidirectional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.


9. 
 Stoica, Petre, et al.
(författare)

Comments on "MinNorm interpretations and consistency of MUSIC, MODE, and ML"
 1998

Ingår i: IEEE Transactions on Signal Processing.  1053587X. ; 46:8, s. 22622263

Tidskriftsartikel (refereegranskat)abstract
 The results and interpretations obtained in the above referred paper are shown to be well known or obvious. Additionally, corrections to some misleading statements in the aforementioned paper are presented.


10. 
 Stoica, P, et al.
(författare)

The evil of superefficiency
 1996

Ingår i: Signal Processing.  01651684. ; 55:1, s. 133136

Tidskriftsartikel (refereegranskat)abstract
 We discuss the intriguing notion of statistical superefficiency in a straightforward manner with a minimum of formality. We point out that for any given parameter estimator there exist other estimators which have a strictly lower asymptotic variance and hence are statistically more efficient than the former. In particular, if the former estimator was statistically efficient (in the sense that its asymptotic variance was equal to the CramerRao bound) then the latter estimators could be called ''superefficient''. Among others, the phenomenon of superefficiency implies that asymptotically there exists no uniformly minimumvariance parameter estimator.

