| 1. |
- Swartling, Mikael, et al.
(författare)
-
Calibration errors of uniform linear sensor arrays for DOA estimation : an analysis with SRP-PHAT
- 2011
-
Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 91:4, s. 1071-1075
-
Tidskriftsartikel (refereegranskat)abstract
- This article presents an analysis of the sensitivity of geometrical sensor errors in acoustic source localization using the well-established SRP-PHAT method. The array in this analysis is a uniform linear array and the intended source is human speech in the far field. Two major results are presented: inner-sensor geometrical errors in the linear array produce smaller localization errors than corresponding geometrical errors do in the two end-point sensors, and the localization error rises sharply for a total geometrical error exceeding the equivalence of the acoustic propagation distance of 2/3 of the sample time instance (approximately 3 cm at 8 kHz). The article also provides a mathematical and graphical explanation of the results.
|
|
| 2. |
- van de Beek, Jan-Jaap, et al.
(författare)
-
Three non-pilot based time- and frequency estimators for OFDM
- 2000
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 80:7, s. 1321-1334
-
Tidskriftsartikel (refereegranskat)abstract
- Time-domain maximum-likelihood (ML) estimators of time and frequency offsets are derived for three orthogonal frequency division multiplexing (OFDM) signal models: a pulse-shaped one-shot OFDM signal, a stream of multiple OFDM signals and an OFDM signal in a dispersive channel environment. We then develop structures to simplify their implementation. Simulation results show the relative performance and strengths of each of these three estimators.
|
|
| 3. |
- Anderson, Sören
(författare)
-
On Optimal Dimension Reduction for Sensor Array Signal Processing
- 1993
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 30:2, s. 245-256
-
Tidskriftsartikel (refereegranskat)abstract
- The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full dimension data into a lower dimensional space. Different approaches for selecting these transformations have been proposed. In this paper, a transformation matrix is derived that makes it possible to theoretically attain the full-dimension Cramér-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the estimates of the source localizations, using the lower-dimension data, as that achievable by using the full dimension data. Furthermore, a method is devised for designing the transformation matrix. Numerical examples, using this design method, are presented, where the achievable performance of the (optimal) Weighted Subspace Fitting method with full dimension data is compared to the performance obtained with reduced dimension data. The problem of estimating parameters of sinusoidal signals from noisy data is also addressed by a direct application of the results derived herein.
|
|
| 4. |
- Carlsson, B, et al.
(författare)
-
A NOTCH FILTER BASED ON RECURSIVE LEAST-SQUARES MODELING
- 1994
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 35:3, s. 231-239
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a notch filter based on recursive least-squares sinusoidal modelling. This gives analytical insight both into least-squares modelling of sine waves in noise and the use of constrained notch filters. Especially, the derived filter corresponds to a commonly used notch filter with constrained poles and zeros.
|
|
| 5. |
- Elias, Elizabeth, et al.
(författare)
-
Tree-structured IIR/FIR uniform-band and octave-band filter banks with very low-complexity analysis or synthesis filters
- 2003
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 83:9, s. 1997-2009
-
Tidskriftsartikel (refereegranskat)abstract
- This paper introduces new tree-structured uniform-band and octave-band digital filter banks (FBs). These FBs make use of half-band IIR filters in the analysis FBs and FIR filters in the synthesis FBs. The resulting FBs are asymmetric in the sense that the analysis FB has a very low arithmetic complexity whereas that in the synthesis FB is higher. However, compared with other asymmetric FBs, the proposed ones have in many cases a lower overall arithmetic complexity and delay. The proposed FBs have magnitude distortion but no phase distortion, further, the aliasing components are either zero (uniform-band case) or approximately zero (octave-band case). The FBs are designed using linear and nonlinear programming. Design examples are included demonstrating the properties of the proposed filters banks. ⌐ 2003 Published by Elsevier B.V.
|
|
| 6. |
- Granlund, Gösta
(författare)
-
The Complexity of Vision
- 1999
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 74:1, s. 101-126
-
Tidskriftsartikel (refereegranskat)abstract
- There is no indication that it will ever be possible to find some simple trick that miraculously solves most problems in vision. It turns out that the processing system must be able to implement a model structure, the complexity of which is directly related to the structural complexity of the problem under consideration in the external world. It has become increasingly apparent that Vision cannot be treated in isolation from the response generation, because a very high degree of integration is required between different levels of percepts and corresponding response primitives. The response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. In addition, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain. This article will focus on what are considered crucial problems in Vision for robotics for the future, rather than on the classical solutions today. It will discuss hierarchical architectures for combination of percept and response primitives. It will discuss the concept of combined percept-response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary flexibility and adaptivity. In consequence, it will be argued that invariances for the purpose of Vision are not abstractly geometrical, but derived from the percept-response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data. The question of object representation is central to the paper. Equivalence is established between the representations of response, geometry and time. Finally an integrated percept-response structure is proposed for flexible response control.
|
|
| 7. |
- Händel, Peter, 1962-, et al.
(författare)
-
Asymptotic noise gain of polynomial predictors
- 1997
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 62:2, s. 247-250
-
Tidskriftsartikel (refereegranskat)abstract
- Finite impulse response filters for the prediction of polynomial signals are considered. An expression for the asymptotic noise gain (as the filter length increases without bound) is derived. It is shown that the asymptotic noise gain only depends on the polynomial order - in particular, it is independent of the prediction horizon. It is also shown that the noise gain forms a non-increasing sequence for increasing filter lengths. Numerical results that lend support to the theoretical findings are included.
|
|
| 8. |
- Händel, Peter, 1962-, et al.
(författare)
-
Asymptotic variance of the AR spectral estimator for noisy sinusoidal data
- 1994
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 35:2, s. 131-139
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper the autoregressive (AR) spectral estimator is analyzed in the case of noisy sinusoidal data. An expression for the large-sample normalized variance is derived and studied in detail for increasing model order. In particular, a very simple formula is derived for the asymptotic (in both number of observed data and model order) normalized variance, which confirms a conjecture made by Sakai.
|
|
| 9. |
- Händel, Peter, 1962-
(författare)
-
High-order Yule-Walker estimation of the parameters of exponentially damped cisoids in noise
- 1993
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 32:3, s. 315-328
-
Tidskriftsartikel (refereegranskat)abstract
- An approach for the estimation of the frequencies and damping factors of exponentially damped cisoids (complex sinusoids) is presented. The method may be seen as an extension of the method of backward linear prediction and singular value decomposition of Kumaresan and Tufts to the second-order statistics domain. The proposed estimator is interpreted as a high-order Yule-Walker (HOYW) method using a data based covariance matrix. The HOYW method is analysed at high SNR where closed-form expressions for the accuracy of the estimates are derived. By Monte Carlo simulations the HOYW method is applied to data consisting of one and two damped cisoids in additive white noise. The simulation results are compared with the results using the Kumaresan and Tufts method, with the Cramer-Rao bound, and with the derived theoretical results. The method is not statistically efficient, but the comparison shows that the HOYW method outperforms the method of Kumaresan and Tufts in terms of accuracy versus algorithmic complexity and in terms of precision in the cases considered. Due to the above properties the method is suitable to provide fast initial estimates for nonlinear search methods.
|
|
| 10. |
- Händel, Peter, 1962-, et al.
(författare)
-
Performance analysis of a correlation based single tone frequency estimator
- 1995
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 44:2, s. 223-231
-
Tidskriftsartikel (refereegranskat)abstract
- This paper analyzes the frequency error variance of a low complexity single tone frequency estimator based on sample correlations of the input data. In the high SNR scenario it is analytically shown that the accuracy of a properly tuned algorithm is nearly optimal, i.e. nearly attains the Cramer-Rao lower bound. For low SNR the statistical efficiency of the algorithm is degraded, but it is analytically proven that for a large number of samples the error variance attains the lower bound for this class of estimators.
|
|
| 11. |
- Isaksson, Alf, et al.
(författare)
-
Inverse Glottal Filtering using a Parameterized Input Model
- 1989
-
Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 18:4, s. 435-445
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper computational algorithms for inverse glottal filtering are studied. The objective of inverse glottal filtering is to estimate the driving source. A good model for the glottal pulse is useful for, e.g., speech synthesis, speech recognition and speaker diagnostics. One common approach is to use a parameterized model of the input signal, i.e., the glottal pulses. The algorithm presented enables simultaneous estimation of the parameters of the input signal and the parameters of the system transfer function, the vocal tract model. The presentation here is restricted to transfer functions of all-pole type, i.e., AR-models. The method can be extended to handle zeros in the transfer function. The computational burden would, however, increase significantly. The algorithm uses efficient numerical methods, as, for instance, QR-factorization through Householder transformations.
|
|
| 12. |
- Jansson, Magnus, et al.
(författare)
-
A Linear Regression Approach to State-Space Subspace System Identification
- 1996
-
Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 52:2, s. 103-129
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, state-space subspace system identification (4SID) has been suggested as an alternative to the more traditional prediction error system identification. The aim of this paper is to analyze 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 describe 4SID methods within the standard framework of system identification and linear regression estimation. For example, this observation is used to compare different cost-functions which occur rather implicitly in the ordinary framework of 4SID. From the cost-functions, estimates of the extended observability matrix are derived and related to previous work. Based on the estimates of the observability matrix, the asymptotic properties of two pole estimators, namely the shift invariance method and a weighted subspace fitting method, are analyzed. Expressions for the asymptotic variances of the pole estimation error are given. From these expressions, difficulties in choosing user-specified parameters are pointed out. Furthermore, it is found that a row-weighting in the subspace estimation step does not affect the pole estimation error asymptotically.
|
|
| 13. |
- Koeck, Philip J B
(författare)
-
Quantization errors in averaged digitized data
- 2001
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 81:2, s. 345-356
-
Tidskriftsartikel (refereegranskat)abstract
- Analytic expressions which describe average quantization errors in digitized data with additive noise are derived. The magnitude of this error depends on the noise present in the analog signal, the bin-size (the difference between neighboring quantization levels) and also the signal itself. An iterative process, which corrects for these residual quantization errors after averaging, is proposed and tested in simulations. Alternatively a method for avoiding quantization errors during digitization of signals which will later be averaged is suggested.
|
|
| 14. |
- Larsson, Erik G., et al.
(författare)
-
Spectral estimation via adaptive filterbank methods : a unified analysis and a new algorithm
- 2002
-
Ingår i: Signal Processing. - Amsterdam, The Netherlands : Elsevier. - 0165-1684 .- 1872-7557. ; 82:12, s. 1991-2001
-
Tidskriftsartikel (refereegranskat)abstract
- The problem of estimating the amplitude spectrum of a signal is of interest in a number of applications ranging from radar imaging to time-series analysis. The so-called adaptive filterbank-based nonparametric spectral estimators have recently received renewed interest as potential solutions to this problem. In essence, the adaptive filterbank methods determine an estimate of the spectrum for a frequency of interest by computing a finite impulse response filter according to a certain criterion and fitting a sinusoid to the filtered data sequence. In this paper, we first analyze the asymptotic estimation accuracy of the amplitude spectrum for various filterbank estimators. Next, we propose a new adaptive filterbank estimator based on a minimum mean square error criterion. Numerical examples indicate that the new estimator can have a better resolution capability than previously known filterbank estimators.
|
|
| 15. |
- Ljung, Lennart, 1946-
(författare)
-
Aspects on the System Identification Problem
- 1982
-
Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 4:5-6, s. 445-456
-
Tidskriftsartikel (refereegranskat)abstract
- System Identification concerns the problem of building mathematical models of a dynamical system from measured input-output data. In this paper we examine some aspects of this problem. Typical sets of models are displayed and basic principles for fitting them to data are discussed. Asymptotic properties of the resulting model are also quoted.
|
|
| 16. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Subspace Identification from Closed Loop Data
- 1996
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 52:2, s. 209-215
-
Tidskriftsartikel (refereegranskat)abstract
- The so-called subspace methods for direct identification of linear models in state space form have drawn considerable interest recently. They have been found to work well in many cases but have one drawback — they do not yield consistent estimates for data collected under output feedback. The present paper points to the reasons for this. We stress how the basic idea is to focus on the estimation of the state-variable candidates — the k-step ahead output predictors. By recomputing these from a ‘non-parametric’ (or, rather, high order ARX) one-step ahead predictor model, closed loop data can be handled.
|
|
| 17. |
- Stoica, Petre, et al.
(författare)
-
The evil of superefficiency
- 1996
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 55:1, s. 133-136
-
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 Cramer-Rao bound) then the latter estimators could be called ''superefficient''. Among others, the phenomenon of superefficiency implies that asymptotically there exists no uniformly minimum-variance parameter estimator.
|
|
| 18. |
- Tichavsky, P., et al.
(författare)
-
Recursive estimation of frequencies and frequency rates of multiple cisoids in noise
- 1997
-
Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 58:2, s. 117-129
-
Tidskriftsartikel (refereegranskat)abstract
- A recursive algorithm for simultaneous estimation and tracking of instantaneous frequencies and instantaneous frequency rates-of-change for signals that consist of multiple narrow-band components in noise is proposed and studied. The algorithm recursively separates the signal to individual components and uses estimated phase differences for updating the instantaneous frequency and frequency rate of each component. The main advantages of the proposed algorithm over frequencies-only tracking algorithms known in literature include the zero asymptotic bias (zero tracking delay) in estimating of the instantaneous frequencies of linear FM (chirp) signals and more accurate tracking of frequencies that cross each other. Performance of the algorithm is studied by means of a linear filter approximation technique and derived results are compared with the appropriate (posterior) Cramer-Rao bound. Superior performance of the algorithm is illustrated by computer simulations.
|
|
| 19. |
- Trump, Tõnu, et al.
(författare)
-
Estimation of Nominal Direction of Arrival and Angular Spread Using an Array of Sensors
- 1996
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 50:1-2, s. 57-69
-
Tidskriftsartikel (refereegranskat)abstract
- The problem of estimating the nominal direction of arrival and angular spread of a source surrounded by a large number of local scatterers using an array of sensors is addressed. This type of propagation occurs frequently in, for example, mobile communications. The maximum likelihood estimator is considered and two computationally less complex estimators are also proposed. They are based on least-squares fits of the sample covariance to the theoretical covariance matrix derived from the measurement model. The performance of the least-squares-based algorithm is analyzed and based on this, an optimally weighted least-squares criterion is proposed. The weighted least-squares algorithm is shown to be asymptotically efficient. Results of numerical experiments are presented to indicate small sample behavior of the estimators. The nominal direction-of-arrival (DOA) estimates are compared with those provided by a standard subspace algorithm. Finally, the methods are applied to experimental array data to determine spread angles for non line of sight scenarios.
|
|
| 20. |
- Viberg, Mats
(författare)
-
Sensitivity of Parametric Direction Finding to Colored Noise Fields and Undermodeling
- 1993
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 34:2, s. 207-222
-
Tidskriftsartikel (refereegranskat)abstract
- A fundamental assumption for most direction finding algorithms is that the spatial correlation structure of the background noise (i.e., the correlation from sensor to sensor) is known to within a multiplicative scalar. In practive, this is often achieved by measuring the array covariance when no signals are present, a procedure which is unavoidably subjected to errors. The presence of undetected weak signals gives rise to similar perturbations. In this paper, the effect of such modeling errors on parametric estimation techniques is examined. First-order expressions for the mean square error (MSE) of the parameter estimates are derived for the deterministic and stochastic maximum likelihood methods and the weighted subspace fitting technique. The spatial noise correlation structures that lead to maximum performance loss are identified under different assumptions. In case of high signal-to-noise ratio, it is found that the MSE can be increased by a factor equal to the number of sensors in the array, as compared to spatially white noise. Furthermore, it is demonstrated that the presence of a relatively weak (− 15 dB) undetected signal can result in a large bias (≈1°) on the estimates of the other signal directions.
|
|
| 21. |
|
|
| 22. |
- Ambat, Sooraj K., et al.
(författare)
-
Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing
- 2014
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 97, s. 146-151
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data.
|
|
| 23. |
- Andersson, Kenneth, 1970-, et al.
(författare)
-
Prediction from off-grid samples using continuous normalized convolution
- 2007
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 87:3, s. 353-365
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel method for performing fast estimation of data samples on a desired output grid from samples on an irregularly sampled grid. The output signal is estimated using integration of signals over a neighbourhood employing a local model of the signal using discrete filters. The strength of the method is demonstrated in motion compensation examples by comparing to traditional techniques.
|
|
| 24. |
- Azizzadeh, Azad, et al.
(författare)
-
BER performance analysis of coarsely quantized uplink massive MIMO
- 2019
-
Ingår i: Signal Processing. - : ELSEVIER. - 0165-1684 .- 1872-7557. ; 161, s. 259-267
-
Tidskriftsartikel (refereegranskat)abstract
- Having lower quantization resolution, has been introduced in the literature, to reduce the power consumption of massive MIMO and millimeter wave MIMO systems. Here, we analyze the bit error rate (BER) performance of quantized uplink massive MIMO employing few-bit resolution ADCs. Considering ZF detection, we derive a signal-to-interference, quantization and noise ratio (SIQNR) to achieve an analytical BER approximation for coarsely quantized M-QAM massive MIMO systems, by using a linear quantization model. The proposed expression is a function of the quantization resolution in bits. We further numerically investigate the effects of different quantization levels, from 1-bit to 4-bits, on the BER of three modulation types QPSK, 16-QAM, and 64-QAM. The uniform and non-uniform quantizers are employed in our simulation. Monte Carlo simulation results reveal that our approximate formula gives a tight upper bound on the BER performance of b-bit resolution quantized systems using non-uniform quantizers, whereas the use of uniform quantizers cause a lower performance. We also found a small BER performance degradation in coarsely quantized systems, for example 2-3 bits QPSK and 3-4 bits 16-QAM, compared to the full-precision (unquantized) case. However, this performance degradation can be compensated by increasing the number of antennas at the BS. (C) 2019 Published by Elsevier B.V.
|
|
| 25. |
- Babu, Prabhu, et al.
(författare)
-
Connection between SPICE and Square-Root LASSO for sparse parameter estimation
- 2014
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 95, s. 10-14
-
Tidskriftsartikel (refereegranskat)abstract
- In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.
|
|
