| 1. |
- Ahlberg, Jörgen, et al.
(författare)
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Ground Target Recognition in a Query-Based Multi-Sensor Information System
- 2006
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Rapport (övrigt vetenskapligt)abstract
- We present a system covering the complete process for automatic ground targetrecognition, from sensor data to the user interface, i.e., from low level imageprocessing to high level situation analysis. The system is based on a query languageand a query processor, and includes target detection, target recognition,data fusion, presentation and situation analysis. This paper focuses on targetrecognition and its interaction with the query processor. The target recognitionis executed in sensor nodes, each containing a sensor and the corresponding signal/image processing algorithms. New sensors and algorithms are easily addedto the system. The processing of sensor data is performed in two steps; attributeestimation and matching. First, several attributes, like orientation and dimensions,are estimated from the (unknown but detected) targets. These estimatesare used to select the models of interest in a matching step, where the targetis matched with a number of target models. Several methods and sensor datatypes are used in both steps, and data is fused after each step. Experimentshave been performed using sensor data from laser radar, thermal and visualcameras. Promising results are reported, demonstrating the capabilities of thetarget recognition algorithms, the advantages of the two-level data fusion andthe query-based system.
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| 2. |
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| 3. |
- Enqvist, Martin, 1976-, et al.
(författare)
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Identification of Wiener System with Monotonous Nonlinearity
- 2006
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Ingår i: Proceedings of the 14th IFAC Symposium on System Identification. - 978-3-902661-02-9 ; s. 166-171
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Konferensbidrag (refereegranskat)abstract
- A Wiener system is composed of a linear dynamic subsystem followedby a static nonlinearity. It is well known in the literature that the identifcationof the linear subsystem of a Wiener system can be separated from that of theoutput nonlinearity, if the input signal is a Gaussian noise. In order to deal withthe non Gaussian input case, two new algorithms are proposed in this paper fordirect identifcation of the linear subsystem, regardless of any parametrization ofthe output nonlinearity. The essential assumption required in this paper is thestrict monotonousness of the output nonlinearity.
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| 4. |
- Enqvist, Martin, 1976-, et al.
(författare)
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Linear Models of Nonlinear FIR Systems with Gaussian Inputs
- 2003
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Ingår i: Proceedings of the 13th IFAC Symposium on System Identification. - 9780080437095 ; s. 1910-1915
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Konferensbidrag (refereegranskat)abstract
- We present a result that can be viewed as a generalization of Bussgang's classical theorem about static nonlinearities with Gaussian inputs. This result is used to characterize the best linear approximation of a nonlinear finite impulse response (NFIR) system with a Gaussian input. The best linear approximation is here defined as the causal and stable LTI system that minimizes the mean-square error. Furthermore, we discuss how this characterization can be used for structure identification and for identification of generalized Hammerstein and Wiener systems.
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| 5. |
- Forssell, U, et al.
(författare)
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A projection method for closed-loop identification
- 2000
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Ingår i: IEEE Transactions on Automatic Control. - 0018-9286. ; 45:11, s. 2101-2106
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Tidskriftsartikel (refereegranskat)abstract
- A new method for closed-loop identification that allows fitting the model to the data with arbitrary frequency weighting is described and analyzed. Just as the direct method, this new method is applicable to systems with arbitrary feedback mechanisms. This is in contrast to other methods, such as the indirect method and the two-stage method, that assume linear feedback. The finite sample behavior of the proposed method is illustrated in a simulation study.
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| 6. |
- Forssell, U., et al.
(författare)
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Identification of unstable systems using output error and Box-Jenkins model structures
- 2000
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Ingår i: IEEE Transactions on Automatic Control. ; 45:1, s. 131-147
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Tidskriftsartikel (refereegranskat)abstract
- It is well known that the output error and Box-Jenkins model structures cannot be used for prediction error identification of unstable systems. The reason for this is that the predictors in this case generically will be unstable. Typically, this problem is handled by projecting the parameter vector onto the region of stability, which gives erroneous results when the underlying system is unstable. The main contribution of this work is that we derive modified, but asymptotically equivalent, versions of these model structures that can also be applied in the case of unstable systems
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| 7. |
- Gillberg, Jonas, et al.
(författare)
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Frequency-Domain Identification of Continuous-Time ARMA Models from Non-Uniformly Sampled Data
- 2005
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Rapport (övrigt vetenskapligt)abstract
- This paper treats direct identification of continuous-time autoregressive moving average(CARMA) time-series models. The main result is a method forestimating the continuous-time power spectral density fromnon-uniformly sampled data. It is based on the interpolation(smoothing) using the Kalman filter. A deeper analysis is alsocarried out for the case of uniformly sampled data. This analysisprovides a basis for proceeding with the non-uniform case.Numerical examples illustrating the performance of the method arealso provided both, for spectral and subsequent parameterestimation.
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| 8. |
- Gustafsson, Fredrik, et al.
(författare)
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Signal processing : exercises
- 2010
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Bok (övrigt vetenskapligt)abstract
- This book provides signal processing exercises and can with advantage be used together with the text book Signal Processing by Fredrik Gustafsson, Lennart Ljung and Mille Millnert. The chapters of the books are aligned, which means that there are matching exercises to each theory chapter. The first part of the book treats classical digital signal processing based on transforms and filters, while model based digital processing is in focus in the second part. Some exercises are more theoretical and solved by hand, while others are intended for Matlab on a computer. The book material is inspired by real problems, and so are the exercises. This is emphasized by the use of data sets, both simulated and real. Most exercises have complete solutions, and a section with hints provides guidance to some exercises. Selected exercises also result in a Matlab function corresponding to specific signal processing algorithms. These functions are used to solve other exercises. Thereby, the reader gradually build up a signal processing toolbox during the studies of the material. The book homepage contains more information and links to access the matlab functions, data sets and examples used in the book. Main book Signal Processing
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| 9. |
- Hagenblad, Anna, et al.
(författare)
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Maximum Likelihood Identification of Wiener Models
- 2009
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Rapport (övrigt vetenskapligt)abstract
- The Wiener model is a block oriented model having a linear dynamicsystem followed by a static nonlinearity.The dominating approachto estimate the components of this model has been to minimize theerror between the simulated and the measured outputs. We show thatthis will in general lead to biased estimates if there is otherdisturbances present than measurement noise. The implications ofBussgang´s theorem in this context are also discussed. For the casewith general disturbances we derive the Maximum Likelihood methodand show how it can be efficiently implemented. Comparisons betweenthis new algorithm and the traditional approach confirm that the newmethod is unbiased and also has superior accuracy.
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| 10. |
- Hu, Xiao-Li, et al.
(författare)
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A Basic Convergence Result for Particle Filtering
- 2007
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Rapport (övrigt vetenskapligt)abstract
- The basic nonlinear filtering problem for dynamical systems is considered. Approximating the optimal filter estimate by particle filter methods has become perhaps the most common and useful method in recent years. Many variants of particle filters have been suggested, and there is an extensive literature on the theoretical aspects of the quality of the approximation. Still, a clear cut result that the approximate solution, for unbounded functions, converges to the true optimal estimate as the number of particles tends to infinity seems to be lacking. It is the purpose of this contribution to give such a basic convergence result.
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