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- Wirfält, Petter, 1979-, et al.
(author)
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Optimal prior knowledge-based direction of arrival estimation
- 2012
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In: IET Signal Processing. - London : Institution of Engineering and Technology. - 1751-9675 .- 1751-9683. ; 6:8, s. 731-742
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Journal article (peer-reviewed)abstract
- In certain applications involving direction of arrival (DOA) estimation the operator may have a-priori information on some of the DOAs. This information could refer to a target known to be present at a certain position or to a reflection. In this study, the authors investigate a methodology for array processing that exploits the information on the known DOAs for estimating the unknown DOAs as accurately as possible. Algorithms are presented that can efficiently handle the case of both correlated and uncorrelated sources when the receiver is a uniform linear array. The authors find a major improvement in estimator accuracy in feasible scenarios, and they compare the estimator performance to the corresponding theoretical stochastic Cramer-Rao bounds as well as to the performance of other methods capable of exploiting such prior knowledge. In addition, real data from an ultra-sound array is applied to the investigated estimators.
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- Wirfält, Petter, 1979-, et al.
(author)
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Prior-Exploiting Direction-of-Arrival Algorithm For Partially Uncorrelated Source Signals
- 2013
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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : IEEE conference proceedings. - 9781479903566 ; , s. 3972-3976
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Conference paper (peer-reviewed)abstract
- In certain direction-of-arrival (DOA) estimation scenarios some of the source directions are known to the operator even before measurements are acquired. It is then undesirable to use regular DOA-algorithms which waste data-samples estimating the known directions. Additionally, in some applications it is known that the signals emanating from the known directions are uncorrelated with those coming from the unknown directions. In this article we present a novel algorithm which exploits the combination of such prior knowledge in a manner more efficient (in terms of accuracy) than any algorithm known to the authors. Through numerical Monte-Carlo simulations we show the estimator to attain the theoretical accuracy bound for significantly lower signal-to-noise ratios than current state-of-the-art methods. Additionally we show the proposed algorithm to treat the stricter problem of entirely uncorrelated emitters better than current state of the art.
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- Wirfält, Petter, 1979-, et al.
(author)
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Prior Knowledge-Based Direction of Arrival Estimation
- 2011
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In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing. - : IEEE. - 9781457705373 - 9781457705380 ; , s. 2540-2543
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Conference paper (peer-reviewed)abstract
- In a number of direction of arrival (DOA) estimation applications there exists prior knowledge about the sources whose bearings are to be determined. We study the case when this prior information concerns some of the source positions and their correlation state, which is a relevant case in, for example, RADAR scenarios where stationary objects exists in the regions of interest. Traditional DOA methods are not designed to exploit such information, and thus cannot obtain the highest theoretical accuracy. We present a method that can utilize in an asymptotically efficient manner both knowledge on some source positions and that the source signals are uncorrelated.
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- Wirfält, Petter, 1979-, et al.
(author)
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Subspace-based frequency estimation utilizing prior information
- 2011
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In: 2011 IEEE Statistical Signal Processing Workshop (SSP). - Piscataway, NJ : IEEE. - 9781457705694 ; , s. 533-536
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Conference paper (peer-reviewed)abstract
- In certain frequency estimation applications one or more of the underlyingfrequencies are known. For example, in rotary machines the known frequencymay be a strong network frequency masking important closely spacedfrequencies. Being able to include this information in the design of the estimator can be expected to improve the performance when estimating such closely spaced frequencies. We present a framework to include such priorinformation in a class of subspace-based estimators. Through Monte Carlo simulations and real-data applications we show the usefulness of our approach.
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