Threshold region performance of maximum likelihood direction of arrival estimators

Athley, Fredrik

doi:10.1109/tsp.2005.843717

Cited by 147 publications

(182 citation statements)

References 36 publications

Supporting

Mentioning

177

Contrasting

Unclassified

Order By: Relevance

“…We also observe the well known threshold effect [12] of the estimator variance when the SNR becomes weak (approximatively 5 dB in this case). This phenomena due to outliers gives the validity domain in term of efficiency of this asymptotic analysis (see [13] for more details concerning the CML threshold prediction).…”

Section: A Efficiencymentioning

confidence: 99%

On the High-SNR Conditional Maximum-Likelihood Estimator Full Statistical Characterization

Renaux

Forster

Chaumette

et al. 2006

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

In the field of asymptotic performance characterization of Conditional Maximum Likelihood (CML) estimator, asymptotic generally refers to either the number of samples or the Signal to Noise Ratio (SNR) value. The first case has been already fully characterized although the second case has been only partially investigated. Therefore, this correspondence aims to provide a sound proof of a result, i.e. asymptotic (in SNR) Gaussianity and efficiency of the CML estimator in the multiple parameters case, generally regarded as trivial but not so far demonstrated. Index TermsMaximum Likelihood, statistical efficiency, high Signal to Noise Ratio, array processing.

show abstract

Section: A Efficiencymentioning

confidence: 99%

On the High-SNR Conditional Maximum-Likelihood Estimator Full Statistical Characterization

Renaux

Forster

Chaumette

et al. 2006

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…As the distance between the first two antennas is half of the wavelength, the uniqueness of the DOA estimation is guaranteed. The missing redundancy leads to a high positional variance (Athley, 2005) and thus to an improved accuracy of the single target estimation compared to more conservative four antenna arrays.…”

Section: Signal Modelsmentioning

confidence: 99%

“…Yet, there are few papers which deal with the single snapshot case. There was a successful examination of the two classical ML algorithms Boorstyn, 1974, 1976;Athley, 2005). However, to the knowledge of the authors, there is still no comparative study about which DOA algorithm to choose in the single snapshot case, which is what this paper is about.…”

Section: Introductionmentioning

confidence: 97%

Single snapshot DOA estimation

Häcker

Yang

2010

Adv. Radio Sci.

View full text Add to dashboard Cite

Abstract. In array signal processing, direction of arrival (DOA) estimation has been studied for decades. Many algorithms have been proposed and their performance has been studied thoroughly. Yet, most of these works are focused on the asymptotic case of a large number of snapshots. In automotive radar applications like driver assistance systems, however, only a small number of snapshots of the radar sensor array or, in the worst case, a single snapshot is available for DOA estimation.In this paper, we investigate and compare different DOA estimators with respect to their single snapshot performance. The main focus is on the estimation accuracy and the angular resolution in multi-target scenarios including difficult situations like correlated targets and large target power differences. We will show that some algorithms lose their ability to resolve targets or do not work properly at all. Other sophisticated algorithms do not show a superior performance as expected. It turns out that the deterministic maximum likelihood estimator is a good choice under these hard conditions.

show abstract

“…However, it is well known that for small sample sizes or weak signals, the MLE suffers from a "breakdown phenomena" in its performance ( [1], [5], [25] for example). In more detail, below a certain threshold of either the SNR or of the number of samples, the MLE error increases rapidly, starting to significantly deviate from the Cramer-Rao lower bound.…”

Section: A Model Selection and The Mle Breakdown Phenomenamentioning

confidence: 99%

“…We note that although various papers discuss the MLE breakdown phenomena (see [1], [5], [13], [25] for example), to the best of our knowledge, our analysis is the first one to provide a simple explicit expression for its location, Eq. (13).…”

Section: If the Signal Strength Satisfiesmentioning

confidence: 99%

Parametric joint detection-estimation of the number of sources in array processing

Arkind

Nadler

2010

2010 IEEE Sensor Array and Multichannel Signal Processing Workshop

View full text Add to dashboard Cite

Detection of the number of signals and estimation of their directions of arrival (DOAs) are fundamental problems in signal array processing. In this paper we present three main contributions to this problem.First, we show that there is an explicit relation between model selection and the breakdown phenomena of the Maximum Likelihood estimator (MLE). Second, we provide a simple formula for the location of the breakdown of the MLE, using tools from extreme value theory. This gives an explicit formula for the signal strength required for reliable detection. Third, we apply these results and propose a new joint detection-estimation algorithm with state-of-the-art performance. We present computer simulations that demonstrate the performance of our algorithm, compared to other popular methods. Index Termsjoint detection-estimation, statistical hypothesis tests, extreme value theory, MLE breakdown phenomena.

show abstract

Threshold region performance of maximum likelihood direction of arrival estimators

Cited by 147 publications

References 36 publications

On the High-SNR Conditional Maximum-Likelihood Estimator Full Statistical Characterization

On the High-SNR Conditional Maximum-Likelihood Estimator Full Statistical Characterization

Single snapshot DOA estimation

Parametric joint detection-estimation of the number of sources in array processing

Contact Info

Product

Resources

About