Analytical investigation of two-object DOA estimation

Häcker, P.; Yang, Bin

doi:10.1109/wsa.2011.5741939

Cited by 1 publication

(1 citation statement)

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“…The sample starving scenario occurs with highly non-stationary environments such as embedded radars (in automotive, jetfighter or missile) where the stationary duration is very small. In the case of automotive radars, asymptotic performance of DoA estimation has been analyzed in [17] and [18,19] provide a good overview of techniques that can practically be used in this case. Whenever the number of snapshots becomes less than the number of sources, subspace-based methods are no longer appropriate since the signal subspace cannot be retrieved.…”

Section: Introductionmentioning

confidence: 99%

Approximate maximum likelihood estimation of two closely spaced sources

2014

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International audienceThe performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

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Section: Introductionmentioning

confidence: 99%