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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… (More)

—For line spectrum estimation, we derive the maximum a posteriori probability estimator where prior knowledge of frequencies is modeled probabilistically. Since the spectrum is periodic, an appropriate distribution is the circular von Mises distribution that can parameterize the entire range of prior certainty of the frequencies. An efficient alternating… (More)

In certain Direction of Arrival (DOA) scenarios some of the sources are approximately known a priori. It is then desirable to be able to exploit this prior knowledge when estimating the DOAs of the unknown sources. In this paper we modify an estimator utilizing exact angular prior knowledge of some sources such that the estimator is able to exploit prior… (More)

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… (More)

The pulse spin-locking sequence is a common excitation sequence for magnetic resonance and nuclear quadrupole resonance signals, with the resulting measurement data being well modeled as a train of exponentially damped sinusoidals. In this paper, we derive an ESPRIT-based estimator for such signals, together with the corresponding Cramér-Rao lower bound.… (More)

In this article, we investigate the performance of the recently proposed Direction-Of-Arrival (DOA) estimator POWDER (Prior Orthogonally Weighted Direction Estima-toR). The method is exploiting a specific form of prior information, namely that some DOAs are known, as well as that the correlation state between some of the source signals are known. In such… (More)

The estimation of covariance matrices is an integral part of numerous signal processing applications. In many scenarios, there exists prior knowledge on the structure of the true covariance matrix; e.g., it might be known that the matrix is Toeplitz in addition to hermitian. Given the available data and such prior structural knowledge, estimates using the… (More)

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