Jens Steinwandt

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High-resolution parameter estimation algorithms designed to exploit the prior knowledge about incident signals from strictly second-order (SO) noncircular (NC) sources allow for a lower estimation error and can resolve twice as many sources. In this paper, we derive the R \mathchar"702DD NC Standard ESPRIT and the R \mathchar"702DD NC Unitary ESPRIT(More)
Motivated by the performance of the direction finding algorithms based on the auxiliary vector filtering (AVF) method and the conjugate gradient (CG) method as well as the advantages of operating in beamspace (BS), we develop two novel direction finding algorithms for uniform linear arrays (ULAs) in the beamspace domain, which we refer to as the BS AVF and(More)
In this paper, we consider the problem of optimizing the transmit co-variance matrix for a multiple-input multiple-output (MIMO) Gaussian wiretap channel. The scenario of interest consists of a transmitter, a legitimate receiver, and multiple non-cooperating eavesdroppers that are all equipped with multiple antennas. Specifically, we design the transmit(More)
This paper presents a widely-linear (WL) distributed beamforming algorithm that takes advantage of strictly second-order (SO) non-circular source signals. We consider a single-antenna source-destination pair, which is assisted by multiple relays but suffers from strong interference. Assuming that perfect channel state information (CSI) is available, we(More)
We propose a non-data-aided adaptive beamforming algorithm based on Widely Linear (WL) processing techniques and the Auxiliary Vector Filtering (AVF) algorithm for non-circular signals, where only the steering vector of the desired user is known. The proposed Widely Linear Auxiliary Vector Filtering (WL-AVF) algorithm recursively updates the filter weights(More)
—In certain applications involving direction finding, a priori knowledge of a subset of the directions to be estimated is sometimes available. Existing knowledge-aided (KA) methods apply projection and polynomial rooting techniques to exploit this information in order to improve the estimation accuracy of the unknown signal directions. In this paper, a new(More)
Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. As a benchmark for these NC methods, we(More)
High-resolution parameter estimation algorithms designed to benefit from the presence of non-circular (NC) source signals allow for an increased identifiability and a lower estimation error. In this paper, we present a 1-D first-order performance analysis of the NC standard ESPRIT and NC Unitary ESPRIT estimation schemes for strictly second-order (SO)(More)
Recently, ESPRIT-based parameter estimation algorithms have been developed to exploit the structure of signals from strictly second-order (SO) non-circular (NC) sources. They achieve a higher estimation accuracy and can resolve up to twice as many sources. However, these NC methods assume that all the received signals are strictly non-circular. In this(More)
This paper presents a first-order analytical performance assessment of the 1-D non-circular (NC) Standard ESPRIT and the 1-D NC Unitary ESPRIT algorithms both using structured least squares (SLS) to solve the set of augmented shift invariance equations. These high-resolution parameter estimation algorithms were designed for strictly second-order (SO)(More)