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The robustness of adaptive beamforming is relate to the input signal-to-noise ratio (SNR). In order to further improve the performance of input SNR estimation, a modified method of input SNR estimation for robust adaptive beamforming is proposed. Comparatively accurate value of the input SNR can be obtained by the proposed method, especially when the input SNR exceeds 0 dB. When the proposed method...
Magnetic resonance imaging provides a superior soft tissue contrast and a noninvasive means for automatic diagnosis of tissue pathogenesis. However, like most imaging modalities, it suffers from a compromise between the achievable spatial resolution, scan time efficiency, and signal-to-noise-ratio. To address this difficulty, super-resolution techniques have been proposed to enhance the spatial resolution...
A distributed localization method has been proposed recently based on a novel concept of angle of departure (AOD) to estimate the angle information and achieve localization. Based on the AOD estimation, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna...
This paper aims at deriving a relationship between minimum mean square error (MMSE) based source separation and independent component analysis (ICA) based on the Kullback-Leibler divergence (KLD) for a linear noisy mixing model. Starting from a description of the demixing task and two well-known solutions, inverse mixing matrix and MMSE solution, we derive an analytic expression for the demixing matrix...
This paper considers the problem of constrained tracking the time-varying room impulse response of a source/microphone pair. The constraint which is used to improve the performance stems from the energy conservation that has to hold for real-world impulse responses. We consider three different recursive estimators and compare their performance with the recursive weighted least squares algorithm which...
Current automotive radar systems measure the distance, the relative velocity and the direction of objects in their environment. This information enables the car to support the driver. When objects cannot be completely separated by their distance or relative velocity, a two object direction of arrival estimator must normally be used to separate the objects. In this paper we take a close look at the...
This paper focuses on the optimization of the 2D geometry of sensor arrays for 2D direction-of-arrival (DOA) estimation. Such arrays can be used for radar imaging purposes. Due to the optimization, the number of array channels can be kept quite small, which reduces hardware costs, while highly accurate DOA estimation accuracy can be achieved. Therefore, we derive a very simple expression of the 2D...
This paper focuses on the estimation of the direction-of-arrival (DOA) of signals impinging on a linear sensor array. In contrast to conventional arrays, where the number of channels equals the number of sensors, we use tapered subarray structures. For this type of array, each channel consists of several sensor elements with different amplitude tapering. By this means, a pre-focussing can be achieved...
High-resolution subspace based direction-of-arrival (DOA) estimation requires a number of assumptions about the signal and antenna. In our application automotive radar, among many practical problems, signals are often correlated and the antenna is not calibrated. This difficult combination has been seldom addressed in the literature. In this paper, we study their simultaneous impact on DOA estimation,...
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