We consider spectrum sensing in a wideband cognitive radio system where 1-bit analog-to-digital converters (ADCs) are adopted at the radio frequency (RF) sensors. We focus on a practical scenario where multiple narrow...
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We consider spectrum sensing in a wideband cognitive radio system where 1-bit analog-to-digital converters (ADCs) are adopted at the radio frequency (RF) sensors. We focus on a practical scenario where multiple narrow-band radio systems coexist in the considered wide spectrum range and the RF sensor has no prior knowledge about those ambient radio systems. First, we use Van Vleck's arcsine law to analyze the impact of 1-bit sampling on performance of covariance matrix reconstruction. Second, we propose a novel 1-bit wideband spectrum sensing algorithm based on the subspace technique. We show that the proposed method exhibits near-zero false alarm (FA) while achieves the similar probability of detection (PD) performance as compared to conventional FFT-based and correlation-based wideband sensing methods.
We investigate a subaperturing technique for twodimensional (2D) transmit arrays within the context of multipleinput multiple-output (MIMO) radar. Specifically, we investigate the performance of transmit beamforming u...
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ISBN:
(纸本)9781479952939;9781479952946
We investigate a subaperturing technique for twodimensional (2D) transmit arrays within the context of multipleinput multiple-output (MIMO) radar. Specifically, we investigate the performance of transmit beamforming using fully overlapped subarrays of a 2D transmit array. As reported for linear array of antennas, this 2D transmit array exploits the advantages of the MIMO radar technology without sacrificing the coherent processing gain at the transmit side provided by the phasedarray concept. In order to achieve high coherent processing gain, a weight vector should be designed for each subarray to steer the transmit beam in certain 2D sector in space. this is achieved by solving a convex optimization problem that minimizes the difference between a desired transmit beampattern and the actual beampattern produced by the 2D array of antennas, under a constraint in terms of uniform power allocation across the transmit antennas.
this paper addresses state estimation where domain knowledge is represented by non-linear inequality constraints. To cope with non-Gaussian state distribution caused by the utilisation of domain knowledge, a truncated...
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ISBN:
(纸本)9781479952939;9781479952946
this paper addresses state estimation where domain knowledge is represented by non-linear inequality constraints. To cope with non-Gaussian state distribution caused by the utilisation of domain knowledge, a truncated unscented particle filter method is proposed in this paper. Different from other particle filtering schemes, a truncated unscented Kalman filter is adopted as the importance function for sampling new particles in the proposed truncated unscented particle scheme. Consequently more effective particles are generated and a better state estimation result is then obtained. the advantages of the proposed truncated unscented particle filter algorithm over the state-ofthe-art particle filters are demonstrated through Monte-Carlo simulations.
the well-known performance breakdown of subspace-based parameter estimation methods is usually attributed to a specific property of the technique, namely “subspace swap”. In this paper, we derive the lower bound for...
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the well-known performance breakdown of subspace-based parameter estimation methods is usually attributed to a specific property of the technique, namely “subspace swap”. In this paper, we derive the lower bound for the maximum likelihood ratio (LR), and use it as a simple data-based indicator to determine whether or not any set of estimates could be treated as a maximum likelihood (ML) set. We demonstrate that in those cases where the performance breakdown is subspace specific, this LR analysis provides reliable identification of whether or not “subspace swap” has actually occurred. We also demonstrate that by proper LR maximisation, we can extend the range of signal-to-noise ratio (SNR) values and/or number of data samples wherein accurate parameter estimates are produced. Yet, when the SNR and/or sample size falls below a certain limit for a given scenario, we show that ML estimation suffers from a discontinuity in the parameter estimates, a phenomenon that cannot be eliminated within the ML paradigm.
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