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- Gianelli, Christopher, et al.
(författare)
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One-Bit Compressive Sampling with Time-Varying Thresholds : Maximum Likelihood and the Cramer-Rao Bound
- 2016
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Ingår i: 2016, 50Th Asilomar Conference On Signals, Systems And Computers. - : IEEE COMPUTER SOC. - 9781538639542 ; , s. 399-403
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Konferensbidrag (refereegranskat)abstract
- This paper considers the problem of estimating the parameters of a noisy signal which has been quantized to one-bit via a time-varying thresholding operation. An expression for the Fisher information matrix (FIM) is derived for a generic deterministic signal parameterized by a vector beta when the noise is independent and identically distributed (i.i.d.) Gaussian with either known or unknown variance. The case of single sinusoidal parameter estimation is considered as a particular example, and the Cramer-Rao bounds (CRB) for amplitude, frequency, and phase estimators are computed for a variety of parameter values. A maximum likelihood (ML) estimator for the sinusoidal signal parameters is proposed, and its performance is compared with the CRB as a function of the number of observations.
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| 3. |
- Gianelli, Christopher, et al.
(författare)
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One-bit Compressive Sampling with Time-Varying Thresholds for Multiple Sinusoids
- 2017
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Ingår i: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). - : IEEE. - 9781538612514
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Konferensbidrag (refereegranskat)abstract
- Wide-band spectral sensing is a challenging task that will be required in future cognitive radio and radar applications. Recent research has shown that sampling using only one-bit of amplitude precision can be realized at an extremely high rate [1] in an affordable manner. In this work, one-bit sampling using time-varying thresholds is considered for line spectral estimation. The time-varying thresholds allow for amplitude estimation. A novel one-bit RELAX algorithm is developed for multi-tone parameter estimation. This algorithm is shown to have excellent performance via a numerical example.
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- Karlsson, Johan, et al.
(författare)
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Fast Missing-Data IAA With Application to Notched Spectrum SAR
- 2014
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Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 0018-9251 .- 1557-9603. ; 50:2, s. 959-971
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the spectral estimation method known as the iterative adaptive approach (IAA) has been shown to provide higher resolution and lower sidelobes than comparable spectral estimation methods. The computational complexity is higher than methods such as the periodogram (matched filter method). Fast algorithms have been developed that considerably reduce the computational complexity of IAA by using Toeplitz and Vandermonde structures. For the missing-data case, several of these structures are lost, and existing fast algorithms are only efficient when the number of available samples is small. In this work, we consider the case in which the number of missing samples is small. This allows us to use low-rank completion to transform the problem to the structured problem. We compare the computational speed of the algorithm with the state of the art and demonstrate the utility in a frequency-notched synthetic aperture radar imaging problem.
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| 6. |
- Li, Jian, et al.
(författare)
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On Parameter Identifiability of MIMO Radar
- 2007
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Ingår i: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 14:12, s. 968-971
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Tidskriftsartikel (refereegranskat)abstract
- A multi-input multi-output (MIMO) radar system, unlike a standard phased-array radar, can transmit multiple linearly independent probing signals via its antennas. We show herein that this waveform diversity enables the MIMO radar to significantly improve its parameter identifiability. Specifically, we show that the maximum number of targets that can be uniquely identified by the MIMO radar is up to Mt times that of its phased-array counterpart, where Mt is the number of transmit antennas.
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| 7. |
- Li, Jian, et al.
(författare)
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Range compression and waveform optimization for MIMO radar : a Cramér-Rao bound based study
- 2008
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Ingår i: IEEE Transactions on Signal Processing. - 1053-587X .- 1941-0476. ; 56:1, s. 218-232
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Tidskriftsartikel (refereegranskat)abstract
- A multi-input multi-output (MIMO) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. One of the common practices in radar has been range compression. We first address the question of "to compress or not to compress" by considering both the Cramer-Rao bound (CRB) and the sufficient statistic for parameter estimation. Next, we consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. We optimize the probing signal vector of a MIMO radar system by considering several design criteria, including minimizing the trace, determinant, and the largest eigenvalue of the CRB matrix. We also consider waveform optimization by minimizing the CRB of one of the target angles only or one of the target amplitudes only. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.
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