Hybrid Barankin–Weiss–Weinstein Bounds

Ren, Chengfang; Galy, Jérôme; Chaumette, Éric; Larzabal, Pascal; Renaux, Alexandre

doi:10.1109/lsp.2015.2457617

Cited by 12 publications

(14 citation statements)

References 24 publications

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“…This results in a "hybrid" lower bound for the estimation of the parameter vector θ. Such kind of lower bound has already been proposed in the literature [27]- [29]. In this paper, we propose to combine the (deterministic) Cramér-Rao bound [30], [31,Chap.…”

Section: B the Hybrid Cramér-rao-weiss-weinstein Bound (Hcrwwb)mentioning

confidence: 99%

A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points

Bacharach

Korso

Renaux

et al. 2019

IEEE Trans. Signal Process.

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Change-point estimation has received much attention in the literature as it plays a significant role in several signal processing applications. However, the study of the optimal estimation performance in such context is a difficult task since the unknown parameter vector of interest may contain both continuous and discrete parameters, namely the parameters associated with the noise distribution and the change-point locations. In this paper, we handle this by deriving a lower bound on the mean square error of these continuous and discrete parameters. Specifically, we propose a hybrid Cramér-Rao-Weiss-Weinstein bound and derive its associated closed-form expressions. Numerical simulationsassess the tightness of the proposed bound in the case of Gaussian and Poisson observations.

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Section: B the Hybrid Cramér-rao-weiss-weinstein Bound (Hcrwwb)mentioning

confidence: 99%

A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points

Bacharach

Korso

Renaux

et al. 2019

IEEE Trans. Signal Process.

Self Cite

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“…It has recently been shown in the literature [18] that eq. (4) can be bounded using the covariance inequality.…”

Section: Covariance Inequalitymentioning

confidence: 99%

Designing Sar Images Change-point Estimation Strategies Using an Mse Lower Bound

Mian

Bacharach

Ginolhac

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A growing problem in the remote sensing community concerns the estimation of change-points in a time series of Synthetic Aperture Radar (SAR) images. Although the methodologies of change-point estimation have already been investigated in the literature, there are, to the best of our knowledge, no study on the expected performance for the estimation of change-points in a Wishart distributed time series. This is mainly due to the fact that few results exist on change-point estimation performance in the mathematical literature: the classical central limit theorem does not apply and the classical Cramer-Rao Bound does not exist due to the discrete nature of the parameters. To fill this gap, this paper proposes to use a lower-bound on the Mean Square Error (MSE) with fewer regularity conditions. To this end, recent works on hybrid Cramer-Rao/Weiss-Weinstein bound have been adapted to the specific SAR problematic of interest. Since estimation strategies usually rely on a set of parameters which have to be set by the user, we show how the proposed lower bound allows performing an appropriate tuning. Moreover, the proposed bound is computationally efficient which enables an extensive analysis without a high computational cost.

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“…First, with the mindset of frequentists [47]- [49], we assume that the parameters of interest and the quantization threshold are deterministic unknown variables. Then, we consider a hybrid model [50]- [54], where the channel parameters are random and distributed according to a known probability distribution function, while the quantization offset is modeled as a deterministic unknown nuisance parameter. The hybrid approach is motivated by the fact that in various cases prior information about the channel is available at the receiver.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

Stein

Bar

Nossek

et al. 2018

IEEE Trans. Signal Process.

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In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with 1-bit output resolution and an unknown quantization threshold is considered. Singlecomparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by 2/π (−1.96 dB) when comparing to an ideal ∞-bit converter. Due to hardware imperfections, low-complexity 1-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

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Hybrid Barankin–Weiss–Weinstein Bounds

Cited by 12 publications

References 24 publications

A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points

A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points

Designing Sar Images Change-point Estimation Strategies Using an Mse Lower Bound

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

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