Bayesian approach for joint estimation of phase noise and channel in orthogonal frequency division multiplexing system

Jose, Renu; Hari, K.V.S.

doi:10.1049/iet-spr.2012.0381

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…For X t k and θ i k are not independent, a joint processing method considering the joint posterior mixed probability density [27][28][29][30][31][32] rather than the common posterior density…”

Section: Jie In MD Systemsmentioning

confidence: 99%

“…Through the track management technique such as hypothesis testing to prune and merge the temporary tracks the proposed scheme can be generalised. Remark 3: Recently, JDE based on a generalised Bayes risk in single-detection systems as solution has been proposed and paid much attention [27][28][29], which is presented as follows:…”

Section: Jie Bayes Risk For Multi-target Tracking In MD Systemsmentioning

confidence: 99%

“…For

X_{t}^{k}

and

{bold-italicθ}_{i}^{k}

are not independent, a joint processing method considering the joint posterior mixed probability density

P false[()(, X_{t}^{k}, {bold-italicθ}_{i}^{k}) falsefalse| Y false(k false) false]

[27–32] rather than the common posterior density

P (}{, false(X_{t}^{k} falsefalse| Y false(k false) false))

provides more promising result. Theoretically, for the mixed probability density

P (}{, false(X_{t}^{k}, θ_{t}^{k} falsefalse| Y false(k false) false))

, it is better to achieve the final goal directly, rather than to detour of marginalising out the identification

θ_{t}^{k}

and then making approximation again to reach the final goal based on the estimated density.…”

Section: Jie In MD Systemsmentioning

confidence: 99%

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JIE for a class of MD systems

Feng

Liang

Jiao

et al. 2017

IET signal process.

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In multi-detection (MD) systems including over-the-horizon radar (OTHR) systems, the forward-based receivers systems and passive coherent location systems, resolvable multiple detections of one target due to the multi-path propagation will be received, in which both identification (including measurement association and propagation mode/transmitting origin identification) and estimation (including path-conditional state estimation and multi-path track fusion) are deeply coupled and affect each other. Up to present, all corresponding methods fall into the scope of sequential identification orientated or estimation orientated processing, and hence the solutions are not satisfactory. Here the joint identification and estimation scheme for the MD systems (MD-JIE) is developed based on the generalized Bayes risk considering both identification and estimation performance. The likelihood-ratio function and the conditional probability density function as the identification cost and estimation cost are recursively calculated, respectively. Taking the data-association constraints into consideration, the proposed MD-JIE scheme is implemented via online constrained optimization technology. Compared to the identification-thenestimation (ITE) method and the estimation-then-identification (ETI) method, the proposed MD-JIE method prevails in the joint performance measure with the higher correct identification rate than the ETI method and the smaller root MSE than the ITE method. Besides, the robustness of the proposed method is verified.

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Section: Jie In MD Systemsmentioning

confidence: 99%

Section: Jie Bayes Risk For Multi-target Tracking In MD Systemsmentioning

confidence: 99%

“…For

X_{t}^{k}

and

{bold-italicθ}_{i}^{k}

are not independent, a joint processing method considering the joint posterior mixed probability density

P false[()(, X_{t}^{k}, {bold-italicθ}_{i}^{k}) falsefalse| Y false(k false) false]

[27–32] rather than the common posterior density

P (}{, false(X_{t}^{k} falsefalse| Y false(k false) false))

provides more promising result. Theoretically, for the mixed probability density

P (}{, false(X_{t}^{k}, θ_{t}^{k} falsefalse| Y false(k false) false))

, it is better to achieve the final goal directly, rather than to detour of marginalising out the identification

θ_{t}^{k}

and then making approximation again to reach the final goal based on the estimated density.…”

Section: Jie In MD Systemsmentioning

confidence: 99%

See 1 more Smart Citation

JIE for a class of MD systems

Feng

Liang

Jiao

et al. 2017

IET signal process.

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show abstract

“…CRLB for joint estimation of channel in OFDM-based communication system channel estimator is derived in [5,11]. The CRLB for estimation techniques including random parameters are derived using Bayesian approach with prior statistical information and is called Bayesian CRLB [12,13]. For estimations involving both deterministic and random parameters, the most suitable CRLBs are found to be hybrid CRLBs (HCRLBs) [12].…”

Section: Introductionmentioning

confidence: 99%

Joint statistical framework for the estimation of channel and SFO in OFDM systems

Jose

Hari²

2017

IET signal process.

Self Cite

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Joint estimation of channel and sampling frequency offset (SFO) in orthogonal frequency division multiplexing (OFDM) systems, using Bayesian framework, is shown in this study. Hybrid Cramér-Rao lower bounds (HCRLBs) for the estimation of SFO together with channel are obtained. The significance of Bayesian approach in the formulation of joint estimator is shown by comparing HCRLB with the corresponding standard CRLB. The authors propose a joint maximum a posteriori (JMAP) algorithm for the estimation of channel and SFO in OFDM, utilising the prior statistical knowledge of channel. To reduce the complexity of JMAP estimator, a modified MAP algorithm, which has no grid searches, is also proposed. Also, they analyse the effect of inaccurate knowledge of channel statistics and signal-to-noise ratio on the estimation accuracy. The estimation methods are analysed by numerical simulations and resultant conclusions validate the better performance of the proposed algorithms when compared with previous algorithms.

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“…Under the timing synchronisation accomplished by the signal model, many achievements have been obtained for the CRB of frequency offset and carrier phase estimations, as shown in [14–18]. Moreover, CRB of joint time delay and frequency estimation of sinusoidal signals is provided in [19], and Bayesian CRBs for parameter estimation of single‐source signals are derived as well in [20–22].…”

Section: Introductionmentioning

confidence: 99%