Joint estimation of water column parameters and seabed reflectance combining maximum likelihood and unmixing algorithm

AbstractIn this paper, we present a joint time-variant carrier frequency offset (CFO) and frequency-selective channel response estimation scheme for multiple input-multiple output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems for mobile users. The signal model of the MIMO-OFDM system is introduced, and the joint estimator is derived according to the maximum likelihood criterion. The proposed algorithm can be separated into three major parts. In the first part of the proposed algorithm, an initial CFO is estimated using derotation, and the result is used to apply a frequency-domain equalizer. In the second part, an iterative method is employed to locate the fine frequency peak for better CFO estimation. An adaptive process is used in the third part of the proposed algorithm to obtain the updated CFO estimation and track parameter time variations, including the time-varying CFOs and time-varying channels. The computational complexity of the proposed algorithm is considerably lower than that of the maximum likelihood-based grid search method. In a simulation, the mean squared error performance of the proposed algorithm was close to the Cramer-Rao lower bound. The simulation results indicate that the proposed novel joint estimation algorithm provides a bit error rate performance close to that in the perfect channel estimation condition. The results also suggest that the proposed method has reliable tracking performance in Jakes’ channel models.

Download Full-text

Joint estimation of state and parameter with maximum likelihood method

2017 36th Chinese Control Conference (CCC) ◽

10.23919/chicc.2017.8028190 ◽

2017 ◽

Cited By ~ 1

Author(s):

Huiping Zhuang ◽

Jieying Lu ◽

Junhui Li

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Method ◽

Joint Estimation ◽

Likelihood Method

Download Full-text

Algorithms for joint activity–attenuation estimation from positron emission tomography scatter

EJNMMI Physics ◽

10.1186/s40658-019-0254-y ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Yannick Berker ◽

Volkmar Schulz ◽

Joel S. Karp

Keyword(s):

Positron Emission Tomography ◽

Maximum Likelihood ◽

Scattered Data ◽

Joint Estimation ◽

Emission Tomography ◽

Emission Data ◽

True Solution ◽

High Attenuation ◽

Positron Emission ◽

The Impact

Abstract Background Attenuation correction in positron emission tomography remains challenging in the absence of measured transmission data. Scattered emission data may contribute missing information, but quantitative scatter-to-attenuation (S2A) reconstruction needs to input the reconstructed activity image. Here, we study S2A reconstruction as a building block for joint estimation of activity and attenuation. Methods We study two S2A reconstruction algorithms, maximum-likelihood expectation maximization (MLEM) with one-step-late attenuation (MLEM-OSL) and a maximum-likelihood gradient ascent (MLGA). We study theoretical properties of these algorithms with a focus on convergence and convergence speed and compare convergence speeds and the impact of object size in simulations using different spatial scale factors. Then, we propose joint estimation of activity and attenuation from scattered and nonscattered (true) emission data, combining MLEM-OSL or MLGA with scatter-MLEM as well as trues-MLEM and the maximum-likelihood transmission (MLTR) algorithm. Results Shortcomings of MLEM-OSL inhibit convergence to the true solution with high attenuation; these shortcomings are related to the linearization of a nonlinear measurement equation and can be linked to a new numerical criterion allowing geometrical interpretations in terms of low and high attenuation. Comparisons using simulated data confirm that while MLGA converges largely independent of the attenuation scale, MLEM-OSL converges if low-attenuation data dominate, but not with high attenuation. Convergence of MLEM-OSL can be improved by isolating data satisfying the aforementioned low-attenuation criterion. In joint estimation of activity and attenuation, scattered data helps avoid local minima that nonscattered data alone cannot. Combining MLEM-OSL with trues-MLEM may be sufficient for low-attenuation objects, while MLGA, scatter-MLEM, and MLTR may additionally be needed with higher attenuation. Conclusions The performance of S2A algorithms depends on spatial scales. MLGA provides lower computational complexity and convergence in more diverse setups than MLEM-OSL. Finally, scattered data may provide additional information to joint estimation of activity and attenuation through S2A reconstruction.

Download Full-text