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Maximum a posteriori estimation

About: Maximum a posteriori estimation is a research topic. Over the lifetime, 7486 publications have been published within this topic receiving 222291 citations. The topic is also known as: Maximum a posteriori, MAP & maximum a posteriori probability.


Papers
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Journal ArticleDOI
23 Mar 2015-Sensors
TL;DR: Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm.
Abstract: Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm.

79 citations

Journal ArticleDOI
TL;DR: A general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems is introduced and its feasibility is analyzed, and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers.
Abstract: Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems. Its feasibility is analyzed using maximum a posteriori probability (MAP) criterion and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers. To reduce the design and training cost of DNN classifiers, a simple but effective pre-processing method is introduced and adopted. Furthermore, featuring multiple recurrent neural network (RNN) layers, the DNN modulation classifier is realized. Simulation results show that the proposed RNN-based classifier is robust to the uncertain noise conditions, and the performance of it approaches to that of the ideal ML classifier with perfect channel and noise information. Moreover, with a much lower complexity, it outperforms the existing ML-based classifiers, specifically, expectation maximization (EM) and expectation conditional maximization (ECM) classifiers which iteratively estimate channel and noise parameters. In addition, the proposed classifier is shown to be invariant to the signal distortion such as frequency offset. Furthermore, the adopted pre-processing method is shown to accelerate the training process of our proposed classifier, thus reducing the training cost. Lastly, the computational complexity of our proposed classifier is analyzed and compared to other traditional ones, which further demonstrates its overall advantage.

78 citations

Journal ArticleDOI
TL;DR: A data-driven approach to a priori SNR estimation is presented, which reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.
Abstract: The a priori signal-to-noise ratio (SNR) plays an important role in many speech enhancement algorithms. In this paper, we present a data-driven approach to a priori SNR estimation. It may be used with a wide range of speech enhancement techniques, such as, e.g., the minimum mean square error (MMSE) (log) spectral amplitude estimator, the super Gaussian joint maximum a posteriori (JMAP) estimator, or the Wiener filter. The proposed SNR estimator employs two trained artificial neural networks, one for speech presence, one for speech absence. The classical decision-directed a priori SNR estimator by Ephraim and Malah is broken down into its two additive components, which now represent the two input signals to the neural networks. Both output nodes are combined to represent the new a priori SNR estimate. As an alternative to the neural networks, also simple lookup tables are investigated. Employment of these data-driven nonlinear a priori SNR estimators reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.

78 citations

Journal ArticleDOI
TL;DR: A sub-pixel mapping framework based on a maximum a posteriori (MAP) model is proposed to utilize the complementary information of multiple shifted images to improve the accuracy of sub- pixel mapping for hyperspectral imagery.
Abstract: Sub-pixel mapping is technique used to obtain the spatial distribution of different classes at the sub-pixel scale by transforming fraction images to a classification map with a higher resolution. Traditional sub-pixel mapping algorithms only utilize a low-resolution image, the information of which is not enough to obtain a high-resolution land-cover map. The accuracy of sub-pixel mapping can be improved by incorporating auxiliary datasets, such as multiple shifted images in the same area, to provide more sub-pixel land-cover information. In this paper, a sub-pixel mapping framework based on a maximum a posteriori (MAP) model is proposed to utilize the complementary information of multiple shifted images. In the proposed framework, the sub-pixel mapping problem is transformed to a regularization problem, and the MAP model is used to regularize the sub-pixel mapping problem to be well-posed by adding some prior information, such as a Laplacian model. The proposed algorithm was compared with a traditional sub-pixel mapping algorithm based on a single image, and another multiple shifted images based sub-pixel mapping method, using both synthetic and real hyperspectral images. Experimental results demonstrated that the proposed approach outperforms the traditional sub-pixel mapping algorithms, and hence provides an effective option to improve the accuracy of sub-pixel mapping for hyperspectral imagery.

78 citations

Journal ArticleDOI
TL;DR: This letter investigates the sensitivity of the iterative maximum a posteriori probability (MAP) decoder to carrier phase offsets and proposes simple carrier phase recovery algorithms operating within the iteratives MAP decoding iterations, requiring low hardware complexity.
Abstract: In this letter, we investigate the sensitivity of the iterative maximum a posteriori probability (MAP) decoder to carrier phase offsets and propose simple carrier phase recovery algorithms operating within the iterative MAP decoding iterations. The algorithms exploit the information contained in the extrinsic values generated within the iterative MAP decoder to perform carrier recovery, thus requiring low hardware complexity.

78 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202364
2022125
2021211
2020244
2019250
2018236