# IEEE Signal Processing Letters

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• ### On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users

Publication Year: 2014, Page(s):1501 - 1505
Cited by:  Papers (257)
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In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In parti... View full abstract»

• ### A universal image quality index

Publication Year: 2002, Page(s):81 - 84
Cited by:  Papers (1828)  |  Patents (39)
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We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematica... View full abstract»

• ### Making a “Completely Blind” Image Quality Analyzer

Publication Year: 2013, Page(s):209 - 212
Cited by:  Papers (323)
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An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of trainin... View full abstract»

• ### Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

Publication Year: 2017, Page(s):279 - 283
Cited by:  Papers (7)
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The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep CNN architecture for environmental sound classif... View full abstract»

• ### Frequency Shift Chirp Modulation: The LoRa Modulation

Publication Year: 2017, Page(s):1818 - 1821
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Low power wide area networks (LPWAN) are emerging as a new paradigm, especially in the field of Internet of Things (IoT) connectivity. LoRa is one of the LPWAN and it is gaining quite a lot of commercial traction. The modulation underlying LoRa is patented and has never been described theoretically. The aim of this letter is to give the first rigorous mathematical signal processing description of ... View full abstract»

• ### Fairness for Non-Orthogonal Multiple Access in 5G Systems

Publication Year: 2015, Page(s):1647 - 1651
Cited by:  Papers (120)
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In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensur... View full abstract»

• ### An Experimental Study on Speech Enhancement Based on Deep Neural Networks

Publication Year: 2014, Page(s):65 - 68
Cited by:  Papers (126)
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This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be... View full abstract»

• ### SAR Image Despeckling Using a Convolutional Neural Network

Publication Year: 2017, Page(s):1763 - 1767
| | PDF (2125 KB) | HTML

Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep-learning-based approach called, image despeckling convolutional neural network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional l... View full abstract»

• ### Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Publication Year: 2016, Page(s):1499 - 1503
Cited by:  Papers (31)
| | PDF (757 KB) | HTML

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In ... View full abstract»

• ### Learning-Based Image Reconstruction via Parallel Proximal Algorithm

Publication Year: 2018, Page(s):989 - 993
| | PDF (436 KB) | HTML

In the past decade, sparsity-driven regularization has led to the advancement of image reconstruction algorithms. Traditionally, such regularizers rely on analytical models of sparsity [e.g., total variation (TV)]. However, more recent methods are increasingly centered around data-driven arguments inspired by deep learning. In this letter, we propose to generalize TV regularization by replacing th... View full abstract»

• ### Efficient Data Fusion Using Random Matrix Theory

Publication Year: 2018, Page(s):605 - 609
| | PDF (293 KB) | HTML

This letter addresses multisensor data fusion under the Gaussian noise. Under the Gauss-Markov model assumptions, data fusion based on maximum likelihood estimation (MLE) is the minimum variance unbiased estimator. Nonetheless, we propose a linear fusion algorithm based on the random matrix theory, which yields a biased estimator. The proposed estimator has a lower mean squared error (MSE) than th... View full abstract»

• ### Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super Resolution

Publication Year: 2018, Page(s):916 - 920
| | PDF (568 KB) | HTML

This letter presents a novel super-resolution (SR) method via nonlocal similarity modeling and deep convolutional neural network (CNN) gradient prior (GP). Specifically, on the one hand, the group similarity reliability (GSR) strategy is proposed for improving the adaptive high-dimensional nonlocal total variation (AHNLTV) model [statistical prior, GSR-based AHNLTV (GA)], which captures the struct... View full abstract»

• ### Enhancing Image Quality via Style Transfer for Single Image Super-Resolution

Publication Year: 2018, Page(s):571 - 575
| | PDF (845 KB) | HTML

Recently, by feat of the Generative Adversarial Network (GAN), single image super-resolution (SISR) has achieved great breakthroughs in enhancing the perceptual image quality. However, since the network is trained by minimizing the perceptual loss, the GAN based SISR method (SRGAN) [1] results in images with very low objective quality, i.e., peak signal-to-noise ratio (PSNR). In this letter, we ai... View full abstract»

• ### BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering

Publication Year: 2018, Page(s):55 - 59
| | PDF (428 KB) | HTML

Denoising is a fundamental task in image processing with wide applications for enhancing image qualities. BM3D is considered as an effective baseline for image denoising. Although learning-based methods have been dominant in this area recently, the traditional methods are still valuable to inspire new ideas by combining with learning-based approaches. In this letter, we propose a new convolutional... View full abstract»

• ### Residual LSTM Attention Network for Object Tracking

Publication Year: 2018, Page(s):1029 - 1033
| | PDF (704 KB) | HTML

In this letter, we propose an attention network for object tracking. To construct the proposed attention network for sequential data, we combine long-short term memory (LSTM) and a residual framework into a residual LSTM (RLSTM). The LSTM, which learns temporal correlation, is used for a temporal learning of object tracking. In the proposed RLSTM method, the residual framework, which achieves the ... View full abstract»

• ### Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

Publication Year: 2018, Page(s):551 - 555
| | PDF (542 KB) | HTML

Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on co... View full abstract»

• ### A Proximal ADMM for Decentralized Composite Optimization

Publication Year: 2018, Page(s):1121 - 1125
| | PDF (343 KB) | HTML

In this letter, we propose a proximal alternating direction method of multiplier (ADMM) to solve the composite optimization problem over a decentralized network. Compared with existing methods, such as PG-EXTRA and IC-ADMM, the proposed decentralized proximal ADMM method does not rely on assuming a smooth + nonsmooth structure on the objective functions, thus covering a wider range of composite op... View full abstract»

• ### Self-Paced AutoEncoder

Publication Year: 2018, Page(s):1054 - 1058
| | PDF (14078 KB) | HTML

Autoencoder, which learns latent representations of samples in an unsupervised manner, has great potential in computer vision and signal processing. However, the diversity of samples makes learning a component autoencoder remaining a challenging task. This letter proposes a novel Self-Paced AutoEncoder (SPAE) for unsupervised feature extraction. The motivation behind this letter is to take samples... View full abstract»

• ### Ramp Distribution-Based Image Enhancement Techniques for Infrared Images

Publication Year: 2018, Page(s):931 - 935
| | PDF (901 KB) | HTML

A novel image enhancement method for infrared (IR) images is presented. The proposed method consists of two parts considering the characteristics of high-dynamic-range IR images. First, we attempt to enhance image contrast by introducing the ramp distribution that increases with a constant slope in an ordered histogram domain. The ramp-distributed histogram is incorporated into an optimization pro... View full abstract»

• ### Robust Sparse Recovery in Impulsive Noise via Continuous Mixed Norm

Publication Year: 2018, Page(s):1146 - 1150
| | PDF (553 KB) | HTML

This letter investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavytailed impulsive noise is well modeled with stable distributions. Since there is no explicit formula for the probability density function of SαS distribution, alternative approximations are used, such as, generalized Gaussian distribution, which imposes ℓp-n... View full abstract»

• ### Accelerated Particle Filter for Real-Time Visual Tracking With Decision Fusion

Publication Year: 2018, Page(s):1094 - 1098
| | PDF (455 KB) | HTML Media

Correlation-filter-based trackers, showing strong discrimination ability in challenging situations, have recently achieved superior performance in visual tracking. However, because the model treats the tracker's predictions in new frames as training data, the filter can be contaminated by small incorrect predictions, which cause model drift. Particle-filter-based trackers usually produce more accu... View full abstract»

• ### Image Fusion With Convolutional Sparse Representation

Publication Year: 2016, Page(s):1882 - 1886
Cited by:  Papers (6)
| | PDF (361 KB) | HTML Media

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistra... View full abstract»

• ### Additive Margin Softmax for Face Verification

Publication Year: 2018, Page(s):926 - 930
| | PDF (578 KB) | HTML

In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to ... View full abstract»

• ### Dynamic Asymmetric Power Splitting Scheme for SWIPT-Based Two-Way Multiplicative AF Relaying

Publication Year: 2018, Page(s):1014 - 1018
| | PDF (474 KB) | HTML

Power splitting (PS) scheme design is one of the most important challenges in simultaneous wireless information and power transfer-based two-way multiplicative amplify-and-forward relay networks. In this letter, we propose a novel dynamic asymmetric PS (DAPS) scheme to minimize the system outage probability by exploiting the asymmetric instantaneous channel gains between the relay node and the des... View full abstract»

• ### Efficient, Accurate, and Rotation-Invariant Iris Code

Publication Year: 2017, Page(s):1233 - 1237
| | PDF (192 KB) | HTML

The large scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High-performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and perfo... View full abstract»

• ### Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition

Publication Year: 2018, Page(s):1044 - 1048
| | PDF (883 KB) | HTML

This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action recognition. The Ensem-NN is introduced based on the idea of ensemble learning, “two heads are better than one.” According to the property of skeleton sequences, we design one-dimensional convolution neural network with residual structure as Base-Net. From entirety to local, from focus to motion, we ... View full abstract»

• ### Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion

Publication Year: 2018, Page(s):808 - 812
| | PDF (686 KB) | HTML

Accurate age estimation from a facial image is quite challenging, since physical age and apparent age can be quite different, and this difference is dependent on gender, ethnicity, and many other factors. Multitask deep learning is one of the approach to improve age estimation by employing auxiliary tasks, such as gender recognition, that are related to the primary task. However, in traditional mu... View full abstract»

• ### Proportional Fair Multiuser Scheduling in LTE

Publication Year: 2009, Page(s):461 - 464
Cited by:  Papers (107)  |  Patents (1)
| | PDF (447 KB) | HTML

The challenge of scheduling user transmissions on the downlink of a long term evolution (LTE) cellular communication system is addressed. A maximum rate algorithm which does not consider fairness among users was proposed in . Here, a multiuser scheduler with proportional fairness (PF) is proposed. Numerical results show that the proposed PF scheduler provides a superior fairness performance with a... View full abstract»

• ### Adaptive Detection in Distributed Networks Using Maximum Likelihood Detector

Publication Year: 2018, Page(s):974 - 978
| | PDF (461 KB) | HTML

This letter describes a maximum likelihood distributed detector algorithm with a special initialization that yields an exponential decay rate on error probabilities in transient, using the adaptive diffusion least mean squares (LMS) algorithm in a distributed sensor network. The nodes that compose the network must decide between two concurrent hypotheses concerning the environment where they are i... View full abstract»

• ### A Spectral Method for Stable Bispectrum Inversion With Application to Multireference Alignment

Publication Year: 2018, Page(s):911 - 915
| | PDF (334 KB) | HTML

We focus on an alignment-free method to estimate the underlying signal from a large number of noisy randomly shifted observations. Specifically, we estimate the mean, power spectrum, and bispectrum of the signal from the observations. Since the bispectrum contains the phase information of the signal, reliable algorithms for bispectrum inversion are useful in many applications. We propose a new alg... View full abstract»

• ### Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition

Publication Year: 2017, Page(s):510 - 514
Cited by:  Papers (3)
| | PDF (597 KB) | HTML

Human activity recognition in videos with convolutional neural network (CNN) features has received increasing attention in multimedia understanding. Taking videos as a sequence of frames, a new record was recently set on several benchmark datasets by feeding frame-level CNN sequence features to long short-term memory (LSTM) model for video activity recognition. This recurrent model-based visual re... View full abstract»

• ### Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting

Publication Year: 2018, Page(s):1136 - 1140
| | PDF (324 KB) | HTML

We propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with dynamic time warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classif... View full abstract»

• ### Voice Activity Detection Using an Adaptive Context Attention Model

Publication Year: 2018, Page(s):1181 - 1185
| | PDF (557 KB) | HTML

Voice activity detection (VAD) classifies incoming signal segments into speech or background noise; its performance is crucial in various speech-related applications. Although speech-signal context is a relevant VAD asset, its usefulness varies in unpredictable noise environments. Therefore, its usage should be adaptively adjustable to the noise type. This letter improves the use of context inform... View full abstract»

• ### A New Information Theoretic Relation Between Minimum Error Entropy and Maximum Correntropy

Publication Year: 2018, Page(s):921 - 925
| | PDF (311 KB) | HTML

The past decade has seen the rapid development of information theoretic learning and its applications in signal processing and machine learning. Specifically, minimum error entropy (MEE) and maximum correntropy criterion (MCC) have been widely studied in the literature. Although MEE and MCC are applied in many branches of knowledge and could outperform statistical criteria (such as mean square err... View full abstract»

• ### AMI-Net: Convolution Neural Networks With Affine Moment Invariants

Publication Year: 2018, Page(s):1064 - 1068
| | PDF (730 KB) | HTML

Affine moment invariant (AMI) is a kind of hand-crafted image feature, which is invariant to affine transformations. This property is precisely what the standard convolution neural network (CNN) is difficult to achieve. In this letter, we present a kind of network architecture to introduce AMI into CNN, which is called AMI-Net. We achieved this by calculating AMI on the feature maps of the hidden ... View full abstract»

• ### Adaptive Graph-Based Total Variation for Tomographic Reconstructions

Publication Year: 2018, Page(s):700 - 704
| | PDF (536 KB) | HTML Media

Sparsity exploiting image reconstruction (SER) methods have been extensively used with total variation (TV) regularization for tomographic reconstructions. Local TV methods fail to preserve texture details and often create additional artifacts due to over-smoothing. Nonlocal TV (NLTV) methods have been proposed as a solution to this but they either lack continuous updates due to computational cons... View full abstract»

• ### Global Temporal Representation Based CNNs for Infrared Action Recognition

Publication Year: 2018, Page(s):848 - 852
| | PDF (511 KB) | HTML

Infrared human action recognition has many advantages, i.e., it is insensitive to illumination change, appearance variability, and shadows. Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered. In this lette... View full abstract»

• ### Empirical mode decomposition as a filter bank

Publication Year: 2004, Page(s):112 - 114
Cited by:  Papers (1004)  |  Patents (2)
| | PDF (160 KB) | HTML

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it... View full abstract»

• ### Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

Publication Year: 2017, Page(s):1547 - 1551
| | PDF (662 KB) | HTML

Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a ste... View full abstract»

• ### An Adaptive Correlated Image Prior for Image Restoration Problems

Publication Year: 2018, Page(s):1024 - 1028
| | PDF (629 KB) | HTML Media

Image restoration is typically defined as an ill-posed problem which has to be regularized to obtain an acceptable solution. In Bayesian interpretation, regularization is equivalent to prior model of the image. An added value of Bayesian point of view is the ability to form a hierarchical model and estimate the hyperparameters of the prior from the data. Many prior models are available, usually ba... View full abstract»

• ### Spectrally Constrained Unimodular Sequence Design Without Spectral Level Mask

Publication Year: 2018, Page(s):1004 - 1008
| | PDF (502 KB) | HTML

Due to the freedom degree loss resulted from the unimodular constraints, it is not easy to specify proper and feasible stopband and passband levels for frequency grids of interest in spectrally constrained sequence design problems. In an attempt to avoid this difficulty, we devise a cost function that minimizes the ratio of the maximal stopband level to the minimal passband level. Next, we introdu... View full abstract»

• ### Distributed Nonlinear System Identification in $alpha$ -Stable Noise

Publication Year: 2018, Page(s):979 - 983
| | PDF (959 KB) | HTML

In this letter, a novel diffusion Volterra (DV) algorithm is proposed for distributed in-network system identification in the presence of α-stable noise. The proposed algorithm is based on the logarithmic least mean pth-power criterion, which makes it robust against impulsive interferences, at the price of increased complexity. To overcome this shortcoming, we further develop the diffusion ... View full abstract»

• ### A Novel Multiconnected Convolutional Network for Super-Resolution

Publication Year: 2018, Page(s):946 - 950
| | PDF (636 KB) | HTML

Convolutional neural networks exhibit superior performance for single image super-resolution (SISR) tasks. However, as the network grows deeper, features from the earlier layers are impeded or less used in later layers. In SISR, the earlier layers are mainly composed of local features that are essential to the task. In this letter, we present a novel multiconnected convolutional network for SISR t... View full abstract»

• ### Sound Field Recording Using Distributed Microphones Based on Harmonic Analysis of Infinite Order

Publication Year: 2018, Page(s):135 - 139
| | PDF (466 KB) | HTML Media Code

A sound field recording method based on spherical or circular harmonic analysis for arbitrary array geometry and directivity of microphones is proposed. In current methods based on harmonic analysis, a sound field is decomposed into harmonic functions with a center given in advance, which is called a global origin, and their coefficients are obtained up to a certain truncation order using micropho... View full abstract»

• ### Kernel Deep Regression Network for Touch-Stroke Dynamics Authentication

Publication Year: 2018, Page(s):1109 - 1113
| | PDF (535 KB) | HTML

Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a classifier separately. In this letter, we propose a stacking-based deep learning network that performs feature extraction and classification, collectively dubbed Kernel Deep Regression Ne... View full abstract»

• ### Support vector machines using GMM supervectors for speaker verification

Publication Year: 2006, Page(s):308 - 311
Cited by:  Papers (397)  |  Patents (3)
| | PDF (136 KB) | HTML

Gaussian mixture models (GMMs) have proven extremely successful for text-independent speaker recognition. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. Recent methods in compensation for speaker and channel variability have proposed the idea of stacking the means of the GMM model to form a GMM mean... View full abstract»

• ### Wireless Information Surveillance and Intervention Over Multiple Suspicious Links

Publication Year: 2018, Page(s):1131 - 1135
| | PDF (316 KB) | HTML

This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdroppe... View full abstract»

• ### Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions

Publication Year: 2011, Page(s):130 - 133
Cited by:  Papers (50)
| | PDF (295 KB) | HTML

In this letter, we present a novel feature extraction method for sound event classification, based on the visual signature extracted from the sound's time-frequency representation. The motivation stems from the fact that spectrograms form recognisable images, that can be identified by a human reader, with perception enhanced by pseudo-coloration of the image. The signal processing in our method is... View full abstract»

• ### Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI

Publication Year: 2018, Page(s):783 - 787
| | PDF (348 KB) | HTML

Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other. To extract the discriminative features and alleviate overfitting problem for motor imagery brain-computer interface (BCI), spatial filtering is widely applied but often only very few common spatial patterns (CSP) are selected as features while ignoring all others. However... View full abstract»

• ### Riemann–Langevin Particle Filtering in Track-Before-Detect

Publication Year: 2018, Page(s):1039 - 1043
| | PDF (389 KB) | HTML

Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Mo... View full abstract»

## Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
James E. Fowler
Dept Electrical & Computer Engineering
Associate Director
Distributed Analytics and Security Institute
Mississippi State University
Mississippi State, MS 39762 USA
fowler@ece.msstate.edu