Optimum Filter Synthesis with DPLMS Method for Energy Reconstruction

Optimum filters are granted increasing recognition as valuable tools for parametric estimation in many scientific and technical fields. The DPLMS method, introduced some twenty years ago, is effective among the synthesis algorithms since it derives the optimum filters directly from the experimental signal and noise waveforms. Two new extensions of the DPLMS method are here presented. The first one speeds up the synthesis phase and improves the energy estimation by synthesizing optimum filters with automatically designed flat-top length. The second one improves the quality of parameter estimation in multi-channel systems by taking advantage of the inter-channel noise correlation properties. The theoretical and functional aspects behind the DPLMS method for optimum filter synthesis are first recalled and illustrated in more detail. The two new DPLMS extensions are subsequently introduced from the theoretical viewpoint and more thoroughly considered from the applicative perspective. The DPLMS optimum filters have been applied first to simulated signals with various amounts and characteristics of superimposed noise and then to the experimental waveforms acquired from a solid-state Ge detector. The results obtained are considered from both the absolute viewpoint and in comparison with those of more traditional, suboptimal filters. The results demonstrate the effectiveness of the two new DPLMS extensions. For single-channel energy estimations, the optimum filters provide comparatively better results than the other filters. The DPLMS multi-channel optimum filters further enhance the quality of the estimations, compared to single-channel optimum filters, with non-negligible inter-channel noise correlation. The effectiveness and robustness of the DPLMS method in synthesizing high-quality filters for energy estimation will be tested soon within leading-edge multi-channel physics experiments.Comment: 15 pages, 13 figure

Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Complex amplitudes tracking loop for multi-path channel estimation in OFDM systems: Synthesis and extension

version corrigée (4 corrections en rouge dans les formules par rapport à la publication de la conférence)International audienceThis study deals with pilot-aided multi-path channel estimation for orthogonal frequency division multiplexing (OFDM) systems under slow to moderate fading conditions. Some algorithms exploit the channel time-domain correlation by using Kalman filters (KFs) to track the channel multi-path complex amplitudes (CAs), assuming a primary acquisition of the delays. Recently, it was shown that less complex algorithms, based on a second-order Complex Amplitude Tracking Loop (CATL) structure and a Least-Square (LS) pilot-aided error signal, can also reach near optimal asymptotic mean-squared error (MSE) performance. The LS-CATL-based algorithms are inspired by digital Phase-Locked Loops (PLL), as well as by the "prediction-correction" principle of the KF (in steady-state mode). This paper sums up and extends our previous results for the tuning and steady-state performance of the LS-CATL algorithm: analytic formulae are given for the first-, second-, and third-order loops, usable here for the multi-path multi-carrier scenario, and adaptable to any Doppler spectrum model of wide-sense stationary channels

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

12 pagesInternational audienceThis study deals with multi-path channel estimation for orthogonal frequency division multiplexing systems under slow to moderate fading conditions. Advanced algorithms exploit the channel time-domain correlation by using Kalman Filters (KFs) based on an approximation of the time-varying channel. Recently, it was shown that under slow to moderate fading, near optimal channel multi-path complex amplitude estimation can be obtained by using the integrated Random Walk (RW) model as the channel approximation. To reduce the complexity of the high-dimensional RW-KF for joint estimation of the multi-path complex amplitudes, we propose using a lower dimensional RW-KF that estimates the complex amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman gain formulation through the Woodbury's identities. Hence, this new algorithm consists of a superposition of independent single-path single-carrier KFs, which were optimized in our previous studies. This observation allows us to adapt the optimization to the actual multi-path multi-carrier scenario, to provide analytic formulae for the mean-square error performance and the optimal tuning of the proposed estimator directly as a function of the physical parameters of the channel (Doppler frequency, Signal-to-Noise-Ratio, Power Delay Profile). These analytic formulae are given for the first-, second-, and third-order RW models used in the KF. The proposed per-path KF is shown to be as efficient as the exact KF (i.e., the joint multipath KF), and outperforms the autoregressive-model-based KFs proposed in the literature

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both channel and spatial dimensions, thus to pinpoint the structural relevance of multi-channel features to the filtering system. In contrast to the widely used spatial regularisation or feature selection methods, to the best of our knowledge, this is the first time that channel selection has been advocated for DCF-based tracking. We demonstrate that our GFS-DCF method is able to significantly improve the performance of a DCF tracker equipped with deep neural network features. In addition, our GFS-DCF enables joint feature selection and filter learning, achieving enhanced discrimination and interpretability of the learned filters. To further improve the performance, we adaptively integrate historical information by constraining filters to be smooth across temporal frames, using an efficient low-rank approximation. By design, specific temporal-spatial-channel configurations are dynamically learned in the tracking process, highlighting the relevant features, and alleviating the performance degrading impact of less discriminative representations and reducing information redundancy. The experimental results obtained on OTB2013, OTB2015, VOT2017, VOT2018 and TrackingNet demonstrate the merits of our GFS-DCF and its superiority over the state-of-the-art trackers. The code is publicly available at https://github.com/XU-TIANYANG/GFS-DCF

Multi-channel coded-aperture photography

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 87-89).This thesis describes the multi-channel coded-aperture photography, a modified camera system that can extract an all-focus image of the scene along with a depth estimate over the scene. The modification consists of inserting a set of patterned color filters into the aperture of the camera lens. This work generalizes the previous research on a single-channel coded aperture, by deploying distinct filters in the three primary color channels, in order to cope better with the effect of a Bayer filter and to exploit the correlation among the channels. We derive the model and algorithms for the multi-channel coded aperture, comparing the simulated performance of the reconstruction algorithm against that of the original single-channel coded aperture. We also demonstrate a physical prototype, discussing the challenges arising from the use of multiple filters. We provide a comparison with the single-channel coded aperture in performance, and present results on several scenes of cluttered objects at various depths.by Jongmin Baek.M.Eng

MULTI-CHANNEL CORRELATION FILTERS WITH LIMITED BOUNDARIES: THEORY AND APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH