Set-Membership Proportionate Affine Projection Algorithms

Proportionate adaptive filters can improve the convergence speed for the identification of sparse systems as compared to their conventional counterparts. In this paper, the idea of proportionate adaptation is combined with the framework of set-membership filtering (SMF) in an attempt to derive novel computationally efficient algorithms. The resulting algorithms attain an attractive faster converge for both situations of sparse and dispersive channels while decreasing the average computational complexity due to the data discerning feature of the SMF approach. In addition, we propose a rule that allows us to automatically adjust the number of past data pairs employed in the update. This leads to a set-membership proportionate affine projection algorithm (SM-PAPA) having a variable data-reuse factor allowing a significant reduction in the overall complexity when compared with a fixed data-reuse factor. Reduced-complexity implementations of the proposed algorithms are also considered that reduce the dimensions of the matrix inversions involved in the update. Simulations show good results in terms of reduced number of updates, speed of convergence, and final mean-squared error

Multichannel fast QR-decomposition RLS algorithms with explicit weight extraction

ABSTRACT Multichannel fast QR decomposition recursive least-squares (MC-FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. However, these algorithms have been restricted to problems seeking an estimate of the output error signal. This is because their transversal weights are embedded in the algorithm variables and are not explicitly available. In this paper we present a novel technique that can extract the filter weights associated with the MC-FQRD-RLS algorithm at any time instant. As a consequence, the range of applications is extended to include problems where explicit knowledge of the filter weights is required. The proposed weight extraction technique is used to identify the beampattern of a broadband adaptive beamformer implemented with an MC-FQRD-RLS algorithm. The results confirm that the extracted coefficients of the MC-FQRD-RLS algorithm are identical to those obtained by any RLS algorithm such as the inverse QRD-RLS algorithm

Contribution from Tree Legumes to Mixed Grass-Legume Pastures

Legumes and associated microorganisms may fix N from atmosphere and benefit grass on mixed grass-legume pastures. Nitrogen may be transferred by different mechanisms, including direct transfer of N compounds by roots, decomposition of nodules, roots, litter from legume (Nair 1993), and through animal excreta after legume intake by cattle. Silvopastoral systems including tree legumes may become a viable option in tropical regions, considering the increasing prices of N fertilizers compared to farm products such as beef and milk. This experiment evaluated legume contribution on mixed grass-legume pastures in the coastal region of Pernambuco State, Brazil

Animal Performance in Signalgrass Monoculture or in Silvopastoral Systems

Silvopastoral systems (SPS) can increase overall productivity and long-term income due to the simultaneous production of trees, forage, and livestock. This 2-yr study evaluated animal performance and herbage responses in C4-grass monoculture or in SPS in the sub-humid tropical region of Brazil. The experimental design was randomized complete block with three replications. Treatments were: Urochloa decumbens (Stapf.) R. Webster (Signalgrass) + Mimosa caesalpiniifolia Benth (SPS-Mimosa); Signalgrass + Gliricidia sepium (Jacq.) Kunth ex Walp (SPS-Gliricidia); and Signalgrass monoculture (SM). Response variables included herbage and livestock responses. Cattle were managed under continuous stocking with variable stocking rate. There was interaction between treatment × month for herbage mass. Green herbage accumulation rate ranged from 20 to 80 kg DM ha-1d-1 across months, with SPS-Mimosa presenting lower rates. Average daily gain was greater in SPS-Gliricidia, followed by SM, and SPS-Mimosa, respectively (0.77; 0.56; 0.23 kg d-1), varying across months. Stocking rate ranged from 0.86 to 1.6 AU ha-1. Total gain per area during the experimental period was greater for SPS-Gliricidia (423 kg BW ha-1), followed by signalgrass in monoculture (347 kg BW ha-1), and SPS-Mimosa (50 kg BW ha-1). Silvopasture systems using signalgrass and gliricidia enhanced livestock gains compared with signalgrass in monoculture, and mimosa trees outcompeted signalgrass, reducing livestock gains. Silvopasture systems with tree legumes have potential to provide numerous ecosystem services and reduce C footprint of livestock systems in the tropics, however, the choice of tree species is key and determined by which ecosystem service is prioritized

Improved PLL-based MUSIC Algorithm for Single Receiver DF Systems

Abstract In this paper we propose an improved version of a single receiver DF system that combines a PLL scheme with the MUSIC algorithm to obtain both azimuthal and zenithal angles. The new approach uses a more efficient algorithm to remove the typical ambiguities of PLL-based schemes. We carry out several experiments to assess the algorithm performance. Among them, we consider complex environments, such as wireless communication channels where the received signal is corrupted by noise and suffers from distortion, interference, and multipath effects. The new scheme is tested for BPSK and QPSK signals under these conditions. The simulated results show superior performance of the proposed PLL algorithm when compared to previous techniques

Robust Passive Coherent Location via Nonlinearly Constrained Least Squares

This paper addresses the problem of target location by means of a passive radar. Existing approaches based on time difference-of-arrival (TDOA) measurements, namely spherical interpolation and spherical intersection, are revisited for the case of single transmitter and multiple receivers. The mathematical formulations of these state-of-the-art approaches do not take into account possible TDOA estimation errors, which degrade the target location performance. We extend those formulations by incorporating a nonlinear constraint into the underlying least squares problem, thus conferring robustness to the location technique against TDOA estimation errors, as corroborated by extensive numerical experiments

Improved PLL-based MUSIC Algorithm for Single Receiver DF Systems

<div><p>Abstract In this paper we propose an improved version of a single receiver DF system that combines a PLL scheme with the MUSIC algorithm to obtain both azimuthal and zenithal angles. The new approach uses a more efficient algorithm to remove the typical ambiguities of PLL-based schemes. We carry out several experiments to assess the algorithm performance. Among them, we consider complex environments, such as wireless communication channels where the received signal is corrupted by noise and suffers from distortion, interference, and multipath effects. The new scheme is tested for BPSK and QPSK signals under these conditions. The simulated results show superior performance of the proposed PLL algorithm when compared to previous techniques.</p></div

Improved PLL-based MUSIC Algorithm for Single Receiver DF Systems

<div><p>Abstract In this paper we propose an improved version of a single receiver DF system that combines a PLL scheme with the MUSIC algorithm to obtain both azimuthal and zenithal angles. The new approach uses a more efficient algorithm to remove the typical ambiguities of PLL-based schemes. We carry out several experiments to assess the algorithm performance. Among them, we consider complex environments, such as wireless communication channels where the received signal is corrupted by noise and suffers from distortion, interference, and multipath effects. The new scheme is tested for BPSK and QPSK signals under these conditions. The simulated results show superior performance of the proposed PLL algorithm when compared to previous techniques.</p></div

Online Learning and Adaptive Filters

Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering