A5101536237- Advanced Adaptive Filtering Techniques
- Blind Source Separation Techniques
- Sparse and Compressive Sensing Techniques
- Direction-of-Arrival Estimation Techniques
- Speech and Audio Processing
- Image and Signal Denoising Methods
- Control Systems and Identification
- Advanced Algorithms and Applications
- Stochastic Gradient Optimization Techniques
- Stability and Controllability of Differential Equations
- Consumer Perception and Purchasing Behavior
- Indoor and Outdoor Localization Technologies
- Quantum Information and Cryptography
- Advanced Sensor and Control Systems
- Stochastic processes and financial applications
- Optimization and Variational Analysis
- Stability and Control of Uncertain Systems
- Distributed Sensor Networks and Detection Algorithms
- Statistical Methods and Inference
- Orbital Angular Momentum in Optics
- Industrial Technology and Control Systems
- Quantum Mechanics and Applications
- Electrical and Bioimpedance Tomography
- Advanced Wireless Communication Techniques
- Radar Systems and Signal Processing
Hengtong Optoelectronic (China)
2022
National University of Defense Technology
2020
Nanjing Institute of Technology
2011-2013
Sany (China)
2011
PLA Army Engineering University
2007-2009
Xiaomi (China)
2009
Wayne State University
2003
Academia Sinica
1993-1994
An averaging procedure for adaptive filtering is developed. In contrast to the traditional approach, two sequences (x/sub n/) and are constructed, where arithmetic average of n/). The authors show that algorithm so designed has optimal rate convergence. Therefore, approach asymptotically efficient.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
By building the generalized Sigmoid function relationship between normalized step-size and power of error signal, a novel variable NLMS algorithm is proposed. It proved that NPVSS-NLMS changes as new does when A=σv -m B=2. The physical meanings parameters in this are explored. theoretical analysis illustrate combine virtues function, it leads to faster convergence rate lower final misalignment. computer simulation results support analysis.
Compressive sensing (CS) could sample the sparse signal at a rate much lower than Nyquist-rate and reconstruct it with high probability by optimization technique. This paper introduced frequency estimation method of hopping (FH) based on CS theory, because sparsity FH signals local Fourier basis, frequencies can be estimated position non-zero projection basis. Experiment results show that this is effective efficient; in addition, estimate small number compressive incoherent measurements.
In the presence of narrowband interference, non-Wiener effects have been observed for normalized least-mean-square (NLMS) equalizers. These can lead to performance improvements over fixed Wiener filter with same model structure, but convergence rate NLMS equalizer slows down seriously. this paper, steady-state and performances affine projection (AP) are studied in multipath interferences. Examples show that also occur AP equalizer, especially when order is small. And more importantly, high...
In the spectrum estimation algorithm based on Compressed Sensing, selection of measurement matrix has significant influence whether can estimate signal power with high precision.In article, it briefly describes basic principles and common matrixes.In addition, presents a detailed comparison analysis construction method, pros cons among random matrix, structured deterministic matrix.On this basis, aiming at single-tone, multi-tone QPSK signals, feasibility six kinds matrixes used in...
To improve the convergence rate, multiple input vectors are used to update filter coefficients in affine projection algorithm (APA) family, however, different vector affects differently. The ideal selection criterion of is derived by largest decrease mean-square deviation. This condition fits for algorithms APA family. Based on criterion, a family APAs which dynamically select presented. experimental results show that proposed have fast speed, small steady-state error compared conventional...
Support recovery from multiple measurement vectors has been regarded as a critical aspect of compressive sensing. Most existing algorithms require the prior knowledge sparsity or noise power, which are unknown even time-varying in actual applications, to determine termination condition iterative process. Motivated by entropy concept information theory, frequency-domain (FDE)-based blind support algorithm is proposed, where FDE employed test statistic whether there sparse signal remains...
The robustness of adaptive filtering algorithms is considered. the main effort has been devoted to obtaining reasonably good upper bounds for iterates when law large numbers only approximately valid. Asymptotic order estimates absolute deviation are obtained, and an almost sure convergence result proved. Comments made regarding corresponding algorithm with randomly varying truncations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Carrier frequency offset (CFO) between transmitter and receiver carrier generators degrade the performance of adaptive filters seriously, even for very small CFO. Tracking analysis affine projection filter is carried out in presence The energy conservation relation with both random cyclic nonstationarities derived. Based on steadystate mean-square error derived nonstationarities. theoretical results are close to Monte Carlo simulation curves. Besides, simulations show that tracking improves...
The following class of recursive algorithm is considered: (X/sub n+1/=X/sub n/+a/sub n/ (B+C/sub n/)X/sub n/+b/sub n/). Under weaker assumptions about (C/sub n/) than those used for previous results, the necessary and sufficient condition on (b/sub convergence this established, its robustness examined. These results are then applied to an adaptive filtering in order obtain better previously given by authors (1991).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML"...
To estimate the wideband or multi-channel signals’ spectrum swiftly and exactly is a key technology to improve performance of sensing. The paper was proposed novel estimation algorithm based on compressed sensing (CS) multi-taper method (MTM), which called CS-MTM. new validated by single-tone, multi-tone QPSK signals. Meanwhile, has six common random meaurement matrixes can be used in successfully. Simulation results show that approach potentially achieve better than combined with...
In the presence of narrowband interference, non-Wiener effects have been observed for normalized least-mean-square (NLMS) algorithm and affine projection (APA). The convergence performance (APA) is analyzed in strong interference. Firstly, relationship between APA NLMS derived, with help decorrelation input signal. Then, statistical properties decorrelated signals are from which we can see that almost not affected by computer simulation results support theoretical analysis.
In this article, we propose an improved algorithm of blind signal separation that jointly exploits the selection rational nonlinear functions and quasi-Newton method. The proposed uses in constructing cost function, which have less computational complexity than usual such as hyperbolic tangent Gaussian functions. We use method to solve solution procedure function based on maximum likelihood criterion has good asymptotic performance. source data for simulation are taken from generalized...
With good sparsity of Frequency Hopping (FH) signals on the local Fourier basis based compressive sensing (CS) theory, this paper proposes a novel adaptive synchronous estimation method hopping frequency for FH signal. Given hop interval, and by continuously adjusting starting time observation, frequencies can be estimated with few measurements without reconstructing original Simulation results show that achieve relatively ideal performance at lower compression ratio signal to noise (SNR).
A novel adaptive algorithm for blind separation of the linearly mixed signals based on relationship variability between source and is presented, which utilizes generalized eigen-subspace decomposition recursive least square (RLS) parallel computation. The presented has less mean error by avoiding complexity estimation high-order statistics signal, it faster speed convergence. Simulation results illustrate efficiency algorithm.
Greedy algorithm is an important class of sparse recovery compressive sensing. Most existing greedy algorithms need the prior knowledge signal's sparsity, which unknown or even time-varying in actual applications, to determine stop threshold iterative process. Recently, power residual signal used compare with noise whether there some remaining. In this paper, statistical property proved follow chi square distribution parameter L(M-k), where L and M size observation matrix, k iteration times....
With the main motivation of improving asymptotic properties, and with reference to recent developments on parallel stochastic approximation methods, a novel approach adaptive Robbins-Monro algorithms (1951) is developed. The authors take previously developed algorithm as their point departure suggest convex combination approach. essence this that in lieu using single observer, collection observers which operate used estimate same system. At any time, all observation possibly different noise...
Adaptive filtering with delayed data is examined in detail. The recursive algorithm developed has two features: signals are allowed, and parallel implementation via pipelining structure can be incorporated into the framework of algorithm. considered a natural generalization classical adaptive filter procedures. It shown that convergence probability one preserved when appear A simple example given to demonstrate properties. demonstrated delays do not harm computation procedure as far...