11 research outputs found
Robust Andrew's sine estimate adaptive filtering
The Andrew's sine function is a robust estimator, which has been used in
outlier rejection and robust statistics. However, the performance of such
estimator does not receive attention in the field of adaptive filtering
techniques. Two Andrew's sine estimator (ASE)-based robust adaptive filtering
algorithms are proposed in this brief. Specifically, to achieve improved
performance and reduced computational complexity, the iterative Wiener filter
(IWF) is an attractive choice. A novel IWF based on ASE (IWF-ASE) is proposed
for impulsive noises. To further reduce the computational complexity, the
leading dichotomous coordinate descent (DCD) algorithm is combined with the
ASE, developing DCD-ASE algorithm. Simulations on system identification
demonstrate that the proposed algorithms can achieve smaller misalignment as
compared to the conventional IWF, recursive maximum correntropy criterion
(RMCC), and DCD-RMCC algorithms in impulsive noise. Furthermore, the proposed
algorithms exhibit improved performance in partial discharge (PD) denoising.Comment: 5 pages, 5 figure
LMS Adaptive Filters for Noise Cancellation: A Review
This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted
Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques
This thesis presents a study into nonlinearities in the radar receiver and investigates
advanced digital signal processing (DSP) techniques capable of mitigating the resultant
deleterious effects. The need for these mitigation techniques has become more prevalent
as the use of commercial radar sensors has increased rapidly over the last decade. While
advancements in low-cost radio frequency (RF) technologies have made mass-produced
radar systems more feasible, they also pose a significant risk to the functionality of the
sensor. One of the major compromises when employing low-cost commercial off-theshelf
(COTS) components in the radar receiver is system linearity. This linearity trade-off
leaves the radar susceptible to interfering signals as the RF receiver can now be driven
into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear
regime as distortion is observed in the radar output if they do. If radars are to maintain
operational performance in an RF environment that is becoming increasingly crowded,
novel techniques that allow the sensor to operate in the nonlinear regime must be developed.
Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear
receiver problem in modern radar. While there is very little work published on this topic
in the radar literature, inspiration can be taken from the related field of communications
where techniques have been successfully employed.
It is clear from the communications literature that for any mitigation algorithm to be
successful, the mechanisms driving the nonlinear distortion in the receiver must be understood
in great detail. Therefore, a behavioural modelling technique capable of capturing
both the nonlinear amplitude and phase effects in the radar receiver is presented before
any mitigation techniques are studied. Two distinct groups of mitigation algorithms
are then developed specifically for radar systems with their performance tested in the
medium pulse repetition frequency (MPRF) mode of operation. The first of these is the
look-up table (LUT) approach which has the benefit of being mode independent and computationally
inexpensive to implement. The limitations of this communications-based
technique are discussed with particular emphasis placed on its performance against receiver
nonlinearities that exhibit complex nonlinear memory effects. The second group
of mitigation algorithms to be developed is the forward modelling technique. While this
novel technique is both mode dependent and computationally intensive to implement,
it has a unique formalisation that allows it to be extended to include nonlinear memory
effects in a well-defined manner. The performance of this forward modelling technique
is analysed and discussed in detail.
It was shown in this study that nonlinearities generated in the radar receiver can be
successfully mitigated using advanced DSP techniques. For this to be the case however,
the behaviour of the RF receiver must be characterised to a high degree of accuracy both
in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects
are significant in the radar receiver, it was shown that memoryless mitigation techniques
can become decorrelated drastically reducing their effectiveness. Importantly however, it
was demonstrated that the LUT and forward modelling techniques can both be extended
to compensate for complex nonlinear memory effects generated in the RF receiver. It was
also found that the forward modelling technique dealt with the nonlinear memory effects
in a far more robust manner than the LUT approach leading to a superior mitigation
performance in the memory rich case
On data-selective learning
Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancellation, signal prediction, and stock market. Therefore, reducing the energy consumption of the adaptive filtering algorithms has great importance, particularly in green technologies and in devices using battery. In this thesis, data-selective adaptive filters, in particular the set-membership (SM) adaptive filters, are the tools to reach the goal. There are well known SM adaptive filters in literature. This work introduces new algorithms based on the classical ones in order to improve their performances and reduce the number of required arithmetic operations at the same time. Therefore, firstly, we analyze the robustness of the classical SM adaptive filtering algorithms. Secondly, we extend the SM technique to trinion and quaternion systems. Thirdly, by combining SM filtering and partialupdating, we introduce a new improved set-membership affine projection algorithm with constrained step size to improve its stability behavior. Fourthly, we propose some new least-mean-square (LMS) based and recursive least-squares based adaptive filtering algorithms with low computational complexity for sparse systems. Finally, we derive some feature LMS algorithms to exploit the hidden sparsity in the parameters.Filtros adaptativos são aplicados em diversos aparelhos eletrônicos e de comunicação, como smartphones, fone de ouvido avançados, DSP chips, antenas inteligentes e sistemas de teleconferência. Eles também têm aplicação em várias áreas como identificação de sistemas, equalização de canal, cancelamento de eco, cancelamento de interferência, previsão de sinal e mercado de ações. Desse modo, reduzir o consumo de energia de algoritmos adaptativos tem importância significativa, especialmente em tecnologias verdes e aparelhos que usam bateria. Nesta tese, filtros adaptativos com seleção de dados, em particular filtros adaptativos da famÃlia set-membership (SM), são apresentados para cumprir essa missão. No presente trabalho objetivamos apresentar novos algoritmos, baseados nos clássicos, a fim de aperfeiçoar seus desempenhos e, ao mesmo tempo, reduzir o número de operações aritméticas exigidas. Dessa forma, primeiro analisamos a robustez dos filtros adaptativos SM clássicos. Segundo, estendemos o SM aos números trinions e quaternions. Terceiro, foram utilizadas também duas famÃlias de algoritmos, SM filtering e partial-updating, de uma maneira elegante, visando reduzir energia ao máximo possÃvel e obter um desempenho competitivo em termos de estabilidade. Quarto, a tese propõe novos filtros adaptativos baseado em algoritmos least-mean-square (LMS) e mÃnimos quadrados recursivos com complexidade computacional baixa para espaços esparsos. Finalmente, derivamos alguns algoritmos feature LMS para explorar a esparsidade escondida nos parâmetros
Massachusetts Domestic and Foreign Corporations Subject to an Excise: For the Use of Assessors (2004)
International audienc