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Adaptive Filters
Published in John T. Taylor, Qiuting Huang, CRC Handbook of ELECTRICAL FILTERS, 2020
Marcello L. R. de Campos, Andreas Antoniou
An adaptive filter is a time-dependent filter that has its coefficients, and in some cases its order, automatically adjusted in real time in order to improve its performance. Although adaptive filtering is a relatively young area in signal processing, recursively adjusting the parameters of a function based on observed data goes back centuries. The idea is to compare the solution obtained from the parameterized function with some reference data and to produce a new set of parameters that is closer in some sense to the optimal solution. Although intuitively sound, the formalization of the method can become very involved. For instance, carefully chosen criteria must guide the actions of the algorithm, and interference by the designer must be absent or be kept to a minimum. Furthermore, adaptive filters must be reliable because in some applications failure can be disastrous.
Introduction to Adaptive Filters
Published in Vijay K. Madisetti, The Digital Signal Processing Handbook, 2017
An adaptive filter is a computational device that attempts to model the relationship between two signals in real time in an iterative manner. Adaptive filters are often realized either as a set of program instructions running on an arithmetical processing device such as a microprocessor or DSP chip, or as a set of logic operations implemented in a field-programmable gate array or in a semi-custom or custom VLSI integrated circuit. However, ignoring any errors introduced by numerical precision effects in these implementations, the fundamental operation of an adaptive filter can be characterized independently of the specificphysical realization that it takes. For this reason, we shall focus on the mathematical forms of adaptive filters as opposed to their specific realizations in software or hardware. Descriptions of adaptive filters as implemented on DSP chips and on a dedicated integrated circuit can be found in [1–3] and [4], respectively.
Introduction
Published in Peter M. Clarkson, Optimal and Adaptive Signal Processing, 2017
In Chapter 4, we turn our attention to our second major objective; the study of adaptation in signal processing. Adaptive signal processing is concerned with the design, analysis and implementation of systems whose structure changes in response to the incoming data. That is, adaptive processing deals with the class of data adaptive techniques. There are two basic factors that motivate adaptive processing. Firstly, we often need to analyze data whose properties are unknown a priori. It is difficult to construct a sensible processing strategy under these conditions. An adaptive processing scheme iterates towards the required processing strategy using each sample of data as it is measured. The second motivating factor for adaptive schemes is the existence of systems whose properties change with time. For such signals, adaptive processing and specifically adaptive filtering, provides a method whereby one may track the changes in the data and thus maintain a strategy which is consistent with the processing aims. Essentially, an adaptive filter is one whose structure can be adjusted in response to changing signal properties.
A new integrated analytics approach for wind turbine fault detection using wavelet, RLS filter and random forest
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Shiyao Qin, Mengzhou Zhang, Xiaojing Ma, Mei Li
In the usual filtering situations, the theory of filtering from the frequency domain has been quite sound. As long as the corresponding design indexes are given, it is very convenient to design the filters that meet the requirements. However, in more general cases, the environment of the filter that people work is time-varying, which results in the decline of the performance of the previously designed filter, and even failure to use. Table the model output residual is a time-varying signal. One of the solutions is to use the adaptive filter to track the characteristics of the channel at real time, and then adjust the parameters of the equalizer constantly to keep it in the optimal state. The adaptive filter is generally a transverse filter with adjustable order (FIR filter or IIR filter) that adjusts the fixed order to realize adaptive filtering. The input signal x(k) filtered by adjustable parameters is to generate an output signal y(k)The reference signal d(k) is to generate the error signal e(k).e(k) is used to adjust the filter parameters through a kind of adaptive algorithm such as RLS.
A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti-jamming
Published in IETE Journal of Research, 2021
Kambham Jacob Silva Lorraine, Madhu Ramarakula
Adaptive filtering is one of the most widely used pre-correlation methods. It is a cost-effective solution. However, it gets limited as it requires prior information regarding the structure and characteristics of the jamming signal. And also, they remove part of the signal spectrum which in turn reduces the signal-to-noise ratio (SNR) [20]. Some of the adaptive filtering techniques are notch filters, frequency-domain adaptive filter (FDAF), Kalman filters, approximate conditional mean (ACM) filter.
Harmonic current suppression method with adaptive filter for permanent magnet synchronous motor
Published in International Journal of Electronics, 2021
Zhe Song, Jun Yang, Xuesong Mei, Tao Tao, Muxun Xu
The adaptive filter can adjust the filter coefficients through the adaptive algorithm, so that the characteristics of the filter can change with the changes of harmonics and noise, so as to achieve an optimal filtering effect. The least mean square (LMS) algorithm is a common adaptive algorithm in adaptive filter, which has the advantages of simple structure and easy implementation. The block diagram of the adaptive filter is shown in Figure 5.