Adaptive FIR filter for frequency estimation of sinusoids using particle swarm optimization

Elissa, Fadia B.; Mismar, M. J.

doi:10.1109/icedsa.2016.7818529

Cited by 4 publications

(4 citation statements)

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“…Additionally, the usage of random search optimization processes towards estimating frequencies and power of sinusoidal signals has been offered; the author in [6] uses a population based stochastic method, Particle Swarm Optimization (PSO) to estimate diverse sinusoids' frequencies along with their real power, without previous knowledge of the signals' number, amplitude, nor phase [7]. Furthermore, random search is employed to evaluate the direction-of-arrival and power estimation of source signals [8].…”

Section: Introductionmentioning

confidence: 99%

Frequency and power estimation of sinusoids using eigen-approach

Mismar

Elissa

2019

MATEC Web Conf.

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Frequency and power estimation of sinusoidal signals, has captured the attention of researchers due to its important applications. This paper involves a frequency and power estimation method of unidentified number of source sinusoidal signals using Eigen technique. The eigenvector corresponding to the correlation matrix’s minimum eigenvalue yields the minimum power of the FIR filter’s output power. The corresponding roots are determined using the filter polynomial and root elimination technique is used to determine the pseudospectrum. Consequently, by deploying the pseudospectrum and filter polynomial, the real power spectrum is derived.

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Section: Introductionmentioning

confidence: 99%

Frequency and power estimation of sinusoids using eigen-approach

Mismar

Elissa

2019

MATEC Web Conf.

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“…Specifically, (GA) was used in adjusting an adaptive antennae receiver (Kokai et al, 2005) and (PSO) in solving the optimisation problem of topology of radio frequency identification network (Zhang et al, 2011). Moreover, these algorithms have proven to be very efficient when used for frequency estimation (Elissa and Mismar, 2016;Mismar and Ismail, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…This is achieved as the roots are on the unit circle and hence minimal iterations are needed. Extending the work presented in Elissa and Mismar (2016) the real power spectrum is derived from the pseudo-spectrum by using filter delay vectors at the frequencies of the roots. The results demonstrate that by controlling the roots of the filter polynomial, the frequency, the power of the source signals, and the number of signals is estimated accurately.…”

Section: Introductionmentioning

confidence: 99%

Adaptive FIR filter for frequency and power estimation of sinusoids

Elissa

Mismar

2019

IJES

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A new frequency and power estimation method which detects frequencies of unknown numbers of source sinusoidal signals is suggested. The system contains an adaptive finite impulse response filter (FIR) which exploits one of several random search algorithms, particle swarm optimisation (PSO). PSO will work on minimising the overall output power by finding the frequencies of the roots on the unit circle. The pseudo-spectrum is achieved by frequency elimination of the roots and the real power spectrum is derived from the pseudo-spectrum and the filter polynomial. The frequency, real power, and number of source signals are all estimated from the real power spectrum. After estimating the frequencies of the undesired interfering signals, the system suppresses these signals while maintaining the strength of the desired signals.

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“…III. DESIGN AND IMPLEMENTATION An approximate filter model is achieved by applying the window method, which consists of finding the appropriate coefficients of a FIR of order L, making a convolution between the transfer function that characterizes the ideal filter by different values of a function called a window [12].…”

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confidence: 99%

Strategy for Designing Digital Filters for Embedded Platforms

Ariza¹,

Jacinto²,

Martinez³

2018

IJET

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This research focuses on the use of a methodological strategy for the implementation of digital filters on embedded systems. The purpose of the document is to perform a results analysis for the implementation of a FIR filter on a PSOC 5LP, comparing the proposed strategy with the actual response of the architecture used by the selected platform. In the document it will be shown the block diagram of the proposed solution, configuration parameters, algorithmic structures used, computational tools used to calculate operating parameters and the development of validation tests for the proposed strategy. Keyword-Digital filters, PSOC, FIR, embedded systems. I. INTRODUCTION The growing development of electronic components of small size, has allowed to build high quality electronic prototypes at low cost. In particular, prototypes that incorporate a digital processing system, have the ability to execute tasks recorded in their memory. This characteristic makes them a central controller of certain embedded systems, which gives them a functional advantage over conventional digital circuits, since they adapt to different types of applications with a single processing system [1-3]. The versatility of this type of controller has allowed its manufacture by several manufacturers, among which stand out the family of programmable controllers (PSOC: Programmable System on Chip) developed by Cypress in 2002. The PSOC are integrated circuits of programmable matrix, that is, the peripherals are defined by sets of functions and assigned to any terminal of the integrated circuit [2-3]. The manufacture of PSOC and other digital systems allows the processing of large volumes of information and signals in hardware digitally, although the speed of response in these applications is reduced to the function of the scheduled tasks and the architecture of the controller. The applications in the areas associated to the digital processing of the signals have had great advances, some representative examples are: facial or voice recognition [4-5], the development of intelligent sensors [6], the manufacture of wireless audio transmitters [7], the design of biometric systems [8], among others. One limitation that can occur during the implementation of this type of applications, is the increase in the margin of error between the measured value and the real value, since the signals that reach a controller can change its magnitude with a case of an external client, for example: the environmental conditions. This limitation has been partly solved thanks to the development of filters, which have the function of eliminating the effect caused by the factors [9-10]. In electronics, two types of filters are found, analog and digital. Analog filters are usually built as passive components such as resistors, capacitors and coils. Digital filters are constructed from algorithms that adapt to different processing systems, to improve the result obtained when interpreting a digital signal, for example, to eliminate the noise present in an audio signal reproduced by...

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Adaptive FIR filter for frequency estimation of sinusoids using particle swarm optimization

Cited by 4 publications

References 10 publications

Frequency and power estimation of sinusoids using eigen-approach

Frequency and power estimation of sinusoids using eigen-approach

Adaptive FIR filter for frequency and power estimation of sinusoids

Strategy for Designing Digital Filters for Embedded Platforms

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