22-05-2013, 04:59 PM
ADAPTIVE FILTER FOR NOISE CANCELLATION MINI PROJECT
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ABSTRACT
An adaptive filter is a self-designing system that relies for its operation on a recursive
algorithm, which makes it possible for the filter to perform satisfactorily in an
environment where knowledge of the relevant statistics is not available. Adaptive filters
are classified into two main groups: linear and non linear. Linear adaptive filters
compute an estimate of a desired response by using a linear combination of the available
set of observables applied to the input of the filter. Otherwise, the adaptive filter is
said to be nonlinear.Adaptive filters find applications in highly diverse fields: channel
equalization, system identification, predictive deconvolution, spectral analysis, signal
detection, noise cancellation, and beamforming.In this proposed project the application
of the LMS criterion to cancel an undesirable sinusoidal noise is to be implemented.
Here LMS criterion is to be used in an adaptive FIR filter.
INTRODUCTION
Communications systems development increases considerably obtaining more troubles
as additive noise, signal interference and echo, therefore errors in data transmission
are generated, but adaptive filter is an option to reduce these channel effects.
Adaptive filters are systems with four terminals as showed in Fig 1., where x is the
input signal, d is the wished signal, y is output signal filter and e is the output filter error. Adaptive filters design technique may be digital, analog or mixed. Every technique
presents advantages and disadvantages, for example, analog adaptive filters are very
fast, but offset avoids to get the least error . Digital filters are slow but precise, because is
necessary the use of a lot of components, due to floating point operations . Mixed design
(analog and digital), offers a good compromise between precision and speed, but VLSI
(Very Large Scale Integration) design is more complicated , because is necessary to
separate analog and digital components inside the chip. In this project a noise canceller
employing a modified LMS (Least Mean Square) adaptive algorithm is implemented.
The objectives are digital design reduction of an adaptive filter, making use of a low
complexity algorithm and to achieve improvement in convergence speed.
ADAPTIVE LMS ALGORITHM
Actually there are different adaptive algorithms like RLS (Recursive Least Square)
or LMS. These algorithms works on time domain and also exist frequency domain algorithms.
The time domain algorithm often used is LMS, because its computational
complexity lets an easy implementation on a chip. The LMS algorithm is based on
gradient search error, the mathematical expression .
Hardware Platform-DSK 6416
Signal processors such as the TMS320C6x (C6x) family of processors are like fast
special-purpose microprocessors with a specialized type of architecture and an instruction
set appropriate for signal processing. The C6x notation is used to designate a
member of Texas Instruments TMS320C6000 family of digital signal processors. The
architecture of the C6x digital signal processor is very well suited for numerically intensive
calculations. Based on a very-long-instruction-word (VLIW) architecture, the C6x
is considered most powerful processor. Digital signal processors are used for a wide
range of applications, from communications and controls to speech and image processing.
The general-purpose digital signal processor is dominated by applications in communications
(cellular). Applications embedded digital signal processors are dominated
by consumer products. They are found in cellular phones, fax/modems, disk drives,
radio, printers, hearing aids, MP3 players, high-definition television (HDTV), digital
cameras, and so on. These processors have become the products of choice for a number
of consumer applications, since they have become very cost-effective. They can handle
different tasks, since they can be reprogrammed readily for a different application.
DSP techniques have been very successful because of the development of low-cost
software and hardware support. For example, modems and speech recognition can be
less expensive using DSP techniques. DSP processors are concerned primarily with
real-time signal processing. Real-time processing requires the processing to keep pace
with some external event, whereas non-real-time processing has no such timing constraint.
They are easy to use, flexible, and economical. The DSK package is powerful,
yet relatively inexpensive, with the necessary hardware and software support tools for
real-time signal processing . It is a complete DSP system. The DSK board includes the
C6416 floating-point digital signal processor and a 32-bit stereo codec TLV320AIC23
(AIC23) for input and output. The onboard codec AIC23 uses a sigmadelta technology
that provides ADC and DAC.
Circuit Diagram Description
The input to the DSK is a contaminated signals (YK) containing both the desired
signal (SK) and noise (NK).Both are uncorrelated with each other. Another input is
the noise signal (XK).The input (XK) is correlated with (NK) and uncorrelated with
(SK).The inputs are taken from function generators. The signals are given to a two
input one output adapter. The adapter has one input connector white (or silver) that
represents the left channel and another input connector red (or gold) that represents the
right channel. The output of the adapter is given to the LINE IN of the DSK. By using
the C program in the processor we can separate the signals from the left and the right
channels. From the LINE IN, the signals are given to the onboard codec (AIC23). It
uses a sigmadelta technology that provides ADC and DAC. The given analog signals
are converted to digital using the ADC. The digital values are read by the C program for
adaptive filter. By using the program we can separate the desired signal from noise. The
output is in digital form which is converted into analog by using the DAC in AIC23.
The output can be displayed on the CRO through the LINE OUT of the DSK. As an
example the contaminated signal is sine1500Hz (desired signal) + sine312Hz (noise
signal). Another noise signal is cosine312Hz which is uncorrelated with desired signal
and correlated with the noise signal (sine312Hz). By using the LMS algorithm we can
separate the desired signal from the contaminated signal.
RESULT
Code Composer Studio, DSK and TMS320C6713 processor has been familiarized.
Adaptive filter using TMS320C6713 processor has been done successfully. Desired
frequency is fed to the left channel and undesired to the right channel. The desired
signal is obtained as the output which is displayed on a DSO.Different waveforms used
and obtained are shown below.