Pulse Doppler Radar Report

7/29/2019 Pulse Doppler Radar Report

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IMPLEMENTATION OF AN ADAPTIVE FIR FILTER FOR PULSE DOPPLER RADAR

Dept Of E&C,VDRIT, Haliyal Page 1

CHAPTER 1

INTRODUCTION

BACKGROUND

In the past decade, the unprecedented advances in VLSI technology have stimulated great

interests in developing special purpose, parallel processor arrays to facilitate real time digital

signal processing. Parallel computing systems such as systolic arrays and wave front arrays have

been extensively studied. The basic arithmetic computation of these parallel VLSI arrays has

often been implemented with a multiplication and accumulation (MAC) unit, because these

operations arise frequently in DSP algorithms. The reduction in hardware cost also motivated the

development of more sophisticated DSP algorithms to enhance the performance of modern

digital signal processing systems. Many of these new algorithms require the evaluation of

elementary functions, such as trigonometric, exponential, and logarithm functions, which cannot

be evaluated efficiently with MAC based arithmetic units. Consequently, when DSP algorithms

incorporate these elementary functions, it is not unusual to observe significant performance

degradation.

CORDIC (Coordinate Rotation Digital Computer) was introduced in 1959 by Jack E.

Volder. It is very efficient to compute the values of sin, cosine, tan , sinh, Cosh, tanh. Its a

Hardware Efficient Algorithm. It is an iterative Algorithm for Circular Rotation. It requires no

Multiplication. Delay/Hardware cost comparable to division or square rooting. To evaluate

trigonometric functions we have many approaches such as

Table lookup Polynomial approximations CORDIC

Compared to other approaches, CORDIC is a clear winner when:

Hardware Multiplier is unavailable (e.g. microcontroller) You wants to save the gates required to implement (e.g. FPGA)


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Its basic ideas is-

Embedding of elementary function evaluation as a generalized rotation operation. Decompose rotation operation into successive basic rotations. Each basic rotation can be realized with shift and add arithmetic operations. Shift and-add

arithmetic operations.

1.2 Objective of the Project Work

The Adaptive filter can be realized for Doppler filtering function and thus play huge role

in target detection and clutter cancellation. The filtering is achieved through a certain number of

FIR filters. Within the radar receiver response curve, each FIR filter is centered on definite

Doppler frequency, that is = 0 Hz to = , where fd is the Doppler frequency and isPulse Repetition Frequency. This project deals with the implementation of Adaptive FIR Filter

with CORDIC algorithm for Pulse Doppler Radar. A fully pipelined CORDIC processor is

designed with the help of Verilog HDL and synthesized. An exhaustive test bench is also written

to simulate the functionality of the processor.

1.3 Organization of the report

The rest of report is organized as follows

Chapter 2 Describes about basics of CORDIC and various CORDIC architectures. Chapter 3 Describes the Adaptive Filter basic operation. Chapter 4Describes Software requirements and specifications. Chapter 5 Describes Implantations. Chapter 6Draws conclusions on this work and give a short discussion on

future work.


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CHAPTER 2

CORDIC

2.1 CORDIC Architecture

CORDIC (for Coordinate Rotation Digital Computer), also known as the digit-by-digit

method and Volder's algorithm, is a simple and efficient algorithm to calculate hyperbolic and

trigonometric functions. It is commonly used when no hardware multiplier is available (e.g.,

simple microcontrollers and FPGAs) as the only operations it requires are addition, subtraction,

bit shift and table lookup.

Fig. 2.1 Single stage iterative CORDIC Architecture

CORDIC is a class of shift-add algorithms for rotating vectors in a plane. In a nutshell,

the CORDIC rotator performs a rotation using a series of specific incremental rotation angles

selected so that each is performed by a shift and add operation.

This algorithm allows implementation of trigonometric functions like sine, cosine, magnitude

and phase with great precision by using just simple shift and adding operations. Although the


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same functions can be implemented using multipliers, variable shift registers or Multiply

Accumulator (MAC) units, but CORDIC can implement these functions efficiently while saving

enough silicon area which is considered to be primary design criteria in VLSI technology.

Rotation of unit vectors provides us with a way to accurately compute trig functions, as well as a

mechanism for computing the magnitude and phase angle of an input vector. Vector rotation is

also useful in a host of DSP applications including modulation and Fourier Transforms.

CORDICs can also be implemented in many ways, including a single-stage iterative

method, which requires very few gates when compared to multiplier circuits. Also, CORDICs

can compute many functions with precisely the same hardware, so they are ideal for applications

with an emphasis on reduction of cost (e.g. by reducing gate counts in FPGAs) over speed. An

example of this priority is in pocket calculators, where CORDICs are very frequently used. The

implementation using MATLAB-Simulink modeling is shown in figure below that further can be

implemented in FPGA using System Generator tool.

2.2 CORDIC Algorithm

The Volders CORDIC algorithm is derived from the general equations of vector

rotation. The theory of CORDIC computation is to decompose the desired rotation angle into the

weighted sum of a set of predefined elementary rotation angles. Each of them can be

accomplished with simple shift add operation for a desired rotational angle . It can be

represented forMiterations of an input vector setting initial conditions: =x, =y, and= as = . If= 0 = i.e.the total accumulatedrotation angle is equal to ; i M-1 denote a sequence of 1s that determine the directionof each elementary rotation. When M is the total number of elementary rotation angles, i-th

angle is given by:

{

Where m=0, 1 and

coordinate system respectively. For a given value of, the CORDIC iteration is given by:


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[]

[] and , where,

In case of counter clockwise rotation of a vector, the recursively updated equations are -

= =

The equations can be simplified in the form of:

= - = +

Here, is restricted to . Thus, multiplication is transformed to an arithmetic right shift.Since cosine is an even function, therefore . The iterative equation can bereduced to = - and = +. Where, is known as gain factor for each iteration. If M iterations are performed, then scalefactor, k, is defined as the multiplication of every . So, .The elementary functions sine and cosine can be computed using the rotation mode of the

CORDIC algorithm, if the initial vector starts at (|K|, 0) with unit length.

Table I. Pre-Computed Angles

I in radians0 1 45 0.7854

1 0.5 26.565 0.4636

2 0.25 14.063 0.2450

3 0.125 7.125 0.1244

4 0.0625 3.576 0.0624

5 0.03125 1.7876 0.0312


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2.3 Pipelined Architecture of CORDIC

In Pipelined CORDIC architecture, number of rotational modules is incorporated and

each module is responsible for one elementary rotation. The modules are cascaded through

intermediate latches (Fig. 1). Every stage within the pipelined CORDIC architecture [4] only

adder/subtractor is used. The shift operations are hardwired using permanent oblique bus

connections to perform multiplications by . The precomputed values, as given in Table I, ofi-th iteration angle required at each module is stored at a ROM memory location. The delay isadjusted by using proper bit-length in the shift register. Since no sign detection is needed to force

=0, the carry save adders are well suited in this architecture.

Fig.2.3 Pipelined CORDIC Architecture.

The use of these adders reduces the stage delay significantly. With the pipelining

architecture, the throughput of the architecture is increased to many folds as the throughput is

given by: If an iterative implementation of the CORDIC isused, the processor would take several clock cycles to give output for a given input. But in the

pipelined architecture, each pipeline stage takes exactly one clock cycle to pass one output. The

most recurrent problem for a CORDIC implementation is overflow. Since the first tangent value

is then rotation range will be . The difference in binary representationbetween these two angles is one bit. Overflow arises when a rotational angle crosses a positive

right angle to a negative one. To avoid overflow, an overflow control is added. It checks for the

sign of the operands involved in addition or subtraction and the result of the operation.


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CHAPTER 3

ADAPTIVE FILTER BASIC OPERATION

Introduction

An Adaptive filter is a filter that self-adjusts its transfer function according to an

optimization algorithm driven by an error signal. Because of the complexity of the optimization

algorithms, most adaptive filters are digital filters. By way of contrast, a non-adaptive filter has a

static transfer function. Adaptive filters are required for some applications because some

parameters of the desired processing operation (for instance, the locations of reflective surfaces

in a reverberant space) are not known in advance. The adaptive filter uses feedback in the form

of an error signal to refine its transfer function to match the changing parameters.

Generally speaking, the adaptive process involves the use of a cost function, which is a

criterion for optimum performance of the filter, to feed an algorithm, which determines how to

modify filter transfer function to minimize the cost on the next iteration.

As the power of digital signal processors has increased, adaptive filters have become

much more common and are now routinely used in devices such as mobile phones and other

communication devices, camcorders and digital cameras, and medical monitoring equipment.

Figure 3.1 Scheme of an adaptive filter

An adaptive filter is required when either the fixed specifications are unknown or the

specifications cannot be satisfied by time-invariant filters. Strictly speaking an adaptive filter is a

nonlinear filter since its characteristics are dependent on the input signal and consequently the

homogeneity and additivity conditions are not satisfied. However, if we freeze the filter


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parameters at a given instant of time, most adaptive filters considered in this text are linear in the

sense that their output signals are linear functions of their input signals.The adaptive filters are

time-varying since their parameters are continually changing in order to meet a performance

requirement. In this sense, we can interpret an adaptive filter as a filter that performs the

approximation step on-line. Usually, the definition of the performance criterion requires the

existence of a reference signal that is usually hidden in the approximation step of fixed-filter

design.The development of digital very large scale integration (VLSI) technology allowed the

widespread use of adaptive signal processing techniques in a large number of applications.

3.1 Adaptive Signal Processing

The design of digital filters with fixed coefficients requires well defined prescribed

specifications. However, there are situations where the specifications are not available, or is time

varying. The solution in these cases is to employ a digital filter with adaptive coefficients, known

as adaptive filters.Since no specifications are available, the adaptive algorithm that determines

the updating of the filter coefficients requires extra information that is usually given in the form

of a signal. This signal is in general called a desired or reference signal, whose choice is

normally a tricky task that depends on the application. Adaptive filters are considered nonlinear

systems; therefore their behavior analysis is more complicated than for fixed filters.

Figure 3.2 General adaptive-filter configurations.

The general set up of an adaptive-filtering environment is illustrated in Fig. 3.1, where k

is the iteration number,x(k) denotes the input signal,y(k) is the adaptive-filter output signal, and


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d(k) defines the desired signal. The error signal e(k) is calculated as d(k) y(k). The error signal is then

used to form a performance (or objective) function that is required by the adaptation algorithm in

order to determine the appropriate updating of the filter coefficients. The minimization of the

objective function implies that the adaptive-filter output signal is matching the desired signal in

some sense.

The complete specification of an adaptive system, as shown in Fig. 3.2, consists of three

items:

1) Application: The type of application is defined by the choice of the signals acquired from the

environment to be the input and desired-output signals. The number of different applications in

which adaptive techniques are being successfully used has increased enormously during the last

two decades. Some examples are echo cancellation, equalization of dispersive channels, system

identification, signal enhancement, adaptive beam forming and noise cancelling.

2) Adaptive-Filter Structure: The adaptive filter can be implemented in a number of different

structures or realizations. The choice of the structure can influence the computational complexity

(amount of arithmetic operations per iteration) of the process and also the necessary number of

iterations to achieve a desired performance level. Basically, there are two major classes of

adaptive digital filter realizations, distinguished by the form of the impulse response, namely the

finite-duration impulse response (FIR) filter and the infinite-duration impulse response (IIR)

filters. FIR filters are usually implemented with non recursive structures, whereas IIR filters

utilize recursive realizations.

Adaptive FIR filter realizations: The most widely used adaptive FIR filter structure is thetransversal filter, also called tapped delay line, that implements an all-zero transfer

function with a canonic direct form realization without feedback. For this realization, the

output signaly (k) is a linear combination of the filter coefficients, that yields a quadratic

mean-square error (MSE = E [|e(k)|2]) function with a unique optimal solution. Other

alternative adaptive FIR realizations are also used in order to obtain improvements as

compared to the transversal filter structure, in terms of computational complexity, speed

of convergence, and finite word length properties as will be seen later in the book.

Adaptive IIR filter realizations: The most widely used realization of adaptive IIR filtersis the canonic direct form realization [5], due to its simple implementation and analysis.

However, there are some inherent problems related to recursive adaptive filters which


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are structure dependent, such as pole-stability monitoring requirement and slow speed

of convergence. To address these problems, different realizations were proposed

attempting to overcome the limitations of the direct form structure. Among these

alternative structures, the cascade, the lattice, and the parallel realizations are

considered.

3) Algorithm: The algorithm is the procedure used to adjust the adaptive filter coefficients in

order to minimize a prescribed criterion. The algorithm is determined by defining the search

method (or minimization algorithm), the objective function, and the error signal nature. The

choice of the algorithm determines several crucial aspects of the overall adaptive process, such

as existence of sub-optimal solutions, biased optimal solution, and computational complexity.

The basic objective of the adaptive filter is to set its parameters, (k), in such a way that its

output tries to minimize a meaningful objective function involving the reference signal. Usually,

the objective function F is a function of the input, the reference, and adaptive-filter output

signals, i.e., F= F[x(k), d(k), y(k)]. A consistent definition of the objective function must satisfy

the following properties:

Non-negativity: F[x(k), d(k), y(k)] 0, y(k), x(k), and d(k);

Optimality: F[x(k), d(k), d(k)] = 0.

3.2 Applications

In this section, we discuss some possible choices for the input and desired signals

and how these choices are related to the applications. Some of the classical applications of

adaptive filtering are system identification, channel equalization, signal enhancement, and

prediction.

Figure 3.3 System identification.


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In the system identification application, the desired signal is the output of the unknown

system when excited by a broadband signal, in most cases a white-noise signal. The broadband

signal is also used as input for the adaptive filter as illustrated in Fig. 3.3. When the output MSE

is minimized, the adaptive filter represents a model for the unknown system.

Figure 3.4 Channel equalization.

The channel equalization scheme consists of applying the originally transmitted signal

distorted by the channel plus environment noise as the input signal to an adaptive filter, whereas

the desired signal is a delayed version of the original signal as depicted in Fig. 3.4. This delayed

version of the input signal is in general available at the receiver in a form of standard training

signal. In a noiseless case, the minimization of the MSE indicates that the adaptive filter

represents an inverse model (equalizer) of the channel.

Figure 3.5 Signal enhancement ( (k) and (k) are noise signals correlated to each other.In the signal enhancement case, a signalx(k) is corrupted by noise

(k), and a signal

(k) correlated to the noise is available (measurable). If (k) is used as an input to the adaptivefilter with the signal corrupted by noise playing the role of the desired signal, after convergence

the output error will be an enhanced version of the signal. Fig.3.5. Illustrates a typical signal

enhancement setup.


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Finally, in the prediction case the desired signal is a forward (or eventually a backward)

version of the adaptive-filter input signal as shown in Fig. 3.6 After convergence, the adaptive

filter represents a model for the input signal, and can be used as a predictor model for the input

signal.

Figure 3.6 Signal prediction.

3.3 Example

In this example, the reference (or desired) signal consists of a discrete-time triangular

waveform corrupted by a colored noise. Fig. 3.7 shows the desired signal. The adaptive-filter

input signal is a white noise correlated with the noise signal that corrupted the triangular

waveform, as shown in Fig. 3.8.

The coefficients of the adaptive filter are adjusted in order to keep the squared value

of the output error as small as possible. As can be noticed in Fig. 3.9, as the number of iterations

increase the error signal resembles the discrete-time triangular waveform shown in the same

figure (dashed curve).

Figure 3.7 Desired signal.


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Figure 3.8 Input signal.

Figure 3.9 Error signal (continuous line) and triangular waveform (dashed line).

3.4 Adaptive Filter

An adaptive filter is a filter that self-adjusts its transfer function according to an

optimization algorithm driven by an error signal. Because of the complexity of the optimization

algorithms, most adaptive filters are digital filters. By way of contrast, a non-adaptive filter has a

static transfer function. Adaptive filters are required for some applications because some

parameters of the desired processing operation (for instance, the locations of reflective surfaces

in a reverberant space) are not known in advance. The adaptive filter uses feedback in the form

of an error signal to refine its transfer function to match the changing parameters.As the power of


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digital signal processors has increased, adaptive filters have become much more common and are

now routinely used in devices such as mobile phones and other communication devices,

camcorders and digital cameras, and medical monitoring equipment.

Figure 4 Scheme of an adaptive filter

ARCHITECTURE OF ADAPTIVE FILTER

The architecture of the adaptive filter mainly consists of four important blocks

Input Pulse Doppler Radar Adaptive Filter Output

Input:

It is an analog signal (contains original and distortion information) applied to the filter from

an external source in order to produce a specified output

Pulse Doppler Radar:

Pulse-Doppler is a 4D radar system capable of detecting both target 3D location as well asmeasuring radial velocity (range-rate). It uses the Doppler Effect to avoid overloading computers

and operators as well as to reduce power consumption. RF energy returning from airborne

objects and spacecraft are combined for successive target reflections returning from a dozen or

more transmit pulses, and these are integrated using Pulse-Doppler signal processing. Pulse-

Doppler reduces microwave power emission and weigh sufficiently for safe and effective use on


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aircraft. Pulse-Doppler radar has fundamental characteristics that differentiate it from pulse-

amplitude time-domain radar and continuous-wave frequency-domain radar.

Improved detection in high-clutter environments Greater track reliability using feedback Passive vehicle type classification Unattended operationPulse-Doppler radar is crucial for military applications called look-down/shoot-down, which

allows small fast-moving objects to be detected near terrain and weather. The purpose is to detect

targets while eliminating hostile environmental influences, such as reflections from weather, the

surface of the earth, and biological objects like birds, and electronic interference, which hide

reflected signals from aircraft, but which move much slower than aircraft. A secondary purpose

is to reduce transmit power while achieving acceptable performance for improved safety and

stealthy radar. In meteorological radars, pulse-Doppler measures instantaneous speed of

precipitations at discrete range intervals as the beam is slewed across the sky. Pulse-Doppler

radar is also the basis of synthetic aperture radar used with radar astronomy.

The main requirement of Radar Receiver is to detect target amidst clutter environment. The

clutter may be echo returns from stationary objects (Hills, electric poles etc.), vegetations or

from other unwanted sources. During target detection, target echo return may accompany with

clutters. In this paper, a bank of filters have been used in a required bandwidth of Doppler shift

to detect target signal and all the filters are designed in such a way that clutters are cancelled

adaptively.

Adaptive Filter:

Adaptive Filter is a filter in which it compares the analog signal x(k) with the filtered signal

d(k) and it produces the error signal e(k) which will be displayed as a output.

Output:

Actual response obtained from the adaptive filter, when the input is applied to it.


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CHAPTER 4

SOFTWARE REQUIREMENT AND SPECIFICATION

Minimum Software Requirement Specification:

1. Xilinx ISE simulator2. Software Verilog HDL3. MATLAB 2011a Simulink

6.1 Xilinx ISE

Xilinx ISEis a software tool produced by Xilinx for synthesis and analysis of HDL

designs, which enables the developer to synthesize ("compile") their designs, perform timing

analysis, examine RTL diagrams, simulate a design's reaction to different stimuli, and configure

the target device with the programmer.

The Web Edition is a free version of Xilinx ISE that can be downloaded or delivered by

mail for free. This edition provides synthesis and programming for a limited number of Xilinx

devices. In particular devices with lots of I/O and huge gate matrix are disabled. The low-cost

Spartan family of FPGAs is fully supported by this edition, as well as the family of CPLDs,meaning small developers and educational institutions have no overheads from the cost of

development software. License registration is required to use the Web Edition of Xilinx ISE,

which is free and can be renewed an unlimited number of times. The 12.2 version released in

2010-07-23 has a size of 3.02 GBSIGN SUITE 13

Verilog HDL

In the semiconductor and electronic design industry, Verilog is a hardware description

language (HDL) used to model electronic systems. Verilog HDL, not to be confused with VHDL

(a competing language), is most commonly used in the design, verification, and implementation

of digital logic chips at the register-transfer level of abstraction. It is also used in the verification

of analog and mixed-signal circuits.Hardware description languages such as Verilog differ from


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software programming languages because they include ways of describing the propagation of

time and signal dependencies (sensitivity).

MATLAB

MATLAB (matrix laboratory) is a numerical computing environment and fourth-

generation programming language. Developed by Math Works, MATLAB allows matrix

manipulations, plotting of functions and data, implementation of algorithms, creation of user

interfaces, and interfacing with programs written in other languages, including C, C++, Java, and

FORTRAN.

Although MATLAB is intended primarily for numerical computing, an optional toolbox

uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. Anadditional package, Simulink, adds graphical multi-domain simulation and Model-Based Design

for dynamic and embedded systems.

In 2004, MATLAB had around one million users across industry and academia.

MATLAB users come from various backgrounds of engineering, science, and economics.

MATLAB is widely used in academic and research institutions as well as industrial enterprises.

The MATLAB application is built around the MATLAB language. The simplest way to

execute MATLAB code is to type it in the Command Window, which is one of the elements of

the MATLAB Desktop. When code is entered in the Command Window, MATLAB can be used

as an interactive mathematical shell. Sequences of commands can be saved in a text file,

typically using the MATLAB Editor.

6.5 Security Requirements

Portability

The application is user-friendly so it is very easy for the user to understand and respond to the

same.

Reliability

This system has high probability to deliver us the correct simulation and the functionalities

available in the application.


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CHAPTER 5

IMPLANTATION

5.1 INTRODUCTION

The model of Adaptive FIR Filter architecture was discussed in the previous chapters. Now this

chapter deals with the simulation and synthesis results of the implemented algorithm. Here

Xilinx ISE Design Suit 13.1 tool is used in order to simulate the design and checks the

functionality of the design. The Appropriate test cases have been taken in order to test this

modeled algorithm.

Fig 5.1: RTL Schematic of Adaptive FIR_fIlter application pulse Doppler_cw

Fig5.2 Internal view of RTL Schematic of Adaptive FIR filter


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Its a internal view of Adaptive FIR filter for Pulse Doppler Radar in which it consists of mainly

Default clock driver Bank of filters D flip_flop

5.2 SIMULATION RESULTS

The test bench is developed in order to test the modeled design. This developed test

bench will automatically force the inputs, which were taken from the reference, and will make

the operations of algorithm to perform. The simulated waveforms have been discussed in this

section.

Fig 5.3 Simulation result for Adaptive FIR filter for Pulse Doppler Radar

Fig 5.4 Closer view of simulation result


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Fig 5.5 shows the Simulink model of Adaptive FIR Filter for Pulse Doppler Radar in which it

mainly consists of four blocks: Input, digital filter, Adaptive filter and output. Here x(k) is the

analog input signal which is applied to the digital filter (which is a cluttered signal) to convert

analog to digital and the output of the digital filter block is d(k).Now apply x(k) and d(k) to the

Fig 5.5 Simulink model of Adaptive FIR Filter for Pulse Doppler Radar

Adaptive FIR filter ,As soon as we apply both inputs to the adaptive filter, then the adaptive filter

starts comparing d(k) with x(k) and produces the output e(k).

Fig 5.6 Internal view of the Adaptive filter


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Fig 5.6 shows the Internal view of the Adaptive filter where the arrangement of delay

circuits, add sub structures, filters which helps to find out the errors efficiently. The adaptive

filter can be realized for Doppler filtering function and thus play huge role in target detection and

clutter cancellation. The filtering is achieved through a certain number of FIR filters. Within the

radar receiver response curve, each FIR filter is centered on definite Doppler frequency, that is

= 0 Hz to = , where fd is the Doppler frequency and is Pulse RepetitionFrequency. The filters tuning on the desired Doppler frequency is achieved utilizing coefficients,

called weights. The weight is complex in nature because the echo signal from target is also a

complex signal. The number of coefficient is needed is equal to number of filters. Each filter is

tuned to a particular Doppler frequency. As a result, the Receiver frequency response curve is

subdivided into a numbers of values.

Fig 5.7 Final output how error is reduced

Initially error is more in the analog signal before it is processing or before applying to the

Adaptive Filter. Thus when the signal is applied to the Adaptive FIR filter the error is reduced

which is shown in Fig 5.7.


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Chapter 6

6.1 CONCLUSION

This project presents the architecture for detection of moving target amidst various

intensity of clutter or noise using pipelined CORDIC unit in adaptive FIR filters bank. The use of

pipelined CORDIC computational architecture makes implementation of this kind of adaptive

filters easier. Numbers of micro-rotations have been adjusted so as to achieve better convergence

and speed of operation while minimizing angle approximation error. The pipelined multiplier

and CORDIC algorithm is used to achieve high throughput facilitating real time signal

processing. The inherent issue of CORDIC i.e. overflow is quite appropriately resolved using

proposed design. The property of good convergence of CORDIC is efficiently used in this

application.

6.2 FUTURE SCOPE

Adaptive filtering algorithms are used for acoustic and noise cancellation and noise

enhancer or clutter removal purpose. Selecting the adaptive filter that best meets our needs

requires careful consideration. Two main considerations frame the decision .When we begin to

develop an adaptive filter for our needs, most likely the primary concern is whether using an

adaptive filter is a cost-competitive approach to solving our filtering needs. Generally many

areas determine the suitability of adaptive filters (these areas are common to most filtering and

signal processing applications). Four such areas are

Filter consistency Filter performance

DSP requirements

In future the parametric adaptive matched filter (PAMF) for space-time adaptive

processing (STAP) is introduced via the matched filter (MF), multichannel linear prediction, and

the multichannel LDU decomposition.


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Dept Of E&C VDRIT Haliyal Page 23

REFRENCES

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[8] J. M. Jung and J. W. Chong, A Low Power Fir Filter design for Image

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