fir滤波器设计方法总结

Introduction

A Finite Impulse Response (FIR) filter is a type of digital filter widely used in signal processing. It is a linear filter that has no feedback loop and has a finite impulse response. In this article, we will discuss the methods for FIR filter design.

Windowing Method

The windowing method is a commonly used method for FIR filter design. This method involves multiplying a finite length of a finite impulse response with a window function. The window function is chosen such that it has a small ripple in the passband and a steep cutoff in the stopband.

The steps involved in the windowing method are:

1. Choose the desired magnitude response
2. Calculate the required impulse response
3. Choose a suitable window function
4. Multiply the impulse response with the window function

Frequency Sampling Method

The frequency sampling method is another commonly used method for FIR filter design. This method involves sampling the desired frequency response of the filter and calculating the impulse response.

The steps involved in the frequency sampling method are:

1. Choose the desired magnitude response
2. Sample the magnitude response at regular intervals
3. Calculate the inverse discrete Fourier transform of the sampled response to obtain the impulse response

Least Squares Method

The least squares method is a commonly used method for designing FIR filters. This method involves minimizing the sum of squared errors between the desired filter response and the actual filter response.

The steps involved in the least squares method are:

1. Choose the desired magnitude response
2. Define a cost function using the sum of squared errors between the desired filter response and the actual filter response
3. Minimize the cost function using an iterative method such as the least squares algorithm

Parks-McClellan Method

The Parks-McClellan method is a highly efficient method for designing FIR filters. This method involves iteratively minimizing the weighted least squares error between the desired magnitude response and the actual frequency response.

The steps involved in the Parks-McClellan method are:

1. Choose the desired magnitude response
2. Define a set of constraints on the magnitude response such as the ripple and stopband attenuation
3. Estimate the initial frequency response
4. Iteratively update the frequency response by minimizing the weighted least squares error between the desired magnitude response and the actual frequency response

Conclusion

In conclusion, there are several methods available for designing FIR filters, including the windowing method, frequency sampling method, least squares method, and Parks-McClellan method. The choice of method depends on the requirements of the filter design and the efficiency of the method. By understanding the different methods available, one can select the appropriate method for designing a FIR filter.