FPGA implementation device and method based on FBLMS algorithm of block floating points

Abstract

The invention belongs to the technical field of real-time adaptive signal processing, particularly relates to an FPGA implementation device and method of an FBLMS algorithm based on a block floating point, and aims to solve the problem that conflicts exist among performance, speed and resources when an existing FPGA device implements the FBLMS algorithm. The method comprises the following steps that an input cache transformation module performs block cache recombination on a reference signal, converts the reference signal into block floating points and then performs FFT transformation; a filtering module carries out filtering in a frequency domain and carries out dynamic bit cutting; an error calculation and output caching module performs block caching on the target signal, subtracts the filtered output after the target signal is converted into a block floating point, and converts the result into a fixed-point system to obtain a final cancellation result; and a weight adjustment calculation module and a weight update storage module obtain the adjustment amount of the weight and update the weight block by block. Aiming at a recursive structure of the FBLMS algorithm, a block floating point data format and a dynamic bit cutting method are adopted, so the data is ensured to have a large dynamic range and high precision, conflicts among performance, speed and resources are solved,and the reusability and expansibility are also improved through modular design.

Description

technical field [0001] The invention belongs to the technical field of real-time self-adaptive signal processing, and in particular relates to an FPGA implementation device and method of a FBLMS algorithm based on block floating points. Background technique [0002] Theoretical research and hardware implementation of adaptive filtering algorithm has been one of the research hotspots in the field of signal processing. When the statistical characteristics of the input signal and noise are unknown or change, the adaptive filter can automatically adjust its own parameters under the premise of meeting certain criteria, and always achieve optimal filtering. Adaptive filters have been widely used in many fields such as signal detection, digital communication, radar, engineering geophysical prospecting, satellite navigation and industrial control. From the point of view of system design, the amount of computation, structure, and robustness are the three most important criteria for ...

AI Technical Summary

Problems solved by technology

Based on the above advantages, FPGA has been widely used in the hardware implementation of various signal processing algorithms. However, FPGA has shortcomings in processing high-precision floating-point operations, which will consume a lot of hardware resources, and even make complex algorithms difficult to implement.

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