Implementation of Adaptive Filters on TMS320C6713 using LabVIEW - A Case Study

Adaptive filters are playing a vital role in signal processing and communication filed of engineering for the purpose of filtering the unwanted signal, signal denoising, signal enhancement, etc. The main characteristic of the adaptive filter is the adjustment of filter coefficients dynamically with respect to the input signal which helps a lot in signal processing applications. This study main focus on implementing such adaptive filters on digital signal processors. The adaptive filtering algorithms such as Least Mean Square (LMS) algorithm and Normalized LMS (NLMS) algorithms are implemented with TMS320C6713 floating-point DSP processor using LabVIEW environment in real time. To test the functionality of the algorithms, the sinusoid signal is added with noisy and applied as an input the filter and the resultant denoising output is obtained with both the algorithms. We implement it with TMS320C6713 floating-point Digital Signal Processor using LabVIEW environment in real time. Our objective is to reduce or filter the noise using these algorithms and obtain the performance metrics like peak output, Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) as a part of simulation results. The PSNR produced by the NLMS algorithm is obtained as 18.414 is very high as compared with 3.28416 produced by the LMS algorithm. Interfacing the TMS320C6713 DSP board with the LabVIEW application is done using the Code Composer Studio software tool. This study focuses the principle of adaptive filters by implementing the Least Mean Square (LMS) algorithm and Normalized LMS algorithms and can be further extended with Kalman filters too. er .

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