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DOI: https://doi.org/10.15332/iteckne.v11i2.726

Adaptive filtering implemented over TMS320c6713 DSP platform for system identification

Filtrado adaptativo implementado sobre plataforma DSP TMS320c6713 para identificación de sistemas

Fabián Rolando Jiménez-López, Camilo Ernesto Pardo-Beainy, Edgar Andrés Gutiérrez-Cáceres

Abstract - 328 | PDF (Español (España)) - 106


Abstract(es_ES)

This paper presents the experimental development of software and hardware configuration to implement two adaptive algorithms: LMS (Least Mean Square) and RLS (Recursive Least Square), using TMS320C6713 DSP platform of Texas Instruments, for unknown systems identification. Methodology for implementation and validation analysis for the adaptive algorithms is described in detail for real-time systems identification applications, and the experimental results were evaluated in terms of performance criterions in time domain, frequency domain, computational complexity, and accuracy.

Keywords(es_ES)

Adaptive Filtering, Digital Signal Processor, LMS Algorithm, RLS Algorithm, Real Time Processing, System Identification

Abstract(en_US)

Este documento  describe  el  desarrollo  experimental  de  la  configuración  de  hardware  y software para implementar dos algoritmos adaptativos: el de Mínimos Cuadrados Promediados LMS (Least Mean Square) y  Mínimos  Cuadrados  Recursivos RLS (Recursive Least Square), usando la  plataforma  DSP  TMS320C713  de Texas   Instruments   para   identificación   de   sistemas  desconocidos. La metodología para la implementación y análisis de operación de los algoritmos adaptativos se presentan en detalle para aplicaciones de identificación de sistemas en tiempo real, y los resultados experimentales fueron evaluados en términos de criterios de desempeño en el dominio temporal, frecuencial, complejidad computacional y precisión.

Keywords(en_US)

Algoritmo LMS; Algoritmo RLS; Filtrado Adaptativo; Identificación de Sistemas; Procesador Digital de Señales; Procesamiento en Tiempo Real

References


S. Haykin, “Adaptive Filter Theory”, 5th ed., Pearson Education: Prentice Hall, 2013.

R. Chassaig, “Digital Signal Processing and Applications with the C6713 and C6416 DSK”, 2nd ed. United States of America, New Jersey: John Wiley & Sons, 2008.

N. Kehtarnavaz, N. Kim, and I. Panahi, “Digital signal processing system design: using LabVIEW and TMS320C6000,” New York: the 11th IEEE Digital Signal Processing Workshop and The 3rd IEEE Signal Processing Education Workshop, pp. 10-14, 2004.

W. S. Gan, “Teaching and learning the hows and whys of real-time digital signal processing,” IEEE Trans. on Education, vol. 45, no. 4, pp. 336-343, 2002.

A. Spanias, V. Berisha, H. Kwon, C. Huang, A. Natarajan and R. Ferzili, “Using the Java-DSP real-time hardware interface in undergraduate classes,” San Diego: presented at the 36th ASEE/IEEE Annual Frontiers in Education Conference, pp. 12-17, 2006.

M. Galanis, A. Papazacharias, and E. Zigouris, “A DSP course for real-time systems design and implementation based on the TMS320C6211 DSK,” presented at the IEEE International Conf. on Digital Signal Processing, vol. 2, pp. 853-856, 2002.

D. Orofino, “Rapid prototyping of a surveillance video compression system,” Matlab Digest, The MathWorks, Inc. Massachusetts, vol. 11, n. 5, pp. 1-5, 2003.

G. Budura, “Nonlinear systems identification using the volterra model,” Romania: Scientific Bulletin of the Politehnica University of Timinoara, 2005.

L. Gulfo and J. Valencia, “Identificación y modelamiento de sistemas de audio API mediante filtros adaptativos”. Medellín, Colombia: Ingeniería de Sonido, Universidad de San Buenaventura, 2012.

D. Millan, “Estudio y comparativa de diferentes algoritmos adaptativos para la identificación de sistemas,”. Barcelona, España: Ingeniería Técnica de Telecomunicaciones, Universidad Politécnica de Catalunya, 2012.

P. S. Diniz, “Adaptive filtering: algorithms and practical implementation,” 3rd ed. New York, NY, USA: Springer, 2008.

H. Quanzhen, G. Zhiyuan, G. Shouwei, S. Yong and Z. Xiaojin, “Comparison of LMS and RLS algorithm for active vibration control of smart structures,” presented at Third International Conference on Measuring Technology and Mechatronics Automation ICMTMA´11, vol.1, no. 1, pp. 745–748, 2011.

S. Agraval and V. Gopal, “Performance analysis of adaptive filtering algorithms for system identification,” International Journal of Electronics and Communication Engineering, vol. 5, no. 2, pp. 207-217, 2012.

J. Velázquez, J. Sánchez, and H. Pérez, “Adaptive filters with codified error LMS algorithm,” International Journal Electromagnetic Waves and Electronic Systems, vol. 1, pp. 23–28, 2006.

E. Soria, J. Calpe, J. Chambers, M. Martínez, G. Camps and J. D. Guerrero, “A novel approach to introducing adaptive filters based on the LMS algorithm and its variants,” IEEE Trans. On Education, vol. 47, pp. 127- 133, 2008.

L. N. Reyes, “Análisis de Filtros adaptativos de la familia SM aplicados para el diseño de un cancelador de eco acústico,” Sangolquí, Ecuador: Ingeniería en Electrónica, Escuela Politécnica del Ejército, 2013.

E. H. Krishna, M. Raghuram, K. V. Madhav and K. A. Reddy, “Acoustic echo cancellation using a computationally efficient transform domain LMS adaptive filter,” presented at 10th International Conf. on Information sciences signal processing and their applications (ISSPA), pp. 409-412, 2010.

H. Zhao, S. Hu, L. Li and X. Wan, “NLMS adaptive FIR filter design method,” presented at IEEE Region 10 Conference TENCON, pp. 1- 5, 2013.

C. Paleologu, J. Benesty, S. L. Grant and C. Osterwise, “Variable step-size NLMS algorithms for echo cancellation,” presented at Conf. Record of the forty-third Asilomar Conference on Signals, Systems and Computers, pp. 633-637, 2009.

X. Guan; X. Chen and G. Wu, “QX-LMS ADAPTIVE FIR filters for system identification,” presented at 2nd International Congress on Image and Signal Processing, CISP ‘09, vol. 1, no. 1, pp.1–5, 2009.

B. B. Farhang, “Adaptive filters theory and applications”, 1st ed. New York: Wiley & Sons,. 1999.

E. Turki; T. A. Tutunji and M. Molhim, “Gyroscope system identification using an impulse response RLS algorithm,” presented at IEEE Conf. on Industrial Electronics, IECON 2006. Paris, Francia, Nov. 2006.

S. Ciochina; C. Paleologu; J. Benesty and A. Enescu, “On the influence of the forgetting factor of the RLS adaptive filter in system identification,” presented at Signals, International Symposium on Circuits and Systems, ISSCS 2009, vol. 1, no. 1, pp. 1–4, 2009.

W. S. Gan, Y. Chong, W. Gong and W. Tan, “Rapid prototyping for teaching real-time digital signal processing,” IEEE Trans. on Education, vol. 43, no. 1, pp. 19-24, 2000.

S. Gannot and V. Avrin, “A Simulink® and Texas instruments C6713® based digital signal processing laboratory.” Florence, Italy: presented at 14th European Signal Processing Conference, September 4-8, EUSIPCO, 2006.

T. Instruments, “TMS320C6713, TMS320C6713B. Floating-point digital signal processors,” Technical Reference Datasheet, Texas Instruments Incorporated, 2005.

S. Digital, “TMS320C6713 DSK, Technical Reference,” Spectrum Digital Inc., 2003.

T. Instruments, “TLV320AIC23B stereo audio CODEC, 8 to 96KHz, with integrated headphone amplifier, user manual,” Texas Instruments Incorporated, 2004.

J. Álvarez, M. Chuez, y P. Vargas, “Implementaciones en Matlab de los algoritmos adaptativos para los sistemas de antenas inteligentes,” Revista Tecnológica ESPOL, vol. 1, no.1, pp. 1-8, 2011.

D. Kaoru, R. Vitória, V. L. Arlindo and T Abrão, “Implementação eficiente de filtros adaptativos utilizando a plataforma TMS320C6713,” presentado em ele semina: ciências exatas e tecnológicas, londrina, vol. 32, no. 1, pp. 115–131, 2011.

S. K. Hasnain, “Digital signal processing, theory & worked examples”, 3th ed. Karachi, Pakistan. Royal Book Company, 2009.

Mathworks Inc., “Simulink, simulation & model based design”, Mathworks Inc., 2012.

Code composer studio IDE getting started user´s guide v3.3. T. Instruments, USA, 2006.

Mathworks Inc.,“Compute filtered output, filter error, and filter weights for given input and desired signal using RLS adaptive filter algorithm – Simulink,” Mathworks Inc., 2014. Available FTP: http://www.mathworks.com/help/dsp/ref/rlsfilter.html.

Mathworks inc., “Compute output, error, and weights using LMS adaptive algorithm – Simulink”, Mathworks Inc., 2014. Available FTP: http://www.mathworks. com/help/dsp/ref/lmsfilter.html.

P. K. Pathak and K. K. Sarma, “Time Varying System Identification using Adaptive Filter,” IRNet Trans. on Electrical and Electronics Engineering, ITEEE´12, vol. 1, no. 2, pp. 49-52, 2012.

T. Kara and I. Eker, “Experimental nonlinear identification of a two mass system,” presented at IEEE Conference on Control Applications, CCA 2003, vol.1, pp. 66-71, 2003.

P. Dobra, R. Duma, D. Petreus and M. Trusca, “Adaptive system identification and control using DSP for automotive power generation,” Ajaccio, France: presented at 16th Mediterranean Conference on Control and Automation Congress Centre, 2008.

Z. Li; C. Li, “LMS and RLS algorithms comparative study in system identification,” presented at International Conference on Multimedia Technology, ICMT 2011, vol. 1, no. 1, 2011, pp. 5428–5430.

M. Shafiq, S. Ejaz and N. Ahmed, “Hardware implementation of adaptive noise cancellation over DSP Kit TMS320C6713,” International Journal of Signal Processing (SPIJ), vol. 7, no. 1, pp. 75-86. 2013.

C. A., Duran, J. A., Reyes and J. C. Sanchez, “Implementation and analysis of the NLMS algorithm on TMS320C6713 DSP,” presented at 52nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS ‘09, Cancun. MEX. 2009.

V. I. Djigan, “Adaptive filtering algorithms with quatratized cost function for Linearly Constrained arrays,” presented at IX International Conference on Antenna Theory and Techniques, ICATT´13, Odessa. UCR. 2013.

R. G. Soumya, N. Naveen and M. J. Lal, “Application of adaptive filter using adaptive line enhancer techniques,” presented at Third International Conference on Advances in Computing and Communications, ICACC´13, vol. 1, no.1, pp. 165–168, 2013.

Y. Xia, L. Jianchang and L. Hongru, “Performance analysis of adaptive filters for time-varying systems,” presented at 32nd Chinese Control Conference, CCC´13, Xi`an. RPC. 2013.

Y. I. Huang, Y. W. Wang, F. J. Meng and G. L.Wang, “A spatial spectrum estimation algorithm based on adaptive beamforming nulling,” presented at Fourth International Conference on Intelligent Control and Information Processing, ICICIP´13, Beijing, RPC, 2013.

J. P. Vijay and N. K. Sharma, “Performance analysis of RLS over LMS algorithm for MSE in adaptive filters,” International Journal of Technology Enhancements and Emerging Engineering Research, vol. 2, no. 4, pp. 40–44, 2014.

A. A. Hameed, “Real-time noise cancellation using adaptive algorithms,” M.Sc. thesis, Computer Engineering. Eastern Mediterranean University, Chipre. 2012.


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