Assumption-free noise suppression for autonomous tractors tracking

L. Marata, J. M. Chuma, A. Yahya, I. Ngebani

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Autonomous tractors have gained high interest from researchers due to the need for increased productivity in Agriculture. Their application include ploughing, weeding and crop spraying. One problem of these tractors which has not been fully addressed is tracking using the noisy measurements from a sensor such as RADAR sensor. Most publications assume the error in the measurement to be Gaussian during the position estimation process. This assumption has seen a poor performance of the estimators in case the sensor noise is non-Gaussian. This research work introduces the use of Separable Monte Carlos based Mean for non-Gaussian noise suppression applied to Autonomous tractor tracking. The Monte Carlos based Means work independent from any assumptions. Gaussian and Cauchy Noise are used in experimentation for RADAR sensor measurement. Results suggest that the Separable Monte Carlos based mean (SMC-MEAN) outperforms the Kalman Filter and the Maximum A Posterior (MAP) in the Mean square error (MSE) sense hence can be of practical use in Autonomous tractor tracking.

Original languageEnglish
Title of host publication2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509025800
Publication statusPublished - Dec 5 2016
Event2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016 - Saint-Gilles Les Bains, Réunion
Duration: Oct 10 2016Oct 13 2016


Other2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016
CitySaint-Gilles Les Bains

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Instrumentation


Dive into the research topics of 'Assumption-free noise suppression for autonomous tractors tracking'. Together they form a unique fingerprint.

Cite this