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A novel adaptive LMS-based algorithm considering relative velocity of source

Shirvani Moghaddam, S ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. Publisher: 2010
  3. Abstract:
  4. In this paper a new least mean square (LMS) based adaptive weighting algorithm is proposed. It is appropriate for antenna array systems with moving targets and mobile applications. The essential goal of this algorithm is to reduce the complexity of weighting process and to decrease the time needed for adjusting the antenna radiation pattern. The main lobe of antenna will be adjusted in the direction of desired signal (main signal) and nulls pointed in the direction of undesired signals (interference signals). By predicting the relative velocity of source, the next location of the source will be estimated and the array weights will be determined using LMS algorithm before arriving to the new point. For the next time associated to the new sampling point, evaluated weights will be used. Furthermore, by updating these weights between two consecutive times the effects of error propagation will be eliminated. Therefore, in addition to reduction in computational complexity at the time of weight allocation, relatively accurate weight allocation can be obtained. Simulation results show that the angular error related to proposed LMS-based algorithm is similar to the conventional LMS algorithm at different signal to noise ratios (SNRs). On the other hand, online computational complexity of new algorithm is slightly less than previous one
  5. Keywords:
  6. Adaptive weighting ; Angular errors ; Antenna array system ; Antenna radiation patterns ; Desired signal ; Error propagation ; Interference signal ; Least mean squares ; LMS algorithms ; Mobile applications ; Moving targets ; Relative velocity ; Sampling points ; Simulation result ; Weight allocation ; Weighting process ; Antenna arrays ; Communication systems ; Computational complexity ; Digital signal processing ; Directional patterns (antenna) ; Mobile antennas ; Signal processing ; Signal to noise ratio ; Adaptive algorithms
  7. Source: 2010 7th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2010, 21 July 2010 through 23 July 2010, Newcastle upon Tyne ; 2010 , Pages 10-14 ; 9781861353696 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5580470