Optimised Complexity Reduction for Maximum Likelihood Position Estimation in Spread Spectrum Navigation Receivers
| Autor: | Stephan Sand, Ingmar Groh |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Computational complexity theory
Mean squared error Computer science 02 engineering and technology 01 natural sciences position estimation Statistics 0202 electrical engineering electronic engineering information engineering computer simulations maximum likelihood position estimation 0101 mathematics Electrical and Electronic Engineering spread spectrum navigation receivers 010102 general mathematics multipath propagation Estimator 020206 networking & telecommunications signal subspace energy errors ML RMSE Spread spectrum Noise SSEE optimised complexity reduction root mean square error Multipath propagation low-complexity high-resolution channel delay estimation Signal subspace Communication channel |
| Popis: | In urban environments, spread spectrum radio navigation is subject to multipath propagation causing multipath errors of tens of metres. Low-complexity high-resolution channel delay estimation is crucial for position estimation in the receivers to mitigate the multipath errors. The main drawback of maximum likelihood (ML) channel delay estimation is the high computational complexity. Thus, recent publications present methods to decrease its computational complexity. These contributions assess the complexity reduction by means of signal subspace energy errors (SSEEs). This assessment of the complexity reduction is incomplete, as the relevant metric, that is, the relationship between complexity reduction and degrading position accuracy in terms of increasing root mean square error (RMSE) lacks. The authors main contribution is the derivation and analysis of this relation. The larger RMSE for complexity-reduced ML estimation algorithms compared to the implementation without complexity reduction consists of an increased noise variance and a non-zero bias. Thus, this contribution associates the SSEE and the RMSE for complexity-reduced ML estimators. Computer simulations confirm the revealed analytical relationships. Furthermore, the authors approach yields a novel method to minimise the increased noise variance of complexity-reduced ML estimation. Thus, the authors algorithms yield a lower RMSE. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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