Optimised complexity reduction for maximum likelihood position estimation in spread spectrum navigation receivers

Groh, Ingmar; Sand, Stephan

doi:10.1049/iet-rsn.2011.0041

Cited by 2 publications

(5 citation statements)

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“…We refer to the recursion in (29) as the MLMS algorithm. It is important to note that since the LMS algorithm is a local optimization approach, as opposed to the recursion in (23) which is a global optimization technique, the convergence of the algorithm also depends on the choice of the initial values in addition to the step size.…”

Section: A Matrix Least-mean-square Algorithmmentioning

confidence: 99%

“…Since G is a known matrix, its inverse is also known beforehand and hence the computations in (42) and (43) do not increase the computational complexity of the system. When a feedback error mechanism is applied in the structure of the recursive algorithms in the last sections, d k is replaced by e d k in the recursions in (29), (A-1), (36), and (41).…”

Section: Los Signal Estimationmentioning

confidence: 99%

“…Another class of multipath mitigation techniques includes advanced methods such as Multipath Estimating Delay Locked Loop (MEDLL) , Multipath Mitigation Technique (MMT) , Vision Correlator (VC) which is an implementation of MMT, the Fast Iterative Maximum Likelihood Algorithm (FIMLA) , Sequential Maximum Likelihood , Reduced Search Space Multipath Likelihood (RSSML) algorithm , complexity‐reduced ML‐based algorithms , and deconvolution approaches . This class of techniques is based on maximum likelihood (ML) estimation.…”

Section: Introductionmentioning

confidence: 99%

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Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

2016

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Multipath propagation is one of the dominant sources in ranging applications. The problem of GNSS multipath mitigation is investigated herein through the development of adaptive multipath channel compensation filters that impose a smaller computational load on the receiver compared to most of the advanced delay estimation techniques. An optimum estimation block is designed to produce the control error signal in the feedback loop used to update the filter coefficients adaptively. Using the proposed method, multipath distortion on the received signal is compensated for by passing signals through a filter whose coefficients are adaptively adjusted employing feedback signals. A set of simulation and real data processing results are presented to compare the performance of the proposed adaptive techniques with some of the conventional DLLs under realistic urban channel conditions. The experimental results are based on live vehicular GPS data collected in dense urban environments; the results demonstrate a reduction of up to 30% in RMSE values of the computed position errors. Copyright © 2016 Institute of Navigation.

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Section: A Matrix Least-mean-square Algorithmmentioning

confidence: 99%

Section: Los Signal Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

2016

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“…Modifications of the CRMM include extension of data size reduction to time-variant signals (Groh et al, 2011), optimized correlator computation (Groh and Sand, 2012), and replacement of the Newton-type optimization with expectation maximization or space alternating general expectation maximization algorithms . In this paper, the focus is on the data size reduction of CRMM and its realizability in hardware in the subsequent sections.…”

Section: Crmmmentioning

confidence: 99%

“…Several publications on CRMM discuss various implementation aspects of the algorithm, e.g. Groh and Sand (2008); Groh et al (2011); Groh and Sand (2012). However, none provides a more detailed analysis with respect to real-time or hardware implementation issues.…”

Section: Introductionmentioning

confidence: 99%

A design study on complexity reduced multipath mitigation

et al. 2012

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Abstract. Global navigation satellite systems, e.g. the current GPS and the future European Galileo system, are frequently used in car navigation systems or smart phones to determine the position of a user. The calculation of the mobile position is based on the signal propagation times between the satellites and the mobile terminal. Further, the satellites need to be line-of-sight to the receiver for exact position calculation. However, in an urban area, the direct path may be blocked and the resulting multipath propagation causes errors in the order of tens of meters for each measurement.In this paper an advanced algorithm for multipath mitigation known as CRMM is presented. CRMM features reduced algorithmic complexity and superior performance in comparison with other state of the art multipath mitigation algorithms. Simulation results demonstrate the significant improvements in position calculation in environments with severe multipath propagation. Nevertheless, in relation to traditional algorithms an increased effort is required for real-time signal processing due to the large amount of data, which has to be processed in parallel. Based on CRMM, we performed a comprehensive design study including a design space exploration for the tracking unit hardware part, and prototype implementation for hardware complexity estimation.

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Optimised complexity reduction for maximum likelihood position estimation in spread spectrum navigation receivers

Cited by 2 publications

References 20 publications

Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

A design study on complexity reduced multipath mitigation

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