Joint estimation of time delays and directions of arrival of multiple reflections of a known signal

Wax, M.; Leshem, Amir

doi:10.1109/78.640713

Cited by 134 publications

(48 citation statements)

References 12 publications

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“…Fig. 6(b) shows the value of the carrier-phase provided by the estimators in (16), (21), and (24). In order to facilitate the comparison, we assume and so that the effect of noise is perfectly averaged out; the rest of parameters are as in Section VI-A.…”

Section: ) Effect Of Varying the Reflection Delaymentioning

confidence: 99%

See 1 more Smart Citation

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

Seco-Granados

Fernandez–Rubio

Fernández‐Prades

2005

IEEE Trans. Signal Process.

104

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Abstract-This paper addresses the estimation of the code-phase (pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. The signal is received by an antenna array in a scenario with interference and multipath propagation. These two effects are generally the limiting error sources in most high-precision positioning applications. A new estimator of the code-and carrier-phases is derived by using a simplified signal model and the maximum likelihood (ML) principle. The simplified model consists essentially of gathering all signals, except for the direct one, in a component with unknown spatial correlation. The estimator exploits the knowledge of the direction-of-arrival of the direct signal and is much simpler than other estimators derived under more detailed signal models. Moreover, we present an iterative algorithm, that is adequate for a practical implementation and explores an interesting link between the ML estimator and a hybrid beamformer. The mean squared error and bias of the new estimator are computed for a number of scenarios and compared with those of other methods. The presented estimator and the hybrid beamforming outperform the existing techniques of comparable complexity and attains, in many situations, the Cramér-Rao lower bound of the problem at hand.

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Section: ) Effect Of Varying the Reflection Delaymentioning

confidence: 99%

“…Many of them assume that the spatial signatures are parameterized by the corresponding directions-of-arrival (DOA), as in [24] and [25]. While these methods may exploit the full space-time structure of the signals, they involve the optimization of multidimensional nonlinear cost functions.…”

Section: B Simplified Signal Modelmentioning

confidence: 99%

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

Seco-Granados

Fernandez–Rubio

Fernández‐Prades

2005

IEEE Trans. Signal Process.

104

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“…Most joint estimators are based on ML techniques and signal subspace approaches, such as MUSIC or ESPRIT, and are developed for joint AOA/TOA estimation of a single users multipath signal components at a receiver. The ML approach in (Wax & Leshem, 1997) for joint AOA/TOA estimation in static channels presents an iterative scheme that transforms a multidimensional ML criterion into two sets of one dimensional problems. Both a deterministic and a stochastic ML algorithm were developed in (Raleigh & Boros, 1998) for joint AOA/TOA estimation in time-varying channels.…”

Section: Joint Parameter Estimationmentioning

confidence: 99%

Wireless Positioning: Fundamentals, Systems and State of the Art Signal Processing Techniques

Zhang¹,

Tao²,

Yang³

2011

Cellular Networks - Positioning, Performance Analysis, Reliability

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“…This is, of course, a classical approach and has been considered, e.g., in [6]- [8] as well. New here is the observation that by stacking the result into a Hankel matrix, the problem is reduced to one that can be solved using 2-D ESPRIT techniques [11], [12], which were developed for joint azimuth-elevation estimation.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches often require computationally unattractive ML searches and/or need accurate initial points and do not always work properly for rays with nearly equal directions or delays. The method proposed by Ogawa et al [9] is a 2-D (windowed) MUSIC algorithm, and the method by Wax et al [8] performs a successive ML optimization for an increasing number of rays, using lower order results as initial points. The method by Swindlehurst et al [6], [7] that is applicable to our scenario consists of an iterative ML scheme (IQML) that requires initialization.…”

Section: Introductionmentioning

confidence: 99%

Joint angle and delay estimation using shift-invariance techniques

Veen

Vanderveen

Paulraj³

1998

IEEE Trans. Signal Process.

261

117

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Abstract-In a multipath communication scenario, it is often relevant to estimate the directions and relative delays of each multipath ray. We derive a closed-form subspace-based method for the simultaneous estimation of these parameters from an estimated channel impulse response, using knowledge of the transmitted pulse shape function. The algorithm uses a two-dimensional (2-D) ESPRIT-like shift-invariance technique to separate and estimate the phase shifts due to delay and direction of incidence with automatic pairing of the two parameter sets. Improved resolution is obtained by enlarging the data matrix with shifted and conjugated copies of itself.

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Joint estimation of time delays and directions of arrival of multiple reflections of a known signal

Cited by 134 publications

References 12 publications

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

Wireless Positioning: Fundamentals, Systems and State of the Art Signal Processing Techniques

Joint angle and delay estimation using shift-invariance techniques

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