Source bearing and steering-vector estimation using partially calibrated arrays

M. Li, Y. Lu

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to the estimation of unknown array parameters such as sensor gains and phases, as a way of performing array self-calibration. The cost function is an extension of the maximum likelihood (ML) criteria that were originally developed for DOA estimation with a perfectly calibrated array. A particle swarm optimization (PSO) algorithm is used to explore the high-dimensional problem space and find the global minimum of the cost function. The design of the PSO is a combination of the problem-independent kernel and some newly introduced problem-specific features such as search space mapping, particle velocity control, and particle position clipping. This architecture plus properly selected parameters make the PSO highly flexible and reusable, while being sufficiently specific and effective in the current application. Simulation results demonstrate that the proposed technique may produce more accurate estimates of the source bearings and unknown array parameters in a cheaper way as compared with other popular methods, with the root-mean-squared error (RMSE) approaching and asymptotically attaining the Cramer Rao bound (CRB) even in unfavorable conditions.
LanguageEnglish
Pages1361-1372
Number of pages12
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume45
Issue number4
DOIs
Publication statusPublished - Oct 2009

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Bearings (structural)
Particle swarm optimization (PSO)
Direction of arrival
Cost functions
Velocity control
Cramer-Rao bounds
Sensors
Sensor arrays
Maximum likelihood
Calibration

Keywords

  • particle swarm optimization
  • of-arrival estimation
  • maximun-likelihood
  • sensor arrays
  • asymptotic performance
  • uncalibrated arrays
  • noise fields
  • phase
  • doa
  • gain

Cite this

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title = "Source bearing and steering-vector estimation using partially calibrated arrays",
abstract = "The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to the estimation of unknown array parameters such as sensor gains and phases, as a way of performing array self-calibration. The cost function is an extension of the maximum likelihood (ML) criteria that were originally developed for DOA estimation with a perfectly calibrated array. A particle swarm optimization (PSO) algorithm is used to explore the high-dimensional problem space and find the global minimum of the cost function. The design of the PSO is a combination of the problem-independent kernel and some newly introduced problem-specific features such as search space mapping, particle velocity control, and particle position clipping. This architecture plus properly selected parameters make the PSO highly flexible and reusable, while being sufficiently specific and effective in the current application. Simulation results demonstrate that the proposed technique may produce more accurate estimates of the source bearings and unknown array parameters in a cheaper way as compared with other popular methods, with the root-mean-squared error (RMSE) approaching and asymptotically attaining the Cramer Rao bound (CRB) even in unfavorable conditions.",
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Source bearing and steering-vector estimation using partially calibrated arrays. / Li, M.; Lu, Y.

In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 45, No. 4, 10.2009, p. 1361-1372.

Research output: Contribution to journalArticle

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