The Experts below are selected from a list of 6033 Experts worldwide ranked by ideXlab platform

Alexander M. Haimovich - One of the best experts on this subject based on the ideXlab platform.

  • target localization accuracy gain in mimo radar based systems
    IEEE Transactions on Information Theory, 2010
    Co-Authors: Alexander M. Haimovich
    Abstract:

    This paper presents an analysis of target localization accuracy, attainable by the use of multiple-input multiple-output (MIMO) radar systems, configured with multiple transmit and receive sensors, widely distributed over an area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. Coherent processing requires a common phase reference for all transmit and receive sensors. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. We further prove that optimization over the sensors' positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best Linear Unbiased Estimator (BLUE) is derived for the MIMO target localization problem. The BLUE's utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area.

  • target localisation techniques and tools for multiple input multiple output radar
    Iet Radar Sonar and Navigation, 2009
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    This study presents a comparative study of coherent and non-coherent target localisation techniques for multiple-input multiple-output (MIMO) radar systems with widely distributed elements. Performance is evaluated based on closed-form solutions developed for the best Linear Unbiased Estimator (BLUE) for each of the localisation methods. These Estimators afford insights into the relation between radar locations, target location and localisation accuracy. In particular, the means squared error of the BLUE is factored into a term dependent on signal and processing characteristics and a term dependent on sensor locations. The latter is referred to as geometric dilution of precision (GDOP). The best achievable accuracy for the coherent case is obtained, and a comparative study with the non-coherent case is presented. MIMO radar systems with coherent processing are shown to benefit from a gain because of coherent processing among sensors. This gain is referred to as coherent localisation gain, and it is proportional to the ratio of the signal carrier frequency to the effective bandwidth (a large ratio for typical signals). The footprint of multiple transmit/receive sensors results in a gain, referred to as MIMO gain, for both processing techniques. The MIMO gain is proportional to the product of the number of transmitting and receiving sensors. Analysis of the MIMO gain through the use of GDOP contour maps demonstrate the achievable accuracy at various target locations for a given layout of sensors.

  • target localization accuracy gain in mimo radar based systems
    arXiv: Information Theory, 2008
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    This paper presents an analysis of target localization accuracy, attainable by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured with multiple transmit and receive sensors, widely distributed over a given area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and non-coherent processing. Coherent processing requires a common phase reference for all transmit and receive sensors. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the non-coherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. We further prove that optimization over the sensors' positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best Linear Unbiased Estimator (BLUE) is derived for the MIMO target localization problem. The BLUE's utility is in providing a closed form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area.

  • Target localization techniques and tools for MIMO radar
    2008 IEEE Radar Conference, 2008
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed or colocated transmitters and receivers. The widely spaced antenna structure suggests unique features that set MIMO radar apart from other radar systems, making it strongly related to MIMO communications. The widely separated transmit/receive antennas capture different aspects of the target cross section that can be exploited to obtain diversity gain for detection and estimation of the targetpsilas various parameters, such as angle of arrival, and Doppler. The use of coherent processing can provide localization accuracy gains well beyond that supported by the radarpsilas waveform. This paper provides a review of some recent work on computing the Cramer-Rao lower bound (CRLB) on the achievable localization accuracy. The geometric dilution of precision (GDOP) is used as a tool for assessing and illustrating the localization accuracy of the best Linear Unbiased Estimator (BLUE).

Jaakko Makinen - One of the best experts on this subject based on the ideXlab platform.

  • a bound for the euclidean norm of the difference between the best Linear Unbiased Estimator and a Linear Unbiased Estimator
    Journal of Geodesy, 2002
    Co-Authors: Jaakko Makinen
    Abstract:

    A bound is established for the Euclidean norm of the difference between the best Linear Unbiased Estimator and any Linear Unbiased Estimator in the general Linear model. The bound involves the spectral norm of the difference between the dispersion matrices of the two Estimators, and the residual sum of squares, all evaluated at the assumed model, but is independent of the provenance of the observation vector at hand. The bound, a straightforward consequence of first principles in Gauss–Markov theory, generalizes previous results on the difference between the best Linear Unbiased Estimator and the ordinary least-squares Estimator. In a numerical example from repeated precise levelling, the bound is used to analyse the sensitivity of estimates of vertical motion to the choice of Estimator.

Rick S. Blum - One of the best experts on this subject based on the ideXlab platform.

  • target localisation techniques and tools for multiple input multiple output radar
    Iet Radar Sonar and Navigation, 2009
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    This study presents a comparative study of coherent and non-coherent target localisation techniques for multiple-input multiple-output (MIMO) radar systems with widely distributed elements. Performance is evaluated based on closed-form solutions developed for the best Linear Unbiased Estimator (BLUE) for each of the localisation methods. These Estimators afford insights into the relation between radar locations, target location and localisation accuracy. In particular, the means squared error of the BLUE is factored into a term dependent on signal and processing characteristics and a term dependent on sensor locations. The latter is referred to as geometric dilution of precision (GDOP). The best achievable accuracy for the coherent case is obtained, and a comparative study with the non-coherent case is presented. MIMO radar systems with coherent processing are shown to benefit from a gain because of coherent processing among sensors. This gain is referred to as coherent localisation gain, and it is proportional to the ratio of the signal carrier frequency to the effective bandwidth (a large ratio for typical signals). The footprint of multiple transmit/receive sensors results in a gain, referred to as MIMO gain, for both processing techniques. The MIMO gain is proportional to the product of the number of transmitting and receiving sensors. Analysis of the MIMO gain through the use of GDOP contour maps demonstrate the achievable accuracy at various target locations for a given layout of sensors.

  • target localization accuracy gain in mimo radar based systems
    arXiv: Information Theory, 2008
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    This paper presents an analysis of target localization accuracy, attainable by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured with multiple transmit and receive sensors, widely distributed over a given area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and non-coherent processing. Coherent processing requires a common phase reference for all transmit and receive sensors. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the non-coherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. We further prove that optimization over the sensors' positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best Linear Unbiased Estimator (BLUE) is derived for the MIMO target localization problem. The BLUE's utility is in providing a closed form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area.

  • Target localization techniques and tools for MIMO radar
    2008 IEEE Radar Conference, 2008
    Co-Authors: Hana Godrich, Alexander M. Haimovich, Rick S. Blum
    Abstract:

    MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed or colocated transmitters and receivers. The widely spaced antenna structure suggests unique features that set MIMO radar apart from other radar systems, making it strongly related to MIMO communications. The widely separated transmit/receive antennas capture different aspects of the target cross section that can be exploited to obtain diversity gain for detection and estimation of the targetpsilas various parameters, such as angle of arrival, and Doppler. The use of coherent processing can provide localization accuracy gains well beyond that supported by the radarpsilas waveform. This paper provides a review of some recent work on computing the Cramer-Rao lower bound (CRLB) on the achievable localization accuracy. The geometric dilution of precision (GDOP) is used as a tool for assessing and illustrating the localization accuracy of the best Linear Unbiased Estimator (BLUE).

H C So - One of the best experts on this subject based on the ideXlab platform.

  • Linear least squares approach for accurate received signal strength based source localization
    IEEE Transactions on Signal Processing, 2011
    Co-Authors: H C So
    Abstract:

    A conventional approach for passive source localization is to utilize signal strength measurements of the emitted source received at an array of spatially separated sensors. The received signal strength (RSS) information can be converted to distance estimates for constructing a set of circular equations, from which the target position is determined. Nevertheless, a major challenge in this approach lies in the shadow fading effect which corresponds to multiplicative measurement errors. By utilizing the mean and variance of the squared distance estimates, we devise two Linear least squares (LLS) Estimators for RSS-based positioning in this paper. The first one is a best Linear Unbiased Estimator while the second is its improved version by exploiting the known relation between the parameter estimates. The variances of the position estimates are derived and confirmed by computer simulations. In particular, it is proved that the performance of the improved LLS Estimator achieves Cramer-Rao lower bound at sufficiently small noise conditions.

Lanxin Lin - One of the best experts on this subject based on the ideXlab platform.

  • best Linear Unbiased Estimator algorithm for received signal strength based localization
    European Signal Processing Conference, 2011
    Co-Authors: Lanxin Lin
    Abstract:

    Locating an unknown-position source using measurements from an array of spatially separated sensors with low complexity is quite necessary in many applications. In this paper, a Linear least squares (LLS) method, which is a best Linear Unbiased Estimator, is proposed to estimate the unknown-position source location based on the received signal strength (RSS) measurements. It is proved that the performance of our proposed method is identical to that of an existing LLS technique but the former is more computationally efficient. A relaxation method is also introduced to extend the LLS methods for RSS-based positioning with unknown path-loss factor. Furthermore, numerical examples are included to evaluate the performance of proposed algorithm by comparing with the existing LLS approach and their theoretical position variances as well as Cramer-Rao lower bound.