ESPRIT-based 2-D direction finding with a sparse uniform array of electromagnetic vector sensors

Zoltowski, M.D.; Wong, Kainam Thomas

doi:10.1109/78.852000

Cited by 209 publications

(150 citation statements)

References 22 publications

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“…A multiple invariant rectangular array is shown in Figure 7. Several methods have been proposed for direction finding and/or other parameter estimation using MI sensor arrays, e.g., [24,27,28,34]. An important assumption of these methods is that the incoming signals are narrowband, so that the propagation delay of the signals from one subarray to another can be approximated by phase shift.…”

Section: B3 the Dp-tals Algorithm With Multiple Invariance Sensor Armentioning

confidence: 99%

Signal Detection and Jammer Localization in Multipath Channels for Frequency Hopping Communications

Liu¹

2005

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Public Reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Frequency hopping is the prevailing spread spectrum method in military communications, largely due to its low probability of detection and interception. In this project we developed a novel signal processing scheme for code-blind reception of multiple frequency hopped transmission over multipath channels. This techniques is based on the principle of dynamic programming and/or expectation-maximization, coupled with multidimensional harmonic and low rank analysis. It is able to jointly estimate hop timing, hop frequency, and directions of multiple frequency hopped signals in the presence of frequency collisions, without the knowledge of signal hop patterns. The method can also be used to obtain locate information and operation characteristic of active frequency hopped jammers. The associated identifiability of 2-D frequency estimation is also studied.

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Section: B3 the Dp-tals Algorithm With Multiple Invariance Sensor Armentioning

confidence: 99%

Signal Detection and Jammer Localization in Multipath Channels for Frequency Hopping Communications

Liu¹

2005

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“…The th polarization vector with orientation uncertainties is (16) where the rotation matrix accounts for the overall orientation errors at the horizontal components of the three-component sensors (17) In presence of sensor-orientation uncertainties, the polarization matrix of the model (10) for the overall wavefields becomes (18) 3) Parametric Model With Positional and Rotational Calibration Errors: Summarizing, in the case of multiple-wavefield and multiple-sensor with calibration uncertainties of both position and orientation, the output signal in (11) is modified as (19) where describes the array manifold for wavefields in the presence of sensor-calibration errors, with and defined as in (15) and (18), respectively. The unknown shape and sensor-calibration error for the th wavefield can be summed up in the set of parameters (20) where accounts for the deterministic shape parameters of the th wavefield defined in (12), and , and are random calibration errors.…”

Section: ) Vector-sensors With Rotational Calibration Errorsmentioning

confidence: 99%

“…A maximum-likelihood estimation (MLE) of polarization parameters has been discussed in [10] and [11], whereas in [12] the authors presented a method for incorporating signal polarization in Capon's minimum variance estimator. Subspace-based methods, which are known to be computational efficient but still consistent, have been also proposed: a MUSIC-based approach is discussed in [13], and ESPRIT-based direction/polarization estimations are found in [14] for polarized signals with two-component sensors, in [15] and [16] using a sparse array of electromagnetic vector-sensors, and in [17] using a single electromagnetic vector sensor.…”

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confidence: 99%

Seismic Velocity and Polarization Estimation for Wavefield Separation

Donno

Nehorai

Spagnolini

2008

IEEE Trans. Signal Process.

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Abstract-We address the problem of estimating the shape parameters of seismic wavefields using linear arrays of three-component (3C) vector sensors with uncertain acquisition geometry. The goal is to separate the different seismic waves, which is of practical need for oil exploration and geophysics. We present a parametric model for multiple wideband polarized signals received by an array of three-component sensors with positional and rotational calibration errors, and derive the Cramér-Rao lower bounds on the performance of the model parameters for both the exact physical model and the model with uncertain acquisition geometry. We propose a method for jointly estimating the velocity and polarization parameters based on the shift-invariance properties of multiple wavefields impinging on the linear array. We then remove the interfering surface waves by using a beamforming filter designed to exploit the velocity and polarization diversity of the different seismic waves, after clustering of the shape-parameter estimates. Examples using simulated and experimental data illustrate the applicability of the proposed methodology.Index Terms-Array signal processing, Cramér-Rao bound, velocity/polarization estimation, vector-sensor calibration errors, vector-sensor broadband beamforming.

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“…A vector-sensor comprises two or more collocated different types of scalar-sensors and is generally advantageous over a scalar-sensor, eg, for an electromagnetic vectorsensor, it can additionally exploit the polarization difference among the received signals [19]. Traditionally the output of a vector-sensor array is preprocessed to be a long-vector [19][20][21][22][23][24][25][26][27], while several recent approaches utilize hypercomplex (eg, quaternion [18], bicomplex [28], biquaternion [29,30], quad-quaternion [31], Euclidean 3-space [32]) or tensorial (eg, fourth-order interspectral tensor [33]) models. The use of hypercomplex algebra provides a compact way of handling of the recorded data, and demonstrates its unique characteristics in reduced memory consumption and improved robustness to model errors [18].…”

Section: Introductionmentioning

confidence: 99%

User–Parameter–Free Robust Adaptive Beamforming Algorithm for Vector–Sensor Arrayswithin the Hypercomplex Framework

Gou¹,

Liu²,

Ma³

et al. 2013

Journal of Electrical Engineering

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The major flaw of the conventional diagonal loading (DL) method is that it is unclear to choose appropriate DL levels or user-parameters (UPs), though several remarkable contributions have been made to regularize model errors without UPs. An UP-free algorithm for two-component vector-sensor arrays, which is robust to steering vector errors, is considered. The algorithm is within the hypercomplex framework using quaternions, and the optimal solution is found at the maximal correlation between the quaternionic and complex outputs. The performance of the proposed beamformer is illustrated via numerical simulations and is compared with several other UP-free adaptive beamformers.K e y w o r d s: array signal processing, robust adaptive beamforming, vector-sensor, user-parameter-free, quaternion

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ESPRIT-based 2-D direction finding with a sparse uniform array of electromagnetic vector sensors

Cited by 209 publications

References 22 publications

Signal Detection and Jammer Localization in Multipath Channels for Frequency Hopping Communications

Signal Detection and Jammer Localization in Multipath Channels for Frequency Hopping Communications

Seismic Velocity and Polarization Estimation for Wavefield Separation

User–Parameter–Free Robust Adaptive Beamforming Algorithm for Vector–Sensor Arrayswithin the Hypercomplex Framework

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