Full-polarization matched-illumination for target detection and identification

Garren, David A.; Odom, A.C.; Osborn, M.K.; Goldstein, J.S.; Pillai, S.U.; Guerci, J.R.

doi:10.1109/taes.2002.1039402

Cited by 92 publications

(35 citation statements)

References 7 publications

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“…Hence, time reversal is a radar waveform adaptive transmission scheme. Other radar waveform design strategies are given in, e.g., [4]- [6]. Our recent work, [7]- [9] considers signal detection using time reversal with a single antenna pair as well as with antenna arrays, and demonstrates that the time reversal generalized likelihood ratio detector (TR-GLRT) significantly improves detection performance when compared with conventional detectors.…”

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confidence: 99%

Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

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Abstract-Time reversal explores the rich scattering in a multipath environment to achieve high target detectability. Multiple-input multiple-output (MIMO) radar is an emerging active sensing technology that uses diverse waveforms transmitted from widely spaced antennas to achieve increased target sensitivity when compared to standard phased arrays. In this paper, we combine MIMO radar with time reversal to automatically match waveforms to a scattering channel and further improve the performance of radar detection. We establish a radar target model in multipath rich environments and develop likelihood ratio tests for the proposed time-reversal MIMO radar (TR-MIMO). Numerical simulations demonstrate improved target detectability compared with the commonly used statistical MIMO strategy.

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confidence: 99%

Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

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“…Thus, the conventional detection is formulated based upon the measurement pair , where is defined in (7), and is given by (20) where (21 The subscript denotes the illuminating signal transmitted from array . The energy normalization factor is (25) Like for TR, we concatenate the data and into a vector and collect all the available data in the Gram data matrix , defined by (26) (27)…”

Section: A Data Organizationmentioning

confidence: 99%

Time-Reversal Detection Using Antenna Arrays

Jin

Moura

2009

IEEE Trans. Signal Process.

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Abstract-The paper studies detection of a target buried in a rich scattering medium by time reversal. We use a multi-static configuration with receive and transmit arrays of antennas. In time reversal, the backscattered field is recorded, time reversed, and retransmitted (mathematically or physically) into the same scattering medium. We derive two array detectors: the time-reversal channel matched filter when the target channel response is known; and the time-reversal generalized-likelihood ratio test (TR-GLRT) when the target channel response is unknown. The noise added in the initial probing step to the time-reversal signal makes the analysis of the TR-GLRT detector non trivial. The paper derives closed form expressions for the signal-to-noise ratio gain provided by this detector over the corresponding conventional clutter subtraction energy detector in the two extreme conditions of weak and strong (electronic additive) noise and shows that time reversal provides, under weak noise, the optimal waveform shape to probe the environment. We analyze the impact of the array configuration on the detection performance. Finally, experiments with electromagnetic data collected in a multipath scattering laboratory environment confirm our analytical results. Under the realistic conditions tested, time reversal provides detection gains over conventional detection that range from 2 to 4.7 dB.

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“…When identifying radar targets, researchers have used resonance based feature set [1][2][3][4], and sometime incorporated along a polarimetric feature to enhance performance [5][6][7][8][9][10][11][12]. As the target works as a polarization transformer, features such as symmetry, elongation and tilt that are related to the target shape can be deduced from the target characteristic polarization states (CPS).…”

Section: Introductionmentioning

confidence: 99%

Description of Radar Target Using Resonance Based Null Polarization

2016

IJSR

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Abstract:By the representation of the target transient response as a series of natural resonance modes, a target feature set based on the co-polarized null states of the modes is used to represent a radar target. This is done by incorporating the mode residue terms in a scattering matrix of quadrature polarization channels, and then bythe optimization (using the Lagrangian optimization procedure) of the received power (determined by the Kennaugh and Stokes formulism), the characteristic polarization state set of a mode is obtained. A 2D plane model is used to demonstrate the ability of this concept to convey a complex target physical attributes such as elongation, symmetry and tilt; the results showed that a feature set based on nulls states of the target modes is robust.

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Full-polarization matched-illumination for target detection and identification

Cited by 92 publications

References 7 publications

Time Reversal in Multiple-Input Multiple-Output Radar

Time Reversal in Multiple-Input Multiple-Output Radar

Time-Reversal Detection Using Antenna Arrays

Description of Radar Target Using Resonance Based Null Polarization

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