Use of a highly deterministic multipath signal model in low-angle tracking

Lo, T.; Litva, J.

doi:10.1049/ip-f-2.1991.0022

Cited by 76 publications

(71 citation statements)

References 7 publications

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“…We consider only the first and second-order reflected signals because signals reflected more than twice can be neglected due to heavy attenuation [9]. The transmitted signal is assumed to be narrow band and denoted by [2] x(t) = be j(ωt+ξ) (1) where b, ω and ξ denote the amplitude, angular frequency and initial phase, respectively. The direct signal is…”

Section: Multipath Propagation Modelmentioning

confidence: 99%

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Target Detection in Low Grazing Angle With Ofdm Mimo Radar

Xia¹,

Song²,

Lu³

et al. 2016

PIER M

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Abstract-In low grazing angle scenario, target detection performance is seriously deteriorated due to multipath effect. This paper deals with moving target detection in low grazing angle with orthogonal frequency division multiplexing (OFDM) multi-input multi-output (MIMO) radar. We show that the detection performance can be improved through utilizing the multipath effect. Realistic physical and statistical effects such as refraction of the lower atmosphere and the Earth's curvature are incorporated into the multipath propagation model. Then, we derive a generalized likelihood ratio test (GLRT) detector by taking advantage of the frequency diversity of OFDM and MIMO configuration. Based on the fact that the target responses resonate at different frequencies and statistical characteristics of the test, we propose an algorithm which adaptively allocates the transmitted energy to improve the detection performance. The effectiveness of the GLRT detector as well as the adaptive design method is demonstrated via numerical examples.

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Section: Multipath Propagation Modelmentioning

confidence: 99%

“…The received signal is the coherent sum of direct and indirect signals. Amplitudes of the total received signals fluctuate due to the random phase variation of indirect signals (related to the path difference, wavelength and reflected surface [2]), which may seriously deteriorate the detection and tracking performances.…”

Section: Introductionmentioning

confidence: 99%

Target Detection in Low Grazing Angle With Ofdm Mimo Radar

Xia¹,

Song²,

Lu³

et al. 2016

PIER M

View full text Add to dashboard Cite

show abstract

“…In the presence of multipath, the received signals by each receiver consist of two components, namely, the direct and indirect signal. For a simple multipath model of a flat earth, the direct signal is given by [26] …”

Section: Problem Formulationmentioning

confidence: 99%

“…The vertical polarization and horizontal polarization Fresnel reflection coefficients are respectively [26] …”

Section: Problem Formulationmentioning

confidence: 99%

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Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Ding¹,

Chen²,

Wang³

et al. 2013

PIER B

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Abstract-In this paper, we focus on target detection and system configuration optimization of Multiple-input Multiple-output (MIMO) radar in low-grazing angle, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient, considering the curved earth effect, is derived. Then, the general signal model for MIMO radar is introduced for low-grazing angle. Using the NeymanPearson sense, the detector of MIMO radar with multipath is analyzed. We use the deflection coefficient as a criterion of system configuration both for MIMO radar and phased-array radar. The simulation results show that the performance can be enhanced markedly when the multipath effects are considered, and the optimal configuration of phased-array radar is with the same number of transmitters as that of receivers, however, the optimal configuration of MIMO radar depends on the signal-to-noise ratio (SNR).

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Direction‐of‐arrival estimation using estimator banks in low‐angle tracking for S‐band radar

Chen

Xiang

2021

Micro & Optical Tech Letters

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Tracking low‐angle targets over an uneven surface are challenging because of the highly correlated, complicated, and volatile multipath signals encountered in radar. Especially, in the context of irregular reflector modulated by rough sea, which results in performance depravation of the existing direction‐of‐arrival (DOA) estimation methods. An effective DOA estimation approach is based on maximum likelihood (ML) estimator which is referred to as optimal synthetic vector maximum likelihood (OSVML) method. The approach is essentially different, multipath signals are present that without known prior information of ideal model. The optimal projection space in estimator banks is spanned with direct subspace and indirect subspace to match the signals received by S‐band radar. Then, the performance of the proposed method is evaluated by simulations in terms of the signal‐to‐noise ratio (SNR) and snapshot. Finally, field data sets acquired from S‐band radar are carried out to verify the practicability of the proposed method in a naval environment.

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Use of a highly deterministic multipath signal model in low-angle tracking

Cited by 76 publications

References 7 publications

Target Detection in Low Grazing Angle With Ofdm Mimo Radar

Target Detection in Low Grazing Angle With Ofdm Mimo Radar

Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Direction‐of‐arrival estimation using estimator banks in low‐angle tracking for S‐band radar

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