Parametric localisation in frequency diverse array

Mahata, Kaushik; Hyder, Mashud

doi:10.1049/iet-rsn.2019.0350

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Tang et al [20] proposed a girdles compressed sensing algorithm to joint estimate range-angle. Reference [21] estimated the range by applying standard spectral analysis and posed the angle estimation as a variant of the block sparse signal recovery problem. Xu et al [22] transformed the joint range and angle estimation issues into a spectrum estimation problems and the minimum variance distortionless response (MVDR) algorithm used to jointly estimate the target range and angle.…”

Section: Introductionmentioning

confidence: 99%

Signal Separation and Target Localization for FDA Radar

Wang

Zhu

2020

IEEE Access

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Frequency diverse array (FDA) radar have attracted great interests due to the range-angledependent transmit beampattern which is different from phased array radar providing only angle-dependent transmit beampattern. In this paper, we firstly proposed a receiver processing strategy based on signal separation method which eliminates the need for employing a bank of bandpass filters at the receiver of FDA radar. In the proposed separation scheme, the received signal at each receiving element was separated into M channels, where M represents the transmitting element number. After time-invariant processing of the separated signal, the angle and range were estimated by two-stage multiple signal classification (MUSIC) algorithm. For velocity estimation, we proposed a novel unambiguous velocity estimation algorithm. This novel algorithm was implemented to calculate the phase of each element and then the differential phase within the adjacent elements is calculated. The velocity of the target was estimated by the differential phase. This mechanism for extending the Nyquist velocity range is that the differential phase of the two adjacent channels has a much smaller variance than the individual channel phase estimated. All estimated parameter performance is verified by analyzing the Cramér-Rao lower bound (CRLB) and the root mean square errors (RMSE).

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Section: Introductionmentioning

confidence: 99%