Structure‐based joint estimation algorithm for distributed coherent aperture radar

Liu, Yin

doi:10.1049/joe.2019.0907

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…In the UKF, the state dimension is k = 1, and the scaling ratio is ρ = 6. References [44,45] introduce an envelope synchronization combining a two-way time comparison algorithm based on UAVs, and the time synchronization accuracy of this algorithm is less than 0.1 ns. Reference [46] described a technique for determining the true time delay using the ambiguous two-tone matched filter output and obtained a timing precision of 2.26 ps.…”

Section: Parameter Estimation Simulationmentioning

confidence: 99%

Near-Field Beamforming Algorithms for UAVs

Zhang¹,

Wang²,

Peng³

et al. 2023

Sensors

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This study presents three distributed beamforming algorithms to address the challenges of positioning and signal phase errors in unmanned aerial vehicle (UAV) arrays that hinder effective beamforming. Firstly, the array’s received signal phase error model was analyzed under near-field conditions. In the absence of navigation data, a beamforming algorithm based on the Extended Kalman Filter (EKF) was proposed. In cases where navigation data were available, Taylor expansion was utilized to simplify the model, the non-Gaussian noise of the compensated received signal phase was approximated to Gaussian noise, and the noise covariance matrix in the Kalman Filter (KF) was estimated. Then, a beamforming algorithm based on KF was developed. To further estimate the Gaussian noise distribution of the received signal phase, the noise covariance matrix was iteratively estimated using unscented transformation (UT), and here, a beamforming algorithm based on the Unscented Kalman Filter (UKF) was proposed. The proposed algorithms were validated through simulations, illustrating their ability to suppress the malign effects of errors on near-field UAV array beamforming. This study provides a reference for the implementation of UAV array beamforming under varying conditions.

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Section: Parameter Estimation Simulationmentioning

confidence: 99%

Near-Field Beamforming Algorithms for UAVs

Zhang¹,

Wang²,

Peng³

et al. 2023

Sensors

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show abstract

“…With the development of the Statistical, or Distributed, Multi-Input Multi-Output (MIMO) Radar System [9][10][11] and Netted Radar System [12,13] concepts, partially coherent processing techniques were proposed, eventually leading to the investigation of fully-coherent techniques requiring that stringent synchronisation requirements be met. The concept of a Distributed Coherent Aperture [14][15][16][17][18] was proposed as a way of achieving coherence spatially through appropriate waveform transmission timings, and recently there has been considerable interest in the use of Microwave Photonics as a means of attaining spatial coherence using a hardware-based approach [5,[19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

On the design of an optimal coherent multistatic radar network configuration

Berry

Dahal

Venkataraman

2022

IET Radar Sonar & Navi

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The purpose of operating a radar system multistatically is to acquire information about targets of interest more quickly and reliably than would be possible using disparate radars, which fuse data and track post‐detection. This paper identifies and addresses the critical issues for designing a coherent multistatic radar system using conventional coherent radars and proposes a set of design principles for configuring a coherent multistatic radar network with the aim of maximising the utility of information while constraining system cost and complexity. The issues discussed are as follows: the localisation of targets in three dimensions; eliminating the inevitable spatial ambiguities arising from sparse spatial sampling while exploiting the large aperture for high resolution; the achievement of spatial coherent processing gain for target detection; the effect of phase centre localisation, phase and frequency errors on synchronisation; the incorporation of three‐dimensional Doppler estimation for slow‐time coherent integration. The arguments presented are physically‐based and complemented by mathematical analysis and numerical experimentation using non‐linear sparse estimation, the sparse algorithm being critically important for resolving spatial ambiguities. An optimal configuration is identified, which has sufficiently many nodes to overcome spatial ambiguities and achieves significant spatial coherent processing gain, while simultaneously constraining the overheads of inter‐node synchronisation and communication.

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Stepped Frequency Signal-Based STAP for Airborne Distributed Coherent Aperture Radar

Wang,

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Structure‐based joint estimation algorithm for distributed coherent aperture radar

Cited by 4 publications

References 23 publications

Near-Field Beamforming Algorithms for UAVs

Near-Field Beamforming Algorithms for UAVs

On the design of an optimal coherent multistatic radar network configuration

Stepped Frequency Signal-Based STAP for Airborne Distributed Coherent Aperture Radar

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