A derivation of colocated MIMO radar equation

Li, Xiangru; Zhao, Zhongwei; Mao, Wu-Xing; Wang, Xiaomo; Lu, Jun

doi:10.1109/iccps.2011.6089762

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…The main lobe of the beam can be made narrower by adjusting the number of antennas to achieve a longer radar sensing range. According to the MIMO radar equation [32], the maximum radar range can be expressed as…”

Section: B Desired Radar Beamformer Calculationmentioning

confidence: 99%

“…for simplicity. Assuming the total power is evenly distributed to all antennas and the transmit power of each antenna is P 0 , the radar transmit power at the k-th beam can be expressed as [32]…”

Section: B Desired Radar Beamformer Calculationmentioning

confidence: 99%

“…(32) becomes a classic convex optimization problem, which can be solved by conventional convex optimization tools, such as CVX toolbox. In (32), we relax the rank-one constraint, resulting in the globally optimal solution to (32). However, the global optimum of (32) is not equivalent to an even feasible solution to (30) when the rank of X is higher than one.…”

Section: Problem Formulation For Vba-isacmentioning

confidence: 99%

“…In (32), we relax the rank-one constraint, resulting in the globally optimal solution to (32). However, the global optimum of (32) is not equivalent to an even feasible solution to (30) when the rank of X is higher than one. Utilizing the approach proposed in [35], we apply the eigen-decomposition of X to yield an approximate solution to (30).…”

Section: Problem Formulation For Vba-isacmentioning

confidence: 99%

“…The computational complexity of such calculation can be omitted in general because the complexity of the entire scheme is dominated by the SDR algorithm. According to the methodology adopted in [35], the worst-case complexity of solving (32) is O N t 3.5 N s 3.5 .…”

Section: E Vba-isac Algorithm and Computational Complexity Analysismentioning

confidence: 99%

See 4 more Smart Citations

Vehicular Behavior-Aware Beamforming Design for Integrated Sensing and Communication Systems

Cong¹,

Guo²,

Dang³

et al. 2023

Preprint

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Communication and sensing are two important features of connected and autonomous vehicles (CAVs). In traditional vehicle-mounted devices, communication and sensing modules exist but in an isolated way, resulting in a waste of hardware resources and wireless spectrum. In this paper, to cope with the above inefficiency, we propose a vehicular behavior-aware integrated sensing and communication (VBA-ISAC) beamforming design for the vehicle-mounted transmitter with multiple antennas. In this work, beams are steered based on vehicular behaviors to assist driving and meanwhile provide spectral-efficient uplink data services with the help of a roadside unit (RSU). Specifically, we first predict the area of interest (AoI) to be sensed based on the vehicles' trajectories. Then, we formulate a VBA-ISAC beamforming design problem to sense the AoI while maximizing the spectral efficiency of uplink communications, where a trade-off factor is introduced to balance the communication and sensing performance. A semidefinite relaxation-based beampattern mismatch minimization (SDR-BMM) algorithm is proposed to solve the formulated problem. To reduce the hardware cost and power consumption, we further improve the proposed VBA-ISAC beamforming design by introducing the hybrid analog-digital (HAD) structure. Numerical results verify the effectiveness of VBA-ISAC scheme and show that the proposed beamforming design outperforms the benchmarks in both spectral efficiency and radar beampattern.Index Terms-Integrated sensing and communication (ISAC), vehicular behavior-aware beamforming design, the intelligent transportation system (ITS), and vehicular networks.

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Section: B Desired Radar Beamformer Calculationmentioning

confidence: 99%

Section: B Desired Radar Beamformer Calculationmentioning

confidence: 99%