Collaborative Sensing Techniques

Sandor, Christian; Pesavento, Marius

doi:10.1002/9781119551539.ch7

Cited by 3 publications

(1 citation statement)

References 85 publications

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“…where the nuclear norm regularization Z 𝑘 * = tr (Z H 𝑘 Z 𝑘 ) 1/2 encourages block rank sparsity, i.e., the solutions blocks Z 𝑘 shall either be zero or low rank [67,45,28,27]. Similar to Problem (14) also Problem (18) admits a convenient reformulation with a significantly reduced number optimization variables, as provided by the following theorem [41,42].…”

Section: Theorem 3 (Problem Equivalence 1)mentioning

confidence: 99%

Recovery under Side Constraints

Ardah¹,

Haardt²,

Liu³

et al. 2021

Preprint

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This chapter addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representation vector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using ℓ 1 -minimization. Furthermore, for constant modulus signals, signals with row-, block-and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning.

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Section: Theorem 3 (Problem Equivalence 1)mentioning

confidence: 99%

Recovery under Side Constraints

Ardah¹,

Haardt²,

Liu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Recovery Under Side Constraints

Ardah

Haardt

Liu

et al. 2022

Compressed Sensing in Information Processing

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Improve Radar Sensing Performance of Multiple Roadside Units Cooperation via Space Registration

Jiang

Wei

et al. 2022

IEEE Trans. Veh. Technol.

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Roadside units (RSUs) can help vehicles sense the traffic environment, so as to improve traffic safety. Since the sensing capability of single RSU is limited, we propose a multiple RSUs cooperative radar sensing network (RSU-CRSN) with signallevel fusion technique. Spatial registration is an essential prerequisite and foundation for RSU-CRSN with signal-level fusion. In this paper, we present an adjustable beam enabled spatial registration algorithm (AB-SRA) that makes the sensing area of each RSU coincide by adjusting the sensing beam width of RSU. To adjust the width of sensing beam flexibly, a beamwidth adjustable beamforming algorithm (BABA) is proposed in this paper. Simulation results show that the performance of AB-SRA is close to perfect spatial registration.

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Collaborative Sensing Techniques

Cited by 3 publications

References 85 publications

Recovery under Side Constraints

Recovery under Side Constraints

Recovery Under Side Constraints

Improve Radar Sensing Performance of Multiple Roadside Units Cooperation via Space Registration

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