Autonomous Vehicle Source Enumeration Exploiting Non-Cooperative UAV in Software Defined Internet of Vehicles

Wan, Liangtian; Sun, Lu; Liu, Kaihui; Wang, Xianpeng; Lin, Qingqing; Zhu, Tong

doi:10.1109/tits.2020.3018377

Cited by 31 publications

(18 citation statements)

References 46 publications

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“…In radar imaging, the targets are generally composed of strong scattering points, which are sparsely distributed in the imaging region. So radar signal processing based on compressive sensing theory has been widely studied [9][10][11][12], such as SAR imaging [13], ISAR imaging [14,15], and MIMO radar imaging [16]. The CS theory is not sensitive to data loss, which can effectively improve the imaging problem of radar imaging system in the case of data loss and improve the signal processing ability.…”

Section: Introductionmentioning

confidence: 99%

High‐Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

Feng

Sun

2021

Wireless Communications and Mobile Computing

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In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.

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Section: Introductionmentioning

confidence: 99%

High‐Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

Feng

Sun

2021

Wireless Communications and Mobile Computing

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“…Recently, multiple-inputmultiple-output (MIMO) radar has attracted a lot of attention due to its unique advantages, such as high degrees of freedom (DOF) and parameter estimation accuracy [5]- [9]. In order to deal with complex and dynamic clutter, MIMO radar adopts flexible signal waveforms and array antenna configuration methods to obtain greater freedom [10], [11]. However, MIMO radar also has certain shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar

Wang

et al. 2021

IEEE Access

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Edge computing has a wide range of applications in the Internet of Things (IoT), especially suitable for low latency, high bandwidth, and high reliability. Edge computing enabling IoT can locate the vehicles rapidly with the help of edge computing. The sensors of IoTs can construct the Frequency diverse array (FDA) radar system, which has been widely concerned by scholars in recent years. The monostatic FDA and multiple-input-multiple-output (FDA-MIMO) is a research hotspot in parameter estimation field, but the research based on bistatic FDA-MIMO radar is insufficient. In this paper, we propose a tensorbased target location method in bistatic FDA-MIMO radar, which implements joint direction of arrival (DOA), direction of departure (DOD) and range parameters estimation. First of all, subarrays with different transmission frequency increment are used to construct the transmitting array to overcome the coupling between DOD and range. Then the tensor-based third-order signal model is established, which saves the multidimensional structure characteristics of received signal. And the signal subspace of each subarray is estimated by high-order-singular value decomposition (HOSVD). Furthermore, the phase period ambiguity is eliminated by limiting the range of the target, and the method for DOA, DOD and range parameters matching is provided. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.

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“…Indoor positioning systems are increasingly very important for a smart factory, such as finding the location of workers, goods, or vehicles [1][2][3][4]. However, the Global Positioning System (GPS) is unable to provide the indoor positioning service, which is commonly used for outdoor positioning.…”

Section: Introductionmentioning

confidence: 99%

Lora RTT Ranging Characterization and Indoor Positioning System

Liu

Bai

Gan

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, indoor positioning systems (IPS) are increasingly very important for a smart factory, and the Lora positioning system based on round-trip time (RTT) has been developed. This paper introduces the ranging characterization, RTT measurement, and position estimation method. In particular, a particle filter localization method-aided Lora pseudorange fitting correction is designed to solve the problem of indoor positioning; the cumulative distribution function (CDF) criteria are used to measure the quality of the estimated location in comparison to the ground truth location; when the positioning error on the x -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 61% and 99%, which are higher than the 32% and 85% without pseudorange correction. When the positioning error on the y -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 71% and 99.9%, which are higher than the 52% and 94.8% without pseudorange correction.

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Autonomous Vehicle Source Enumeration Exploiting Non-Cooperative UAV in Software Defined Internet of Vehicles

Cited by 31 publications

References 46 publications

High‐Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

High‐Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar

Lora RTT Ranging Characterization and Indoor Positioning System

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