DOA estimation method for multi‐path targets based on TR MIMO radar

Liu, Mengbo; Hu, Guoping; Shi, Junpeng; Zhou, Hua

doi:10.1049/joe.2018.5134

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Then, the DOA of the target (i.e., θ) is estimated from {y[t]} T t=1 by using the MUSIC algorithm, as elaborated in the next section. Note that different from the traditional MIMO radar/phased-array radar for target sensing [42], the IRS sensors in the proposed self-sensing IRS receive signals over the two links from the same echo angle, i.e., θ, and hence achieving enhanced DOA estimation performance.…”

Section: B Proposed Protocol For Irs-enabled Target Angle Estimationmentioning

confidence: 99%

Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

Shao¹,

You²,

Ma³

et al. 2022

Preprint

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Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal's amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS's performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a new self-sensing IRS architecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRSreflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals' total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.

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Section: B Proposed Protocol For Irs-enabled Target Angle Estimationmentioning

confidence: 99%

Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

Shao¹,

You²,

Ma³

et al. 2022

Preprint

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“…As the reference [22] does, we suppose that c k = c for all the pulses. Applying matched filtering to the received signal in Equation ( 6), the new signals y k (t) are given by [15]:…”

Section: The Normalization Coefficientmentioning

confidence: 99%

“…As for TR MIMO parameter estimation, Tan proposes an adaptive TR MUSIC algorithm to accomplish direction of arrival (DOA) estimation in a mirror multipath scene by matching multipath channels [14]. Without referring to the precise multipath model in [14], Liu multiplexes the rows and columns and applies the forward-backward spatial smoothing technique to perform multiple targets' parameter estimation [15]. However, these algorithms suffer from diffuse multipath which is difficult to be modeled in real low-angle scenes.…”

Section: Introductionmentioning

confidence: 99%

Joint Angle-Doppler Estimation Algorithm Based on Time Reversal Post-Doppler Adaptive MUSIC in Low-Angle Multipath Environments

Xiong¹,

Fan²,

Huang³

2020

Sensors

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This letter proposes a time-reversal (TR) post-Doppler adaptive multiple signal classification (MUSIC) algorithm for multiple-input multiple-output (MIMO) radars, which addresses the joint estimation of angle and Doppler in diffuse multipath environments. First, an improving TR MIMO multipath model is proposed to avoid the ambiguity between the direction and Doppler in one round trip. Then, the letter designs a spatial filter matrix according to transmit-receive steering matrices, suppressing undesired round trips. Finally, we combine the post-Doppler adaptive MUSIC algorithm and the designed filter to estimate angle and Doppler jointly. Simulation results verify the applicability and effectiveness of the proposed model and algorithm.

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“…Signal source localization technology has been an important technical development [1], which is widely used in the fields of radar [2][3][4], remote sensing [5][6][7][8], microphone systems [9], unmanned aerial vehicles [10], and autonomous vehicles [11]. In source localization techniques, array aperture and degrees of freedom (DOFs) are critical in determining the accuracy of estimating the direction of arrival (DOA) for signal sources [12].…”

Section: Introductionmentioning

confidence: 99%

Symmetric Double-Supplemented Nested Array for Passive Localization of Mixed Near-Field and Far-Field Sources

Wu,

Qi,

Wang

et al. 2024

Remote Sensing

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In mixed-field source localization, the physical properties of a sensor array, such as the degrees of freedom (DOFs), aperture, and coupling leakage, directly affect the accuracy of estimating the direction of arrival (DOA). Compared to conventional symmetric uniform linear arrays, symmetric non-uniform linear arrays (SNLAs) have a greater advantage in mixed-field source localization due to their larger aperture and higher DOF. However, current SNLAs require improvements in their physical properties through modifications to the array structure in order to achieve more accurate source localization estimates. Therefore, this study proposes a symmetric double-supplemented nested array (SDSNA), which translates nested subarrays based on symmetric nested arrays to increase the aperture and inserts two symmetric supplemented subarrays to fill the holes created by the translation. This method results in longer consecutive difference coarray lags and larger apertures. The SDSNA is compared to existing advanced SNLAs in terms of their physical properties and DOA estimation. The results show that, with the same number of sensors, the SDSNA has a higher DOF, a larger aperture, and smaller coupling, indicating the advantages of the SDSNA in terms of its physical properties. Under the same experimental conditions, the SDSNA has a lower root-mean-square error of source location, thus indicating better performance in terms of both DOA and distance estimation.

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DOA estimation method for multi‐path targets based on TR MIMO radar

Cited by 6 publications

References 13 publications

Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

Joint Angle-Doppler Estimation Algorithm Based on Time Reversal Post-Doppler Adaptive MUSIC in Low-Angle Multipath Environments

Symmetric Double-Supplemented Nested Array for Passive Localization of Mixed Near-Field and Far-Field Sources

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