Multitarget AOA Estimation Using Wideband LFMCW Signal and Two Receiver Antennas

Zhang, Dongheng; He, Ying; Gong, Xinyu; Hu, Yang; Zeng, Bing

doi:10.1109/tvt.2018.2827408

Cited by 33 publications

(8 citation statements)

References 26 publications

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“…Taking the process of up sweep frequency as an example. The signal model of LFMCW [17] is given as:stfalse(tfalse)=Uexp[j2π(f0t+kt22)]where U is the amplitude of the signal, k is the chirp rate of the up sweep frequency signal and k=2B/T.B is the bandwidth, and the echo signal can be expressed as follows:srfalse(tfalse)=U0exp{j2π[f0false(t−τfalse)+kfalse(t−τfalse)22]}where U0 is the amplitude of echo signal, τ is the delay time. Then zero intermediate frequency echo signal is obtained:sfalse(tfalse)=UU0exp{j2π[f0τ+ktτ−kτ…”

Section: Sparse-array Tdm Mimo Radar System Signal Processingmentioning

confidence: 99%

“…Among them, TDM [16] is the simplest choice to realize the MIMO radar system on hardware, and it is also widely used in practical engineering. Compared with the pulse radar system, the linear- frequency-modulated continuous-wave (LFMCW) [17] system’s anti-jamming performance is better, and it is transmitted by TDM in this system. Because of the time division mode of TDM system, the Doppler Effect causes additional phase shifts for a motion target [18].…”

Section: Introductionmentioning

confidence: 99%

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An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Miao

Zhao

et al. 2019

Sensors

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In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.

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Section: Sparse-array Tdm Mimo Radar System Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Miao

Zhao

et al. 2019

Sensors

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“…Wearable sensor based system, which requires physical contact between human body and the sensor, is an intrusive solution which makes it not suitable for long-term use. Due to non-intrusive and privacy persevering characteristic of RF signal, RF based systems are attracting more and more attention [1,[3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Pushing the Limit of Phase Offset for Contactless Sensing Using Commodity Wifi

Zhang

Chen

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose BreathTrack2.0, a contactless breath tracking system based on commodity WiFi devices. Breath-Track2.0 could track the detailed breath status through the phase variation of channel state information(CSI). To achieve this, BreathTrack2.0 first combines the signal on multiple antennas and subcarriers with a joint Angle of Arrival(AoA) and Time of Flight(ToF) beamformer to strengthen the signal from human. Then, it utilizes the fact that the phase offsets introduced by the hardware imperfection are the same among different propagation paths to cancel the phase offsets. Finally, the phase variation of the extracted human signal is utilized to estimate the breath rate and indicate the detailed breath status. Real world experiments demonstrate that the proposed system could estimate the breath rate with the median accuracy of over 99% and could track the detailed status of human breath.

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“…Although visible light is the most common media for imaging, it suffers from severe quality degradation under poor lighting conditions and blockage of obstacles [1]- [8]. To resolve this challenge, Radio Frequency (RF) signal, which is independent of light conditions and can traverse obstacles, has been an alternative for imaging [9]- [19]. Among several types of RF-based imaging methods, WiFi-based ones have attracted significant interests given the ubiquity of WiFi devices.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

He¹,

Zhang²,

Chen³

2021

Preprint

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WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root mean square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.

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Multitarget AOA Estimation Using Wideband LFMCW Signal and Two Receiver Antennas

Cited by 33 publications

References 26 publications

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Pushing the Limit of Phase Offset for Contactless Sensing Using Commodity Wifi

High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

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