Low-Complexity Time-Domain Ranging Algorithm with FMCW Sensors

Xi, Peng; Xiang, Chengyong; Liu, Shouliang; Yan, Shuo

doi:10.3390/s19143176

Cited by 6 publications

(5 citation statements)

References 16 publications

(16 reference statements)

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“…Recently, joint communication and positioning in mobile radio systems has gained a lot of interests in rapid deployment for both civilian and military applications [15]- [17]. Traditionally, one of the most popular ways to obtain location information relied on Radar, where dedicated radio frequencies are allocated to optimize the range and speed estimation performances [18]. While there are many classes of localization techniques, high resolution range estimation is one of key techniques to enable or improve range-based radio positioning, particularly for line of sight (LOS) environment in wireless sensor networks, Internet-of-things (IOT), and machine-to-machine (M2M) communication [19].…”

Section: A Uav Communication and Positioningmentioning

confidence: 99%

Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs

Yan²,

Zhang

2021

IEEE Access

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The localization accuracy demand is ever growing in UAV communication networks. We propose a joint coarse and fine range estimation method using single carrier burst signals with two samples per symbol for UAV networks. The coarse estimation of our joint estimation method exploits multiple preamble symbols for flexible single-carrier frequency-domain equalization (SC-FDE) frame structures to calculate correlation metrics, which are insensitive to frequency offset due to the differential correlation operation. Then, we propose a fine range estimation method using only two samples per symbol with expectation relying on shaping or matched filter. Furthermore, we derive the performance bounds for the ranging system using both raised cosine (RC) and better than raised-cosine (BTRC) pulses. Finally, extensive simulations are conducted to validate the proposed method in terms of estimate bias and variance for different modulations, shaping filters, and fading channels. Our simulation results show that, the root mean square errors of proposed ranging method can reach the order of centimeter at medium-to-high signal-to-noise ratio (SNR) region, whereas the case using BTRC filter is capable of enhancing the ranging performance at low SNRs.

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Section: A Uav Communication and Positioningmentioning

confidence: 99%

Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs

Yan²,

Zhang

2021

IEEE Access

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“…In an FMCW radar system, a voltage-controlled oscillator (VCO) creates an LFM signal whose frequency increases linearly as a function of time [14]. The time-frequency slopes of the transmitted and received LFM signals are shown in Figure 1.…”

Section: Basic Principles Of Fmcw Radarmentioning

confidence: 99%

CNN‐based estimation of heading direction of vehicle using automotive radar sensor

Lim

Jung

Lee

et al. 2021

IET Radar Sonar & Navi

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Modern autonomous vehicles are being equipped with various automotive sensors to perform special functions. Especially, it is important to predict the heading direction of the front vehicle to adjust the speed of the ego-vehicle and select appropriate actions. Here, we propose a method for estimating the instantaneous heading direction of a vehicle using automotive radar sensor data. First, using a frequency-modulated continuous wave (FMCW) radar in the 77 GHz band, we accumulate the automotive radar sensor data for different movements of the front vehicle (e.g., stop, going ahead, reversing, turning left, and turning right). To distinguish the different movements of the vehicle, we use the convolutional neural network (CNN) and train it using the acquired radar sensor data. Because the CNN algorithm usually uses image data as input, it is essential to convert radar sensor data into image data. Therefore, we apply a highresolution angle estimation algorithm to the obtained radar data and convert it into a two-dimensional range map. After the CNN model is trained with the obtained radar sensor data, various movements of the front vehicle can be classified with over 94% of accuracy.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In order to identify the conditions resulting in low complexity, we determine a condition where the complexity of two algorithms becomes equal by calculating the difference between the complexities of the two algorithms, i.e., C DFT = C ROI . >From (13) and (14), the condition for C DFT = C ROI is expressed using N P as follows:…”

Section: Complexity Analysismentioning

confidence: 99%

“…Meanwhile, there have been several studies on frequency-modulated continuous wave (FMCW) radar systems as they have many advantages, such as low cost and low complexity [7][8][9][10][11][12][13]. Compared to pulsed radar, which requires large bandwidth and high cost, FMCW radar systems have relatively narrow bandwidth and low transmitted peak power, thus FMCW radar can meet certain range and velocity requirements with relatively low cost hardware and architectures [7][8][9][10][11][12][13]. In FMCW radar systems, a fast Fourier transform (FFT) is typically used as an estimator for parameters such as range, velocity, and angle.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets

Kim

Jin

et al. 2019

Sensors

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A low-complexity joint range and Doppler frequency-modulated continuous wave (FMCW) radar algorithm based on the number of targets is proposed in this paper. This paper introduces two low-complexity FMCW radar algorithms, that is, region of interest (ROI)-based and partial discrete Fourier transform (DFT)-based algorithms. We find the low-complexity condition of each algorithm by analyzing the complexity of these algorithms. From this analysis, it is found that the number of targets is an important factor in determining complexity. Based on this result, the proposed algorithm selects a low-complexity algorithm between two algorithms depending the estimated number of targets and thus achieves lower complexity compared two low-complexity algorithms introduced. The experimental results using real FMCW radar systems show that the proposed algorithm works well in a real environment. Moreover, central process unit time and count of float pointing are shown as a measure of complexity.clutter, there is no Doppler effect. Hence, two beat signals are the same except for noise term and thus the difference of two beat signals contains only noise term. On the other hand, in the case of moving target, there is Doppler effect due to the moving target and thus the difference of them contains the range information of moving target. However, this algorithm might miss the moving target with certain velocity because this algorithm fixedly employs two beat signals. In order to overcome this disadvantage, an FMCW radar algorithm has been proposed by randomly employing two beat signals in [14]. This algorithm effectively avoids missing a target with a certain velocity by randomly selecting two beat signals every frame. In addition, this algorithm performs an angle detection algorithm only if there is a moving target. Therefore, this algorithm reduces the overall complexity. However, this algorithm has still a disadvantage in that it does not detect the velocity of the target. This is because the difference between the two beat signals is used to reduce the complexity of the moving target detection process and in this process, information necessary for the velocity detection of the target is lost.Meanwhile, in [15][16][17][18], low-complexity detection algorithms for FMCW radar have been proposed which intend to reduce the number of FFTs compared to a full-dimension FFT-based FMCW radar algorithm. These algorithms determine a region of interest (ROI), thus reducing the number of inputs in the FFTs for Doppler estimation. However, there is still unnecessary computational complexity in these algorithms, although the complexity is reduced. The number of range bins used as the input in FFTs for Doppler estimation depends on the number of targets. In this algorithm, all chirp signals are used in an FFT to determine the range bins in which peaks exist. However, there is a disadvantage in that the number of range bins calculated in the first FFT for range estimation is too large compared to the number of range bins used as inputs in the FFT fo...

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Low-Complexity Time-Domain Ranging Algorithm with FMCW Sensors

Cited by 6 publications

References 16 publications

Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs

Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs

CNN‐based estimation of heading direction of vehicle using automotive radar sensor

Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets

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