Stochastic Geometry for Automotive Radar Interference With RCS Characteristics

Fang, Zixi; Wei, Zhiqing; Chen, Xu; Wu, Huici; Feng, Zhiyong

doi:10.1109/lwc.2020.3003064

Cited by 36 publications

(19 citation statements)

References 10 publications

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“…We start the interference analysis applying a flat-top directional antenna, which has constant gain within its beamwidth and zero gain outside the beamwidth, like in the literature [11][12][13][14][15][16]. Among these, the maximum antenna gain is usually taken as the transmit and receive antenna gain in [11,12].…”

Section: Interference Modeling and Methodologymentioning

confidence: 99%

“…In [10], a simulation model based on ray-tracing was devised to predict the received interference power at an automotive victim radar for different traffic situations. Furthermore, the stochastic geometry method was leveraged to evaluate interference characterization in automotive radar network [11][12][13][14][15][16]. Al-Hourani et al [11] formulated an analytic framework characterizing the radar interference in terms of its cumulative distribution function and mean value, where locations of vehicular nodes on each lane are modeled as a Poisson point process (PPP) and a regular lattice.…”

Section: Introductionmentioning

confidence: 99%

“…The work in [15] examined the extent of automotive radar interference including incident interference and reflected interference. Meanwhile, the fluctuation of the target radar cross section (RCS) is considered to precisely analyze the radar ranging performance by using Swerling I model and Chi-square model [16].…”

Section: Introductionmentioning

confidence: 99%

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Interference Analysis for mmWave Automotive Radar Considering Blockage Effect

Kui

Huang

Feng

2021

Sensors

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Due to the increasing number of vehicles equipped with millimeter wave (mmWave) radars, interference among automotive radars is becoming a major issue. This paper explores the automotive radar interference in both two-lane and multi-lane scenarios using stochastic geometry. We derive closed-form expressions for mean and variance of interference power considering directional antenna with constant and Gaussian decaying gains. In view of the sensitivity of mmWave radar signals to the blockages, we propose a blockage model including partially and completely blocking, and then calculate the effective number of the interferers. By means of modeling randomness for interferers and blockages as Poisson point process, we characterize the statistics of radar interference under different conditions. We further utilize the interference characterization to estimate the successful ranging probability of automotive radars. These theoretical analyses are verified by using Monte Carlo simulations. The results show that the increasing interfering density and ranging distance largely degrade the radar detection performance, whereas the interference levels decrease as blockage intensity increases.

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Section: Interference Modeling and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interference Analysis for mmWave Automotive Radar Considering Blockage Effect

Kui

Huang

Feng

2021

Sensors

Self Cite

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“…In this study, the unimodal parametric densities considered are 1) Peter Swerlings statistical models, 2) gamma distribution, and 3) generalized Pareto distribution (GPD). The Peter Swerlings statistical models have been widely used to describe the radar scattering characteristics of airborne military aircraft and ground vehicles/tanks [45], [46]. The Peter Swerling RCS density is described by the generalized Chi-Squared (χ 2 or CS) distribution with N = 2m degrees of freedom given as:…”

Section: A Unimodal Uav Rcs Statistical Distributionmentioning

confidence: 99%

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Ezuma¹,

Anjinappa²,

Semkin³

et al. 2021

Preprint

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This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-tonoise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.

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“…For a planar geometry, [14] considers the strongest interferer approximation to obtain the radar detection range and the false alarm rate. While the previous works assume a constant radar cross-section (RCS), [15] have recently studied the standard successful radar detection probability p s P(SINR > β) with random RCS characterized by Swerling I model [16] that captures the varying physical features of the target vehicle. While p s is an important performance metric, it is simply a spatial average of the detection performance of all radars in a given network realization.…”

Section: B Related Workmentioning

confidence: 99%

A Fine-Grained Analysis of Radar Detection in Vehicular Networks

Ghatak

Kalamkar

Gupta

et al. 2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Automotive radar is a critical feature in advanced driver-assistance systems. It is important in enhancing vehicle safety by detecting the presence of other vehicles in the vicinity. The performance of radar detection is, however, affected by the interference from radars of other vehicles as well as the variation in the target radar cross-section (RCS) due to varying physical features of the target vehicle. Considering such interference and random RCS, this work provides a fine-grained performance analysis of radar detection. Specifically, using stochastic geometry, we calculate the meta distribution of the signal-tointerference-and-noise ratio that permits the reliability analysis of radar detection at individual vehicles. We also evaluate the delay aspect of radar detection, namely, the mean local delay which is the average number of transmission attempts needed until the first successful target detection. For a given target distance, we obtain the optimal transmit probability that maximizes the density of successful radar detection while keeping the mean local delay below a threshold. We also provide several system design insights in terms of the fraction of reliable radar links, transmission delay, the density of vehicles, and congestion control.

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Stochastic Geometry for Automotive Radar Interference With RCS Characteristics

Cited by 36 publications

References 10 publications

Interference Analysis for mmWave Automotive Radar Considering Blockage Effect

Interference Analysis for mmWave Automotive Radar Considering Blockage Effect

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

A Fine-Grained Analysis of Radar Detection in Vehicular Networks

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