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Interference Analysis for mmWave Automotive Radar Considering Blockage Effect.

Liping Kui¹, Sai Huang¹, Zhiyong Feng¹

¹School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

Automotive radar interference is analyzed using stochastic geometry. Increased interference density and range degrade performance, while blockages can reduce interference, impacting radar detection.

Keywords:

blockage modeling interference analysis mmWave automotive radars radar ranging performance stochastic geometry

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Area of Science:

Electrical Engineering
Wireless Communications
Signal Processing

Background:

Millimeter wave (mmWave) automotive radars face increasing interference due to more vehicles.
Interference impacts radar performance, especially in complex traffic scenarios.

Purpose of the Study:

To analyze automotive radar interference in two-lane and multi-lane traffic using stochastic geometry.
To develop models for interference power, considering directional antennas and signal blockages.
To estimate the successful ranging probability of automotive radars under various interference conditions.

Main Methods:

Stochastic geometry framework to model interferer distribution.
Derivation of closed-form expressions for interference power mean and variance.
Development of a blockage model for partially and completely blocked signals.
Poisson point process used to model interferers and blockages.
Monte Carlo simulations for theoretical validation.

Main Results:

Interference power statistics characterized under different conditions.
Effective number of interferers calculated considering blockages.
Successful ranging probability estimated.
Performance degradation observed with increased interfering density and ranging distance.
Interference levels found to decrease with increased blockage intensity.

Conclusions:

The study provides a theoretical framework for understanding automotive radar interference.
Blockages can mitigate interference but also affect signal propagation.
Accurate modeling is crucial for predicting and improving automotive radar performance.