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Orthogonality using Frequency Division Multiplexing to enable Multiple Input Multiple Output in an Automotive Radar

机译:使用频分复用实现正交性的汽车雷达中的多输入多输出

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Autonomous vehicles have always been a field of considerable research interest. Past research have demonstrated achievements assuring that self-driving cars arc, in fact the future of mobility. Self-driving cars have been made possible by sensor fusion technique, which incorporates sensors, including camera and radar. Cameras have the best resolution. Nevertheless, their ability to sense may be affected in extreme weather or night conditions. Radars are not affected by these conditions but lack the resolution when compared with radar. Most of the automotive radars are Frequency Modulated Continuous Wave (FMCW) radars whose range resolution depends on the bandwidth of the FMCW chirp, and spatial resolution depends upon the number of the receiving antennas. Having a higher number of receiving antenna elements will improve the angular resolution. Instead of increasing physical receiving antennas, it is possible to generate virtual receiving antennas by adding transmitting antennas, commonly known as the Multiple Input Multiple Output (MIMO) technique. MIMO requires orthogonal signals in multiple transmitting antennas. Commercial automotive radars have implemented the capability of MIMO using Time Division Multiplexing (TDM) and Binary Phase Modulation (BPM) in 2Tx and 4Rx systems. Although the angular resolution is improved, the maximum unambiguous velocity is reduced by half. This paper proposes the Frequency Division Multiplexing (FDM) Technique to achieve orthogonality. A full radar system has been simulated in MATLAB environment, which shows the possibility of using FDM in automotive radars without compromising the maximum unambiguous velocity. Frequency modulated signal with different start-ing frequencies for two Tx antenna is used to create 8 Rx virtual channels. FDM usually requires an increment in sampling frequency of Analog to Digital Converter (ADC). In this paper, the two starting frequencies are chosen, such that the requirement of higher sampling rate has been eliminated.
机译:无人驾驶汽车一直是相当重要的研究领域。过去的研究已经证明了确保自动驾驶汽车,实际上是未来机动性的成就。传感器融合技术使无人驾驶汽车成为可能,该技术融合了包括摄像头和雷达在内的传感器。相机具有最佳分辨率。但是,在极端天气或夜间条件下,它们的感知能力可能会受到影响。雷达不受这些条件的影响,但与雷达相比,分辨率不足。大多数汽车雷达是调频连续波(FMCW)雷达,其距离分辨率取决于FMCW线性调频脉冲的带宽,而空间分辨率则取决于接收天线的数量。具有更多数量的接收天线元件将改善角分辨率。代替增加物理接收天线,可以通过添加发射天线来生成虚拟接收天线,这通常被称为多输入多输出(MIMO)技术。 MIMO需要多个发射天线中的正交信号。商用汽车雷达已经在2Tx和4Rx系统中使用时分复用(TDM)和二进制相位调制(BPM)实现了MIMO的功能。尽管提高了角分辨率,但最大明确速度却降低了一半。本文提出了频分复用(FDM)技术来实现正交性。完整的雷达系统已经在MATLAB环境中进行了仿真,这表明在不损害最大明确速度的情况下在汽车雷达中使用FDM的可能性。两个Tx天线具有不同起始频率的调频信号用于创建8个Rx虚拟通道。 FDM通常需要增加模数转换器(ADC)的采样频率。在本文中,选择了两个起始频率,从而消除了对更高采样率的要求。

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