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    Liu Jun-Qun
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    • Directivity is one of the core performance parameters for an antenna, and its accurate computation and measurement have been receiving attention in the past decades. While quite a number of computational methods for antenna directivity are developed, a comprehensive comparison among these methods, including their advantages and disadvantages, has not yet been reported. In the literature, most of these methods can be roughly classified as two categories, i.e. numerical methods and analytical methods. The numerical methods include those simplified numerical methods for accessing the radiation pattern. For example, some methods assume that the radiation pattern is only elevation-angle dependent, and an approximate truncation of the integral interval is often employed. The analytical methods are developed, where the electromagnetic radiation intensity and the total radiation power for a specific antenna are derived analytically. Nevertheless, so far there have been less efforts dedicated to developing general computational methods based on the antenna's planar near-field theory and associated computational schemes, although indeed quite a few of advanced mathematical approaches have been used to obtain the accurate directivity, including the series expansion based on the spherical wave scheme and the Fourier expansion scheme. In spite of these efforts, one can see that a general accurate numerical scheme for the antenna directivity is still urgently required. As an important supplement to these efforts, this paper adopts or proposes four numerical integration methods regarding planar near-field measurement of the antennas under test. Based on the planar near-field theory, microwave near-field data, and fast Fourier transform (FFT), this paper discusses the numerical integration-based algorithm schemes based on the simulated (or measured) near-field data and relevant numerical uncertainty estimation. Specifically, an antenna with accurate directivity is chosen for illustrating the accuracy assessment. First, a set of near-field data under the well-established probing condition is simulated and then used as an input for subsequent numerical calculation of the antenna directivity value. Four different numerical integration methods are employed and their possible numerical errors are discussed. Consequently, the antenna directivity values are obtained respectively from the four different numerical methods. It is revealed that the numerically evaluated directivity values from the near-field data are roughly at most 0.6 dB larger than the accurate value. This work represents a substantial step toward a reliable estimation of the antenna directivity from the near-field data of an antenna.
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      • 方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (20)式理论真值Dpa 40.9512 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为40.9512, INVuv误差界1.247607 × 10–8; 积分真值区间: [40.95119, 40.95129], 方法4)计算结果与被测天线理论真值近乎完全吻合, 就积分本身结果而言, 方法4)可作为积分真值参考基准 21.51 解析解, 公式应用范围
        受限
        1) PNF 41.13 比真值偏大约0.18 dB 1.11 速度最快
        2) 本文算法基础积分求和估算 40.9522 比真值偏大约0.001 dB 20.56 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 40.9522 比真值偏大约0.001 dB, INVuv误差界1.247085 × 10–8, 积分真值区间: [40.9521, 40.9523] 211.78 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数
        解析值
        40.95218 比真值偏大约0.001 dB, quad2d()算法本身误差, 不存在被积函数值误差, INVuv误差界9.120334 × 10–9, 积分真值区间: [40.952139, 40.952217] 1895.52 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (21)式理论真值Dpa 13.5284 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为13.5299, INVuv误差界1.248662 × 10–4; 积分真值区间: [13.52897, 13.53091], 方法4)计算结果与被测天线理论真值接近完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准. 24.84 解析解, 公式应用范围
        受限
        1) PNF 13.73 比真值大约0.2 dB 1.03 速度最快
        2) 本文算法基础积分求和估算 13.8459 比真值偏大约0.33 dB 20.34 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 13.8598 比真值偏大约0.33 dB, INVuv误差界1.249746 × 10–3, 真值区间: [13.8493, 13.8703] 310.81 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数解析值 13.8184 比真值偏大约0.29 dB, quad2d()算法本身误差, INVuv误差界3.546120 × 10–4, 真值区间: [13.8154, 13.8213] 1611.23 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (20)式理论真值Dpa 38.9473 0; 应用方法(4)对被测天线阵列本身进行数值积分, 结果为38.9474, INVuv误差界2.257101 × 10–8; 积分真值区间: [38.94733, 38.94746], 方法4)计算结果与被测天线理论真值近乎完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准 20.92 解析解, 公式应用范围
        受限
        1) PNF 39.33 比真值偏大约0.38 dB 1.12 速度最快
        2) 本文算法基础积分求和估算 39.2175 比真值偏大约0.27 dB 20.92 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 39.2142 比真值偏大约0.27 dB, INVuv误差界1.248032 × 10–7, 积分真值区间: [39.21385, 39.21457] 201.61 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数解析值 39.2023 比真值偏大约0.25 dB, quad2d()算法本身误差, INVuv误差界2. 873319 × 10–8, 积分真值区间: [39.20225, 39.20241] 1276.35 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (20)式理论真值Dpa 37.8093 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为37.809329, INVuv误差界6.831371 × 10–10; 积分真值区间: [37.809327, 37.809330], 方法4)计算结果与被测天线理论真值近乎完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准 21.47 解析解, 公式应用范围
        受限
        1) PNF 38.40 比真值偏大约0.59 dB 1.04 速度最快
        2) 本文算法基础积分求和估算 38.1953 比真值偏大约0.39 dB 20.65 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 38.2152 比真值偏大约0.4 dB, INVuv误差界1.249154 × 10–8, 真值区间: [38.21517, 38.21523] 189.59 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数解析值 38.190513 比真值偏大约0.38 dB, quad2d()算法本身误差, INVuv误差界1.248422 × 10–9, 真值区间: [38.19051, 38.190516] 1465.8 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (20)式理论真值Dpa 37.8093 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为37.809329, INVuv误差界6.831371 × 10–10; 积分真值区间: [37.809327, 37.809330], 方法4)计算结果与被测天线理论真值近乎完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准. 21.18 解析解, 公式应用范围
        受限
        1) PNF 38.36 比真值偏大0.56 dB, 结果因FFT点数变化与表3相比略有变化 1.09 速度最快, 本例FFT点数为211× 211, 其他算例FFT点数都为210× 210
        2) 本文算法基础积分求和估算 38.1533 比真值偏大0.34 dB 82.18 计算时间加长, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 38.1535 比真值偏大0.34 dB, INVuv误差界1.805049 × 10–8, 真值区间: [38.153468, 38.153549] 900.46 计算时间长, 计算时间依赖于FFT点数和测试数据矩阵大小, 实用性减弱
        4) 累加求和被积函数解析值 38.13822 比真值偏大0.33 dB, quad2d()算法本身误差, INVuv误差界1.247355 × 10–9, 真值区间: [38.138217, 38.138222], 结果因FFT点数变化造成最大值略有变化, 最终结果与表3相比略有变化 1468.83 速度较慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (21)式理论真值Dpa 13.3604 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为13.3604, INVuv误差界3.473624 × 10–5; 积分真值区间: [13.3602, 13.3607], 方法4)计算结果与被测天线理论真值近乎完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准. 25.09 解析解, 公式应用范围
        受限
        1) PNF 13.63 比真值大约0.27 dB 1.03 速度最快
        2) 本文算法基础积分求和估算 13.6866 比真值偏大约0.33 dB 20.25 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 13.6806 比真值偏大约0.32 dB, INVuv误差界7.892290 × 10–4, 真值区间: [13.6742, 13.6870] 279.98 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数解析值 13.6580 比真值偏大约0.30 dB, quad2d()算法本身误差, INVuv误差界1. 382844 × 10–4, 真值区间: [13.6569, 13.6592] 1259.79 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV

        方法 方向系数计算值/dB 误差分析、估计 计算时间/s 方法特点和局限
        (21)式理论真值Dpa 12.6206 0; 应用方法4)对被测天线阵列本身进行数值积分, 结果为12.6213, INVuv误差界1.249209 × 10–4; 积分真值区间: [12.6205, 12.6220], 方法4)计算结果与被测天线理论真值近乎完全符合, 就积分本身结果而言, 方法4)可作为积分真值参考基准 24.84 解析解, 公式应用范围
        受限
        1) PNF 12.90 比真值偏大约0.28 dB 1 速度最快
        2) 本文算法基础积分求和估算 13.0038 比真值偏大约0.38 dB 20.40 速度较快, 计算时间依赖于FFT点数和测试数据矩阵大小
        3) 二维插值估计被积函数 13.0030 比真值偏大约0.38 dB, INVuv误差界1.249323 × 10–3, 真值区间: [12.9943, 13.0116] 297.41 速度较慢, 计算时间依赖于FFT点数和测试数据矩阵大小
        4) 累加求和被积函数解析值 12.9737 比真值偏大约0.35 dB, quad2d()算法本身误差, INVuv误差界2.116856 × 10–4, 真值区间: [12.9722, 12.9751] 1561.10 速度最慢, 计算时间依赖于近场测试数据矩阵大小, 本例为104 × 156, 最佳逼近积分真值
        DownLoad: CSV
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      • Abstract views:9037
      • PDF Downloads:80
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      Publishing process
      • Received Date:21 August 2019
      • Accepted Date:27 October 2019
      • Published Online:20 January 2020

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