[参考译文] PGA460-Q1：BPF 滤波后的数字表示

你好，Isaac！

我确定转换是正确的。 实际采样值为0x7B、解码值为正123、而最近的邻居为负、这提供了伪影。 我提供的图形是在另一个换能器突发时在仅监听模式下完成的。

我检查了你提供的系列,发现了相同的多个文物从250到320我们。 上部子图是无符号字节解释(红线是128)、下部子图是二进制补码解释。

我们可以看到上面的图系列从上到下穿过了红线。 当发生这种交叉时、下方的图上会出现伪影。

在250us 之前、我们可以看到87.6kHz 以及320us 后的良好波。 在从250到320us 的时间间隔内、频率增长多达4倍、且似乎超出 BPF 频段。 所以、我想您的系列也会受到影响。

绘图代码：

import numpy as np
import matplotlib.pyplot as plt

def processBpfTwoComplement(samples, width):
    sampleValues = samples.copy()
    width = 8 if width == '8bit' else 12
    for i in np.ndindex(sampleValues.shape):
        val = int(sampleValues[i])
        if (val & (1 << (width - 1))) != 0:
            sampleValues[i] = val - (1 << width)
    return sampleValues

samples = np.array([0, 254, 255, 255, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 252, 251, 255, 4, 12, 13, 10, 5, 244, 237, 231, 233, 247, 1, 12, 29, 32, 26, 18, 250, 239, 224, 223, 232, 242, 9, 20, 27, 30, 26, 9, 254, 236, 231, 235, 241, 3, 10, 12, 10, 7, 255, 251, 249, 254, 7, 11, 13, 12, 7, 246, 238, 231, 232, 243, 252, 21, 31, 38, 34, 25, 255, 237, 211, 207, 218, 231, 11, 32, 48, 59, 53, 22, 255, 209, 194, 191, 202, 243, 14, 41, 74, 76, 51, 28, 224, 198, 172, 176, 213, 241, 17, 73, 88, 80, 60, 255, 219, 168, 160, 164, 207, 241, 58, 84, 100, 88, 34, 252, 180, 158, 150, 175, 204, 29, 66, 108, 108, 67, 32, 206, 172, 150, 150, 172, 245, 34, 101, 115, 98, 69, 242, 199, 165, 137, 146, 205, 249, 79, 107, 118, 100, 28, 237, 193, 137, 132, 170, 207, 44, 84, 126, 122, 69, 26, 233, 153, 132, 142, 170, 255, 48, 118, 130, 103, 68, 23, 184, 149, 126, 140, 210, 2, 93, 121, 126, 103, 67, 227, 181, 125, 123, 170, 211, 54, 96, 135, 128, 104, 21, 225, 143, 123, 137, 169, 5, 56, 98, 138, 130, 68, 20, 177, 141, 119, 136, 211, 5, 57, 129, 139, 107, 68, 225, 176, 119, 117, 166, 210, 5, 101, 130, 133, 108, 19, 221, 137, 118, 144, 181, 227, 65, 97, 113, 99, 36, 252, 182, 164, 171, 192, 221, 30, 58, 83, 74, 32, 3, 207, 190, 187, 204, 227, 24, 46, 69, 64, 25, 0, 211, 197, 193, 207, 230, 25, 46, 67, 64, 27, 1, 209, 194, 191, 205, 228, 25, 47, 69, 67, 32, 4, 209, 192, 186, 197, 218, 20, 46, 75, 75, 44, 14, 212, 191, 177, 186, 204, 8, 39, 80, 85, 62, 35, 223, 195, 167, 171, 186, 243, 23, 79, 92, 81, 59, 245, 210, 164, 159, 167, 217, 0, 69, 91, 96, 79, 13, 230, 169, 155, 155, 192, 228, 51, 82, 106, 97, 42, 0, 182, 158, 148, 171, 200, 25, 63, 109, 110, 71, 34, 205, 171, 143, 151, 173, 246, 35, 101, 115, 97, 68, 238, 196, 142, 138, 149, 211, 0, 83, 109, 115, 95, 18, 227, 155, 138, 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118, 41, 248, 200, 137, 127, 160, 196, 30, 75, 127, 129, 80, 34, 241, 158, 135, 140, 165, 248, 41, 114, 128, 105, 69, 22, 186, 153, 132, 146, 213, 3, 90, 117, 121, 98, 59, 220, 178, 133, 134, 182, 225, 63, 99, 126, 114, 87, 253, 207, 142, 132, 160, 196, 34, 75, 122, 121, 104, 29, 237, 159, 139, 145, 171, 2, 48, 112, 123, 117, 60, 15, 182, 151, 135, 152, 228, 19, 96, 116, 121, 83, 45, 209, 171, 135, 140, 198, 243, 74, 104, 119, 102, 73, 240, 196, 139, 134, 173, 214, 48, 85, 109, 113, 92, 16, 226, 154, 137, 152, 184, 17, 60, 114, 119, 108, 48, 4, 175, 147, 138, 160, 241, 30, 99, 115, 115, 72, 35, 204, 167, 136, 144, 208, 253, 78, 104, 116, 93, 63, 233, 192, 141, 138, 180, 221, 53, 88, 110, 108, 86, 10, 220, 152, 138, 157, 191, 23, 64, 96, 116, 103, 41, 254, 172, 146, 142, 165, 247, 35, 74, 115, 113, 67, 27, 195, 162, 138, 150, 218, 7, 85, 108, 116, 87, 54, 222, 183, 140, 141, 188, 231, 62, 94, 113, 105, 80, 252, 208, 147, 138, 165, 200, 33, 73, 118, 117, 100, 29, 239, 162, 141, 147, 173, 1, 46, 110, 120, 115, 60, 15, 184, 153, 137, 153, 224, 15, 94, 115, 121, 86, 48, 212, 173, 135, 140, 193, 238, 70, 102, 119, 105, 75, 243, 199, 142, 136, 171, 209, 43, 81, 108, 115, 95, 18, 229, 157, 140, 156, 185, 13, 54, 87, 114, 103, 43, 2, 182, 158, 153, 173, 241, 25, 60, 100, 99, 59, 25, 210, 183, 164, 174, 224, 0, 58, 76, 82, 61, 36, 235, 210, 183, 186, 219, 244, 38, 56, 65, 54, 36, 246, 225, 201, 201, 223, 242, 25, 39, 47, 41, 29, 250, 233, 213, 212, 229, 245, 22, 35, 43, 37, 26, 249, 233, 213, 213, 231, 247, 25, 37, 44, 37, 25, 247, 231, 211, 209, 227, 243, 24, 39, 52, 48, 36, 0, 236, 206, 200, 213, 229, 14, 34, 58, 58, 35, 15, 249, 209, 197, 198, 211, 253, 22, 59, 67, 55, 37, 14, 221, 202, 188, 194, 230, 255, 45, 61, 67, 56, 38, 245, 221, 189, 186, 207, 228, 23, 47, 72, 71, 60, 17, 247, 201, 187, 189, 203, 249, 20, 61, 71, 71, 44, 21, 225, 204, 184, 188, 222, 247, 42, 60, 72, 63, 47, 254, 229, 191, 184, 198, 218, 12, 36, 68, 71, 64, 28, 4, 212, 195, 188, 198, 238, 7, 51, 64, 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5, 1, 250, 247, 246, 247, 253, 1, 8, 10, 11, 8, 5, 253, 249, 245, 246, 251, 254, 6, 9, 10, 8, 5, 254, 250, 246, 246, 249, 253, 4, 7, 10, 9, 7, 1, 253, 247, 246, 249, 252, 3, 7, 10, 9, 8, 1, 254, 247, 245, 247, 249, 2, 6, 12, 12, 11, 5, 0, 247, 245, 244, 246, 254, 3, 11, 13, 13, 7, 3, 249, 245, 242, 244, 252, 1, 10, 13, 15, 11, 7, 252, 248, 242, 242, 248, 252, 7, 11, 14, 12, 8, 255, 250, 243, 243, 246, 250, 5, 9, 14, 13, 11, 3, 253, 245, 243, 244, 247, 1, 5, 12, 13, 12, 5, 0, 247, 244, 243, 246, 255, 4, 12, 13, 11, 8, 3, 249, 245, 242, 244, 251, 1, 10, 13, 13, 10, 6, 252, 247, 241, 242, 247, 252, 8, 12, 15, 14, 11, 1, 252, 243, 241, 244, 248, 3, 8, 14, 14, 9, 4, 254, 245, 242, 242, 245, 255, 4, 13, 15, 13, 9, 5, 250, 246, 242, 243, 250, 255, 8, 11, 12, 10, 7, 253, 249, 244, 244, 248, 252, 5, 9, 12, 12, 6, 2, 253, 246, 245, 247, 249, 1, 5, 10, 10, 6, 3, 0, 249, 247, 247, 249, 0, 3, 9, 10, 9, 6, 255, 252, 249, 246, 247, 252, 255, 5, 7, 7, 6, 1, 254, 251, 248, 249, 252, 254, 4, 6, 7, 7, 4, 2, 255, 250, 250, 251, 252, 0, 2, 5, 5, 3, 2, 0, 253, 252, 252, 253, 0, 1, 3, 3, 3, 3, 2, 255, 254, 253, 253, 254, 255, 1, 1, 1, 0, 0, 0, 0, 0, 0, 255, 255, 0, 1, 2, 1, 1, 1, 0, 0, 255, 255, 255, 255, 255, 255, 0, 0, 1, 1, 1, 1, 1, 255, 255, 254, 255, 0, 1, 2, 2, 2, 2, 1, 255, 254, 252, 252, 253, 254, 1, 2, 4, 5, 4, 3, 1, 253, 252, 251, 251, 254, 0, 4, 5, 6, 5, 4, 255, 252, 248, 248, 250, 252, 2, 5, 8, 8, 7, 3, 0, 251, 249, 249, 251, 255, 1, 6, 7, 7, 5, 2, 252, 250, 248, 249, 253, 0, 5, 6, 7, 6, 4, 0, 254, 250, 250, 252, 254, 2, 4, 5, 5, 4, 0, 254, 251, 251, 252, 254, 1, 3, 5, 5, 4, 2, 0, 254, 253, 252, 253, 255, 1, 4, 4, 2, 1, 255, 252, 251, 252, 253, 0, 2, 4, 4, 3, 2, 1, 255, 254, 253, 254, 255, 0, 2, 3, 2, 1, 254, 253, 252, 253, 254, 1, 2, 4, 4, 3, 2, 255, 254, 253, 253, 253, 0, 2, 4, 5, 3, 1, 253, 251, 250, 251, 252, 1, 3, 6, 7, 5, 3, 254, 251, 250, 250, 251, 255, 1, 6, 6, 5, 4, 254, 251, 249, 248, 249, 254, 2, 7, 9, 8, 6, 0, 253, 249, 248, 249, 252, 255, 5, 7, 9, 7, 1, 254, 248, 247, 247, 250, 253, 4, 7, 10, 9, 4, 1, 251, 248, 247, 248, 250, 1, 4, 9, 9, 6, 3, 251, 248, 246, 247, 249, 0, 4, 9, 10, 8, 5, 255, 252, 247, 247, 248, 252, 0, 7, 9, 9, 7, 0, 252, 246, 245, 246, 251, 255, 6, 9, 10, 8, 3, 255, 249, 248, 249, 250, 253, 3, 6, 8, 8, 3, 0, 250, 248, 248, 250, 252, 2, 5, 8, 8, 5, 2, 253, 250, 248, 249, 251, 1, 4, 8, 8, 4, 1, 251, 248, 247, 249, 254, 2, 5, 9, 9, 6, 3, 253, 250, 248, 248, 252, 255, 3, 8, 9, 7, 4, 253, 250, 246, 246, 251, 254, 2, 8, 9, 9, 7, 1, 254, 249, 248, 249, 251, 254, 4, 7, 7, 6, 1, 254, 250, 249, 250, 252, 1, 3, 6, 7, 7, 4, 1, 252, 250, 249, 250, 255, 1, 4, 7, 6, 2, 0, 251, 250, 250, 252, 0, 3, 5, 7, 7, 4, 2, 253, 251, 249, 250, 254, 0, 5, 7, 7, 5, 2, 253, 250, 248, 248, 252, 255, 5, 7, 8, 7, 6, 0, 253, 249, 248, 249, 252, 2, 5, 8, 7, 6, 1, 255, 250, 249, 250, 252, 0, 3, 6, 6, 6, 3, 2, 253, 252, 251, 251, 255, 1, 4, 5, 5, 2, 0, 253, 251, 252, 253, 0, 1, 4, 4, 4, 3, 1, 254, 253, 252, 253, 255, 1, 4, 5, 4, 2, 0, 252, 251, 251, 252, 254, 0, 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samplesDecoded = processBpfTwoComplement(samples, '8bit')

x = np.arange(samples.size)

fig = plt.figure()
ax1 = fig.add_subplot(211)
ax1.plot(x[210:350], samples[210:350])
ax1.axhline(y=128, color='r', linestyle='--')

ax2 = fig.add_subplot(212, sharex = ax1)
ax2.plot(x[210:350], samplesDecoded[210:350])

你好、Xibei、

感谢您提供信息。 如果使用 DSP、BPF 是第一级、因此很可能不足以消除您在该信号上引用的其他干扰。 如果您看一下 PGA460的方框图、信号仍有额外的处理来消除这种称为伪影的额外噪声。

此致！

艾萨克

你好，Isaac！

我不同意。 我们看到的现象看起来不像噪声。

让我们考虑您提供的系列中的一个半波在249至254的指数范围内采样。 无符号字节值为[48,118,130,103、68、 23]的情况下运行。 当我们将这些值绘制为无符号 int 时、我们可以看到漂亮的半波。

解码的二进制补码值为[48,118、-126,103、68、 23]的情况下运行。 这个值-126看起来不像噪声值,而是伪差。 它太大了、不会产生噪声。 当我们绘制这些值时、 不 采用半波设计！ 这个值似乎等于130。

您说过 BPF 是信号处理的第一个阶段。 下一个阶段是整流器。 在本例中、实际的整流器输出是：[48,118,130,103、68、 23]或[48,118,126,103、68、 23]？ 我觉得126输出是错误的。 是的、将移除伪影、但整流器的输出将被拧紧。

我认为真实数据可以使用第一个导数分析来恢复。 0th 值保持不变、按如下方式处理所有其他项。

假设所有样本都是无符号字节、我们计算差值的两种变体。 第一个是第 i 个样本和第 i 个样本(I-1)次采样之间的简单差值、第二个是第 i 个样本减去(I-1)第 h 个样本减去256个样本。 现在、我们选择绝对值较小的差值。 恢复的第 i 个样本将是(I-1)第次采样和所选差值之和。 还原代码如下所示。

def restore(samples, width):
    width = 8 if width == '8bit' else 12
    restored = np.copy(samples)
    for i in range(1, restored.size):
        diff1 = samples[i] - restored[i-1]
        diff2 = samples[i] - (1 << width) - restored[i-1]
        absDiffList = [np.abs(diff1), np.abs(diff2)]
        diff = [diff1, diff2][absDiffList.index(min(absDiffList))]
        restored[i] = restored[i - 1] + diff
    return restored

您可以比较下方子图上的蓝色和橙色曲线。 得到最大峰值为-139和139的良好波。

你好、Xibei、

感谢您提供信息。 我看到你在这里的意思,我是误解你的意思是神器。 使用带符号的二进制补码可能不是数据的正确转换。 但是、如果不放大数据、对有符号的二补码实现似乎是有意义的。 您能否在下方用橙色显示您的实施方案的整个数据集？

我正在与数字设计团队核实不同阶段输出何种数据、以确保我们进行了适当计算。 我会在他们提供更多信息后回复您。

此致！

艾萨克

你好，Isaac！

我已经在您提供的 Excel 工作表中添加了新列"已恢复"。 此列实现了我提供的算法。 二进制补码和恢复的序列绘制在同一图形上。

PGA460直接数据突发原始 data.xlsx

你好、Xibei、

感谢您提供的信息！ 我还在等待数字设计团队的回复。 可能需要一些时间、因为熟悉此设计的设计师不在办公室。

此致！

艾萨克