通信信号分析与处理智慧树知到期末考试答案章节答案2024年哈尔滨工业大学

通信信号分析与处理智慧树知到期末考试答案+章节答案2024年哈尔滨工业大学古典概率具有有限性和等可能性。（）

答案:对在大多数情形下把大量重复试验所得到的频率作为随机事件的概率的近似值是合理的。（）

答案:对Z(t)=X(t)Y(t)，因为X(t)和Y(t)相互独立，所以(z;t)=(x;t)(y;t)。（）

答案:错随机过程的相关时间越小则其变化越缓慢，随机过程越接近宽平稳过程。（）

答案:错希尔伯特变换相当于一个90度的移相器，它对所有频率分量的幅度响应都是1，对所有正频率分量（包括零频率分量）移相90度。（）

答案:错宽平稳随机过程不一定是严平稳随机过程。（）

答案:对随机变量有时间和试验结果两个参数，当时间固定，试验结果可变量时，随机过程是一个随机变量。（）

答案:对理想白噪声过程随时间的起伏极快。（）

答案:对相关时间越小，这说明随机过程随时间变化越剧烈。（）

答案:对随机变量有时间和试验结果两个参数，当时间和试验结果固定时，随机过程是一个确定的时间函数。（）

答案:错平稳高斯随机过程与确定信号之和仍为平稳高斯随机过程。（）

答案:错功率谱密度为非负函数。（）

答案:对样本空间的完备性是指样本空间必须包含随机试验的所有可能的基本空间。（）

答案:错窄带随机过程的莱斯表示中同向分量和正交分量都是实随机过程。（）

答案:对窄带随机过程的莱斯表示中同向分量和正交分量的数学期望为零。（）

答案:错两个随机变量不相关，那么这两个随机变量一定相互独立。（）

答案:对信号两次希尔伯特变换后信号不会发生改变。（）

答案:错实平稳随机过程的自相关函数等于其希尔伯特变换的自相关函数。（）

答案:对如果试验次数足够多,那么频率具有稳定性,且趋近于事件概率。（）

答案:对系统等效噪声带宽的等效原则是（）。

答案:理想系统与实际系统在同一白噪声激励下，两个系统输出平均功率相等。###理想系统的增益等于实际系统的最大增益。功率谱密度为Sx(w)的平稳随机过程X(t)，通过传递函数为H(w)的线性系统后输出为随机过程Y(t)，功率谱密度为S(w)，那么下列公式错误的是（）

答案:data:image/png;base64,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###data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMYAAAAeCAYAAAB39mEfAAAHq0lEQVR4Ae2Y61EjSwxGSYEYSIEcCGFjIAUyIAMyIAIi2ARIgAzIwVvHdY9LV6gfNtMu/xhVzY6n1a3HJ32aYe8Ou+wI7Aj8QODux8q+sCOwI3DYibE3wY5AgUBJjM/Pz8Pr6+vhz58/h6enp+Od54+Pj8LEeUvYwBY+tpLv7++jzbe3t61MTtlZiRMBkA9Ykd9WMov/+/v70fffv3+Hrmf3zvoeOrxgw7k98j9ifH19HYlwd3d3aF0zQLXippGwC9m2lpeXl6Pta5BjNU5gQx5gBTG2FGK/v78/PDw8dAlHjWb9z+xdWftZfM7pkRMxYBSAAQZ3jMBwiMBEeH5+Pq7/hhgUA/sUZ4Vgn9hX2Sfma+BE/OBEPiuEemKfGrdkptk9O7N3de2NZXSf7ZETMWQTjdV6dbN+adMx+SjG1hMwAgGR8cEn4CpZjRNx22i/GUKj/B8fH49YtT5pjWGmXqO916j9KF/1sz1yIobJ9aaIxi+5QziatkW6S2xWZ1ZPptU4+clB464U3xp8CVRinlsQ41q1r/Ko1mZ65AcxWkBVDmbXZGm2Dehc1dSCQOorP0xT9Hmq+m2+iuA2TM6livGSNd9INK7CW1osqsECfujjGc9yZx19ftv3GtY8OTeS3t5W7Uc21Rs7dirxPyhybuwVt4zLTI+ciGFBmOq52aqAzlmjibCbk+OTh/WqiQW0FY/FyEkDBmeYCitkJU7E6zTLBLCJM4aciTFVOYMHV24e65IxxIb4/pYY+qjirmLNazZx9QYFI3Or4mQNfR5iMz1yIgZOBF9juTg56NnnVrEpCL6qJhZQ9BVxWOeqYvT7OTcC8VLw2atqmJU4WbCqCcQjF5mcxBc8cgMy5FivbDp8KptbEcPYqjrN9I+YkEOup/3Tys8cMib47fUI+hMxeOCV7AGcQRRYVyUlG6u3izoTwVbV/L2k2S+ouagWm8Qr8U1UxUYssxd5VHIOTuZY5W8eNmZ+jr5tgmwn2ievPESsRZULeXCmwtGmwh+/e5cDtfKB/RxzzGvmN+exkweVw0J97lPOcFXS6xH2l6dI0GQxzO8cFIcFLTvmjCBZ7Ap8zknEaN+CUWT1kowzfjrwmq0E3zGGas8Wa7M4iUHMEf8Zv17cFJ2cuCIWfmpgGx02o4gfmFaizayTGOpn7tZcW+bdqr37Rnfr7QBxP31Jfuojvr4NW/9D2cMa+yUxUFCIXPgcmMUAAEWHPo/AcX+0bbFJTn1M2mLHBtEfd89wXy0zOBGD0814jNFn7q614rZZIxZOPuJQLy6SKZMl+rTh4xq/tUXc1LB3WY8c96j2kBU/1RWJrJ2YRxye6mMPVWSJOY6wbhJDI4Ar+ICYv9cINgbEngiQQZN8JSYI+xWA9lm9zJ8p9ihp/Wx5H+GEL7ABKxqX37HB0Y/idmBkLHxWzx1xasb6HBXhH+LgyiIxYi3zHp9be0e1V28M8Y4uCv2AXsLY+D6jt2c4J1kdEtEWv0dY/0QkW/jvGQIQWG5w3xoEwO/Iao6afD4X3WibJG18i82+mLTFBpiW9JJGN3vl4rT8xXVzqfIVK3SVvhc3PiSUDSAWEiEPEZsnD7MYr80Y1/hNfOiIaSStvTO1H9lWD7mJx1zj8GQP/YI+9hB7WjLCepoYGqoKSjOg556noMWqzhm0BSRpmyfaERRs+btXbGMVRP1wtxFm7tg5V/TdytcmqiYZ8RJXz6+TMGIRbVEDiSNJGTYtwZ/74x7j7MXi/tbemdprY3R3CEAAB0R8E9o3sYdmhmfVI8QyTQybtyq4QVGISlrguzcmbePHYmqfIsXCez7fLVQ18VmbvWLDZR+t5x5OkTQVVk7Y+LbMfrRPQbGRp6L4iVnPlv6qmorhb4hB7KPa5/xaz35JQGJzi8NRshC3GEDMlpgfGFRyIgYAAkJsSA9wmIB606ync8r1Gk37FDsXSlBMpldsYtZWlYs5XXq/FCcLR1H9nZvOPCvSGK9TmD0V5g4ZsWpNROyhw0Y1WT2fYzSOeO/tnal9tNX7DfbEa+55r76Ihx7oyahHTsQwORzjgGcug3C91WzoWyDGKdcK1qSxUxXTpNHTXC2xcdi/Qi7FCRw5q4AVueRhYZ69aWdROZ/3SS50lX39cxfzOHnVm2erpu7j3ts7U/toq/dbIpMXsWfRF/r4mZX3zfTIiRg4jSQQWO6sA1CLFDhmXwvEmUB8PWInNwv2bST0vTgEpyJXBuiS50twMvacF7lEshCPxSePlvipQF0qsVF7w0ECtSarNlo1jX57e2dqH231fvumBbdqOPppiL4iu7ZneuREDA8BGA688kRyX74TTA9EwcPuKiF2Cs3VI88W/mdxkhQVNg6DON2ulUMvri3wiTauUfvor/d7Ft8fxOgZ/Y1ONgPSKrlmsVflgN3Vecw2x1Y5XqP2s7HOYns1YhC4nwC919xsgnnflq/sbPvazzQun0G8+fLn1xax9P622MJ+ZWNl7St/1do5PXJVYqwqeLQ7++lXAXdLa+QBMXp/J1wSb/X5domdc8/EGq0g+yie6H+mR65KjFHwu35H4FYQ2IlxK5XY47gpBHZi3FQ59mBuBYGdGLdSiT2Om0JgJ8ZNlWMP5lYQ+AciQFIOKjz0HAAAAABJRU5ErkJggg==###data:image/png;base64,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推导随机过程功率谱密度的包含（）步骤

答案:帕斯瓦尔公式###截短###求平均###求极限关于期望为零的平稳窄带随机过程的莱斯表示中同向分量和正交分量说法错误的是（）。

答案:同向分量和正交分量正交。###同向分量和正交分量的方差不相等。对于随机变量X和Y，正确的是（）

答案:对于任意常数C,有D[C]=0###对于任意常数C,有E[C]=C###对于任意常数c和b，有E[cX+bY]=cE[X]+bE[Y]古典概率具有（）性质。

答案:有限性###等可能性关于期望为零的平稳窄带随机过程的莱斯表示中同向分量和正交分量频域数字特征说法正确的是（）。

答案:同向分量和正交分量的功率谱仅包含低频且是限带。###同向分量和正交分量的互功率谱不一定为零。平稳随机过程X(t)通过单位冲激响应为h(t)的线性系统后输出为随机过程Y(t)，那么下列公式正确的是（）

答案:data:image/png;base64,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###data:image/png;base64,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###data:image/png;base64,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关于期望为零的平稳窄带随机过程的莱斯表示中同向分量和正交分量说法正确的是（）

答案:同向分量和正交分量都是平稳随机过程，且联合平稳。###同向分量和正交分量的数学期望相等。对随机试验而言,样本空间给出它的所有可能的试验结果,事件域给出了由这些可能结果组成的各种各样的事件,而概率给出每一事件发生的概率,则（样本空间，事件域，概率）称为（）。

答案:概率空间平稳随机过程X(t)通过单位冲激响应为h(t)的线性系统后输出为随机过程Y(t)，那么下列公式错误的是（）。

答案:data:image/png;base64,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当样本数N

趋近于无穷时，估计量的方差趋于零，则称该估计量为（）。

答案:一致估计量平稳随机过程的时间均值是（）。

答案:随机变量一般随机过程的积分是（）。

答案:随机变量窄带高斯随机过程包络的一维概率密度是（）。

答案:瑞利分布随机变量的数学期望是（）。

答案:常数两个联合平稳随机过程互谱密度是（）。

答案:实部是偶函数确定性信号的带宽是根据信号的频谱确定，随机信号的带宽是根据随机信号的（）来确定。

答案:功率谱密度实平稳随机过程的希尔伯特变换是（）。

答案:实平稳随机过程对于随机变量X和Y，错误的是（）。

答案:E[XY]=E[X]E[Y]下列说法错误的是（）。

答案:渐进无偏估计不可能好于无偏估计。随机事件是样本空间的子集。（）

答案:对对于随机信号可以直接使用傅里叶变换研究其频域特性。（）

答案:错对随机试验而言,样本空间给出它的所有可能的试验结果,事件域给出了由这些可能结果组成的各种各样的事件,而概率给出每一事件发生的概率,则（样本空间，事件域，概率）称为概率空间。（）

答案:对希尔伯特变换是一全通滤波器，只改变信号的相位，不会改变信号的能量和功率。（）

答案:对对随机过程进行时间的采样，得到一串随机变量所构成的序列，即随机序列。（）

答案:对概率是衡量一个事件发生的可能性大小的数量指标，其值介于0和1之间。（）

答案:对复信号没有负频谱。（）

答案:错实平稳随机过程与其希尔伯特变换的互相关函数是奇函数。（）

答案:对实平稳随机过程与其希尔伯特变换相互正交。（）

答案:错样本空间的可加性是指样本空间中的任意两个基本事件可以相加，不影响事件的概率。（）

答案:错窄带随机过程的莱斯表示中同向分量和正交分量的功率和原窄带随机过程相同。（）

答案:对如果随机过程的功率谱密度具有带通形式，那么该随机过程就是窄带随机过程。（）

答案:错条件概率是概率的一种特殊情况，所以相应于概率对应的概率空间，条件概率没有对应的条件概率空间。（）

答案:错若系统输入非高斯随机过程的功率谱带宽远远小于线性系统带宽，则线性系统输出随机过程的概率分布趋于高斯分布。（）

答案:错一个高斯随机过程经过任意线性变换后仍然为高斯随机过程，其数字特征保持不变。（）

答案:错理想白噪声在任何两个相邻时刻(不管这两个时刻多么邻近)的取值都是不相关的。（）

答案:对希尔伯特变换相当于一个正交滤波器。（）

答案:对若线性系统输入随机过程是宽平稳的随机过程,则系统输出随机过程也是宽平稳的随机过程,且输入与输出随机过程联合宽平稳。（）

答案:对在对实际信号进行分析时，利用随机过程的遍历性，用时间平均来代替随机变量的统计平均，即用任何一个样本函数的统计特性即可获取整个随机过程的数学期望、自相关、均方值等数字特征。（）

答案:对随机过程的相关时间越小则其变化越剧烈，随机过程越接近严平稳过程。（）

答案:错由于随机过程是有一簇确定时间函数组成的，所以随机过程也同样具有采样定理。（）

答案:错如果一个线性时不变系统对任意有界输入其响应有界，那么称此系统是因果的。（）

答案:错样本空间具有完备性和可加性。（）

答案:错遍历随机过程一定是平稳随机过程,但反之不一定成立。（）

答案:对古典概率具有无限性和等可能性。（）

答案:错关于期望为零的平稳窄带随机过程的莱斯表示中同向分量和正交分量频域数字特征说法错误的是（）

答案:同向分量和正交分量的功率谱为零。###同向分量和正交分量的互功率谱为零。下面关于高斯分布正确的是（）

答案:窄带高斯随机过程的同向分量和正交分量的一维概率密度是服从高斯分布###窄带高斯随机过程的希尔伯特变换是服从高斯分布下列关于平稳随机过程自相关函数曲面描述正确的是（）。

答案:不能出现平顶。###不能在幅度上有任何不连续。###不能为任意形状。###不能出现垂直边。白噪声通过理想低通线性系统后说法正确的是（）。

答案:输出功率谱宽度与系统带宽有关。###输出的相关时间与系统带宽成反比。###系统带宽越宽，系统输出的随机过程随时间变化越剧烈。###输出平均功率与系统带宽成正比。平稳随机过程X(t)通过单位冲激响应为h(t)的线性系统后输出为随机过程Y(t)，那么下列公式错误的是（）

答案:data:image/png;base64,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###data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAAcCAYAAABBGgLYAAAJo0lEQVR4Ae2ZjZHVOgxGtwVqoAV6oARqoAU6oAM6oAIqoAEaoAN62DfnzhxGq5EcJ/Hdn0c8E+zYsvTpk+R4Lw+PV7sYuBj45xh4+Oc8vhx+FgZ+/vz5+PXr18fv378/i73nMPL79++bT/j11tut8HEkP9++fXskeDj73O3Xr183PD9+/Nht+s+fP7e94H/pljnl/V68nvUbrsEH9zMNP5DvGmsPDw+PHz9+7ESm5vfiikr35pG2upynHvCJ50zbi+uoLWNEbuR280Bnuv7Tp0+P1easbMU7dt6/f//47t27wza/fPlyC85LF3/Hp/MreUUXeknOI41kh3O4n4k1Bf3hw4fW1IrCp0Dw6czhAUZ0zBxoYu78X1H4K/K7JT0tcJDhO7mR25PC//z58+0UhwCKR9LYzLgjJCs98w4G7J29Inp4dKf3GYyze/GD5968wpV2ZrFVcuoh9luNYhwVpEU0ktmyQQzx60wM2YuO0SElDjH7nvsVhb8qvzO27l17HAKxPSn86mthMkDevb+gEnsmWXRudNopc+8eznjuySuHMV/qMzekyIOHffWFJC7kADYZ8zBmLsfMIsrz0dZo7H76s210A2SNhwNCm9gj78EeDx3zk5geae4/yskRm+CvbnKbhY8xk6G6MhwB0+3xuppPp05+a37FF2PLxmh9VPjsW8ErRYedma/0CKtrHvR8KWLjIBAv9uBWfnlnLRaJRXQ0yUlW9K64ZYJLzNEnxuBjzXUPUd9jLlq4rB1pq/N7FoNffWJrmyp8yTkaRI2NegIs2VmOwAGaZBo90TF0rC6KjGvrHX94qi8+e1fwajHGohMXdkd8uZb3bhWdvOIb46qhm3VzBhscTrxTAOzritrbWj58sMMBxF6xd33mfKvokNdv7FbYkDGmYEEGLPjEA5bMpdyM8luZrR4dcNP5HOejLjgDN7limyp8k2vVV0XjsfdLk21ArGRv9SaZegkCe/gyvUQTb05CsZzlVf9iQNVtEoth1Gd81RdCvSQffJrsjJnLjSTEJnLGNmMAd7VX+/Fri36LN+up3rEfmxiqw0TdcEb+oa/6UycWPusVx8xVe7Wf8ztiHI0t3srXai7r8nbmwbRZ+AJGeU6QrPzMexdsAkjycABgP5If55ivCLe4dDhiJAgm8FZ/JGAGpOJtBa/qqLCZxBQP9uUXPniPTy6+0RcXnvALPj14mMvNwjfheCc+PK6hhxjm5p6MCzv4gd/gjx+F6A/jHG90YQ/duYkHvTRk4C/bR68xRYaDSFvs9SBgPjf5z4dZluvesYNNsDLmMbfR7Zx91kOOgF0fnxR+LCQACrYKkAmQDfDOGiDdr7Eo634DlN+VzeQzL/k4udX8SlSy2lTfqEd2b1PfHl7Byb7KHjyyRvBpOWEjvsybspXeuI+xX5csi06SLR406GWuswfeqrjNjWwD++whf3LLNuQK+ZlmYWZZsOCDDb3I5g9JtFcVt/Gp8OAn8+a7tmb77Dv71Gk+jHR5SCr7pPABlh9PtazU015FrksOPQ1wOYgSpAxy2lXPqFc27u/kwYd8xtnJr5wXZ9V3vGK/4od59JCkNgO/hwf2zDQxV7IxCeM4ysp7jr0y3iqwExu+MDeDU9msI+qL4xFf2Y/8jp5orypg9shbjonzEc/Zsf7M5LbY5fVJ4XPqsaBCwMbTPQP11I7z7neuOiDQm8HuIUbZTK42Y28CZntR5l5jce7lFTzwGIsGruO7MtjYwwN6Z5rYZ2QrGXnv7JmI2InN+W5fJZt