文章根据移动应用需求,推导三维空间最小二乘(least square,LS)算法、Taylor级数展开法和查恩(Chan)算法3种经典到达时间差(time difference of arrival,TDOA)算法求解过程,通过仿真模拟分析3种算法的不同特点,确定移动定位场景下的最...文章根据移动应用需求,推导三维空间最小二乘(least square,LS)算法、Taylor级数展开法和查恩(Chan)算法3种经典到达时间差(time difference of arrival,TDOA)算法求解过程,通过仿真模拟分析3种算法的不同特点,确定移动定位场景下的最佳算法。为了进一步提高定位精度,采用Kalman滤波中递推估计思想,减小噪声干扰产生的误差,提升到达时间(time of arrival,TOA)测距精度,进而获得三维空间中性能优良的TDOA算法。测试试验表明,改进后的Chan算法有效且性能优良,定位误差最大为10~30cm。展开更多
Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation...Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.展开更多
文摘文章根据移动应用需求,推导三维空间最小二乘(least square,LS)算法、Taylor级数展开法和查恩(Chan)算法3种经典到达时间差(time difference of arrival,TDOA)算法求解过程,通过仿真模拟分析3种算法的不同特点,确定移动定位场景下的最佳算法。为了进一步提高定位精度,采用Kalman滤波中递推估计思想,减小噪声干扰产生的误差,提升到达时间(time of arrival,TOA)测距精度,进而获得三维空间中性能优良的TDOA算法。测试试验表明,改进后的Chan算法有效且性能优良,定位误差最大为10~30cm。
文摘Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.
