时频谱分析可以提供信号在时间域和频率域的联合分布信息，针对该方法很难同时保证较高的时间分辨率和频率分辨率的问题，提出了一种时频聚集性很高的谱融合方法。先用谐波小波包将信号分解到不同频段，再用高时间分辨率的Morlet小波和高频率分辨率的短时傅里叶变换分别对各个频段上的分量进行分析，得到小波尺度矩阵和短时傅里叶变换时频矩阵，然后通过算法将二者融合在一个时频谱中。通过仿真和对动态条件下加速度计信号的分析，证明该算法既能提取出微弱的动态变化，又具有较高的时频分辨率，可以直观、全面、精确地对信号进行识别。

Time-frequency spectrum analysis can provide the signal features in time and frequency domains.However, the method is difficult to make a good tradeoff between high time-resolution and high frequency-resolution in the process of signal identification.Therefore, a new algorithm is proposed, which has a good time-frequency concentration.Firstly, the signal is decomposed into different frequency bands by harmonic wavelet packet.Then, Morlet wavelets with high time-resolution, and short-time Fourier transform with high frequency-resolution, are used to analyze the time-frequency distribution of each frequency band.Therefore, Morlet wavelets scale matrix and short-time Fourier time-frequency matrix are obtained.At last, these two matrices are fused into a new matrix with a specific algorithm.Analysis is made to accelerometer dynamic signal under simulation and dynamic condition.The result shows that the fusion algorithm can not only extract the weak dynamic change, but also has high time-frequency resolution, which is an intuitive, comprehensive, and accurate signal identification algorithm.