搜索资源列表
AnadaptiveKalmanfilterfordynamicharmonicstateestim
- Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive
mypaper4
- To fully utilize the character of the bioradar echo signal, autocorrelation analysis, spectral estimation methods and time-frequency analysis are presented in this paper. These methods are employed to deal with no one, single people and multi-people
SPL_2012_noise
- This a demo program for the paper J. Tian and L. Chen, "Image noise estimation using a variation-adaptive evolutionary approach," IEEE Signal Processing Letters, Vol. 19, No. 7, Jul. 2012, pp. 395-398.- This is a demo program for the paper
253474
- 在拉普拉斯白噪声中估计正弦信号的频率，英文版本，详细的推导过程。-Estimation of the Frequency of Sinusoidal Signals in Laplace Noise，English version, the detailed derivation.
02.estimation
- Estimation of the frequency of a sinusoid in white Gaussian noise-Estimation of the frequency of a sinusoid in white Gaussian noise
spl15689_2column.pdf
- Cram ́ er-Rao bound (CRB) has been formulated in earlier work for linear, planar and 3-D array configurations. The formulations developed in prior work, make use of the standard spatial data model. In this paper, the existence of CRB for th
estimation-extended-Kalman-filter
- 针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题，提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点，采用可调整的算法模型对粒子群算法进行改进，将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理，将优化后的卡尔曼滤波器应用于感应电机转速估计。- Extended K
battery-SOC-estimation-based-on-EKF
- 基于扩张卡尔曼滤波的磷酸铁锂蓄电池SOC检测，给出了电池模型和算法实现过程。-The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the model due to the nonlinear behav
SC_FDE
- 频域均衡算法的总结，对经过时间捕获的导的时域信号进行信道估计之后，得到其信道频域响应，进而消去每一帧信号的噪声干扰还原的到原始信号。-Frequency domain equalization algorithm, the time-trapped time domain signal channel estimation, the channel frequency domain response, and then eliminate the noise of each frame of t