ID	原文	译文
21635	呼吸率均值误差为0.41±1.49次/min，与标准值之间的相关性为0.9971。	It is exhibited as well that the mean error respiration rate is 0.41±1.49 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9971 to the standard values.
21636	实验表明，研制的系统可在零负荷的状态下无感进行生理参数测量，在健康医疗领域具有广泛的应用前景。	It is suggested that the developed system can be senselessly used under zero load and is promising in future.
21637	该文针对利用多站到达时差观测量的辐射源目标定位问题，已知目标高度先验信息，提出一种基于加权最小二乘的闭式高精度定位方法。	To solve the problem of radiant target localization using Time Difference Of Arrival (TDOA) measurements from multiple sensors, an algebraic closed-form method based on Weighted Least Squares (WLS) minimizations is proposed, with the priori knowledge of target altitude.
21638	近距离场景下，可忽略地球曲率影响，此时目标高度信息可等效为目标的1维坐标。	In near distance scenario, neglecting the effect of earth curvature, the target altitude can be regarded as one-dimensional coordinate of the target.
21639	基于该条件，使用一种新的两步加权最小二乘算法实现对目标的定位解算。	Based on this condition, the target position is solved by a new two-step WLS algorithm.
21640	该算法不需要目标位置初始值估计，无需迭代运算，计算量较小。	It does not require initial solution guess, and is computationally attractive due to the non-iterative operation.
21641	仿真表明：利用目标高度先验信息可有效提高对目标的定位精度；在观测量噪声为高斯噪声且功率较小时，算法定位性能可达到克拉美罗界。	Simulation results show that the target localization accuracy is greatly improved using target altitude, and the proposed method can reach Cramer-Rao Lower Bound (CRLB) accuracy under small Gaussian measurement noise.
21642	论文提出一种基于频率位置多项式的稀疏混叠频谱快速恢复算法。	A fast algorithm based on Frequency Locator Polynomial (FLP) for sparse spectrum recovery is proposed.
21643	该算法使用不同延时的多通道欠采样得到的信号混叠频谱，通过建立频率位置多项式，快速定位非零频点，并有效地将非线性的频谱恢复问题转换成一系列线性方程组的求解问题。	Using the shifted subsampled signals, the FLPs are constructed, thus to locate rapidly the nonzero frequencies. In particular, the nonlinear problem of sparse spectrum recovery is converted into solving a series of linear equations.
21644	该算法的计算速度相对国外同类算法(BigBand)有显著提高，并且实验结果表明该算法具有更低的频谱恢复错误率。	Experimental results show that the proposed algorithm exhibits higher processing speed and lower error spectrum reconstruction rate than its predecessor BigBand.