ID 原文 译文
21985 其次，该算法控制参数很少，运算速度明显提高。 Besides, there are few parameters to be adjusted, therefore, the computation speed is obviously improved.
21986 该算法的收敛速度十分稳定，定位精度更高。 Moreover, the convergence performance of the proposed algorithm is very stable and the accuracy of location is higher.
21987 仿真结果表明，樽海鞘群算法在 3 维时差定位中能够快速、稳定地收敛至目标位置，对传统粒子群算法(PSO)、改进的线性权重粒子群算法(IPSO)与 SSA 的定位精度进行比较，SSA 精度明显高于 PSO 与 IPSO。 Simulation results show that the proposed algorithm can converge to the position of emitters fast and stably in 3D TDOA location. Comparing with Particle-Swarm- Optimization (PSO) and Improved-Particle-Swarm-Optimization (IPSO), the proposed algorithm has lower mean square error.
21988 序列的k-错线性复杂度是序列线性复杂度稳定性的重要评价指标。 The k-error linear complexity of a sequence is a fundamental concept for assessing the stability of the linear complexity.
21989 在求得一个序列k-错线性复杂度的同时，也需要求出是哪些位置的改变导致了序列线性复杂度的下降。 After computing the k-error linear complexity of a sequence, those bits that make the linear complexity reduced also need to be computed.
21990 该文提出一个在GF( )q上计算2np -周期序列s的k-错线性复杂度以及对应的错误序列e的算法，这里p和q是素数，且q是一个模2p的本原根。 For 2np-periodic sequence over GF( )q, where p and q are odd primes and q is a primitive root modulo2p, an algorithm is presented, which not only computes the k-error linear complexity of a sequence s but also gets the corresponding error sequence e.
21991 该文设计了一个追踪代价向量的trace函数，算法通过trace函数追踪最小的代价向量来求出对应的错误序列e，算法得到的序列e使得()se+的线性复杂度达到k-错线性复杂度的值。 A function is designed to trace the vector cost called "trace function", so the error sequence e can be computed by calling the "trace function", and the linear complexity of ()se+ reaches the k-error linear complexity of the sequence s.
21992 开展角闪烁噪声下的目标跟踪研究对提升传感器的探测性能具有重要意义，其中角闪烁噪声具有的分布未知和非平稳特性是长期困扰研究者的难点。 Research on target tracking with glint noise is important to improve detection performance of sensor, in which the glint noise's unknown distribution and non-stationary property puzzle researchers for a long time.
21993 针对该问题，该文首先给出角闪烁下基于变分贝叶斯参数学习的跟踪滤波理论框架。 In order to solve this problem, the tracking theoretical framework of variational Bayesian parameter learning with glint noise is firstly introduced.
21994 其次，提出一种联合估计运动状态和闪烁噪声分布的变分贝叶斯-交互式多模型(VB-IMM)算法，该算法通过设计多个并行的跟踪模型处理角闪烁的跟踪问题，同时利用变分贝叶斯方法实现闪烁噪声分布参数的在线学习，并反馈给跟踪模型，实时调整跟踪模型参数。 Then, a novel algorithm called Variational Bayesian-Interacting Multiple Model (VB-IMM) is proposed to estimate the system states as well as the unknown glint noise's distribution. The proposed algorithm designs a bank of tracking filters in parallel with different measurement noise. Moreover, the algorithm utilizes variational Bayesian method to learn distribution parameters of the glint noise online and feed these parameters back to the tracking filters to revise the filters.