ID 原文 译文
54777 结果表明,新提出的方法具有很好的抗多径性能,且具有带宽利用率高、复杂度低的特点,适用于中、远程及多用户水声通信。 Results show that the proposed method has good anti-multipath performance, and has the characteristics of the high bandwidth utilization, low complexity, suitable for medium and long-range and underwater acoustic communication among multiple users.
54778 传统多输入多输出(multi-input multi-output,MIMO)雷达发射波形设计方法对传播矩阵误差敏感,导致难以得到最优的匹配波形,进而造成系统检测性能严重下降。 Traditional multiple input multiple output (multi - input multi - output, MIMO) radar transmitted waveform design method is sensitive to error propagation matrix, making it difficult to get the optimal matching of the waveform, causing serious loss system detection performance.
54779 针对此问题,提出一种基于概率约束的MIMO雷达稳健发射波形设计方法。 Aiming at this problem, put forward a sound emission constraints based on probability of MIMO radar waveform design method.
54780 该方法考虑最差情况的发生为小概率事件,基于输出信噪比(signal noise ratio,SNR)低于可接受水平的概率小于中断概率的约束条件,通过最大化输出信噪比设计最优波形。 The method considered the worst situation for the small probability event, based on the output SNR (signal noise thewire, SNR) below the acceptable level of probability is less than the interrupt probability constraints, by maximizing the output signal-to-noise ratio design optimal waveform.
54781 利用传播矩阵误差的概率分布特性,将概率约束转化为凸约束,从而将统计优化问题转化为确定性优化问题。 Error probability distribution characteristics of the transmission matrix, the probability constraints into convex constraints, thus will statistical optimization problem is transformed into deterministic optimization problem.
54782 该方法在传播矩阵存在误差情况下以高概率实现系统性能最优化。 The method in the presence of error propagation matrix with high probability to achieve optimal system performance.
54783 仿真结果表明所提方法能够提高输出SNR,具有较好的检测性能。 The simulation results show that the proposed method can improve the output SNR, has good detection performance.
54784 给出了一种交互多模型(interacting multiple model,IMM)算法中Markov转移概率矩阵在线修正的方法,并将平方根容积卡尔曼滤波器(square-root cubature Kalman filter,SRCKF)引入到IMM算法中,提出一种时变转移概率的机动目标跟踪IMM-SRCKF算法。 Presents an interacting multiple model (interacting multiple model, the IMM) algorithm in the Markov transition probability matrix online correction method, Kalman filter and square root volume (the square - root cubature Kalman filter, SRCKF) is introduced into the IMM algorithm, put forward a kind of time-varying transition probability of the IMM - SRCKF of maneuvering target tracking algorithm.
54785 该算法利用当前量测中包含的模式信息,对IMM算法中的转移概率矩阵进行实时递推估计,避免了常规IMM算法中转移概率先验确定的困难,提高了模型切换速度和跟踪精度;同时,SRCKF以目标状态协方差的平方根进行迭代更新,确保了滤波过程中协方差矩阵的对称性和半正定性,改善了数值精度和稳定性。 The algorithm using the current measurement information contained in the model, the transition probability matrix in IMM algorithm for real-time recursive estimate, avoiding the conventional transfer probability in the IMM algorithm is prior to determine difficulty, improves the model switching speed and tracking accuracy;At the same time, a target state SRCKF iterate the square root of the covariance update, ensure the symmetry of covariance matrix in the process of filtering and half are qualitative, to improve the numerical accuracy and stability.
54786 仿真实验结果表明,该算法对机动目标的跟踪性能优于常规的IMM及IMM-CKF算法。 The simulation results show that the proposed algorithm for maneuvering target tracking performance is superior to the conventional IMM and IMM - CKF algorithm.