To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. ...To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).展开更多
Vehicle positioning with the global navigation satellite system （GNSS） in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak sig...Vehicle positioning with the global navigation satellite system （GNSS） in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak signals, the coupling between all visible satellite signals is ignored in the absence of navigation state feedback, and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators. The vector delay/frequency locked loop （VDFLL） with navigation state feedback and the joint vector tracking loop （JVTL） with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation （MLE） are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments. A joint vector position tracking loop （JVPTL） directly tracking user position and velocity is proposed to further improve tracking sensitivity. The coherent navigation parameter discriminator of JVPTL, being able to ease the contradiction between thermal noise error and dynamic stress threshold, is based on MLE according to the navigation parameter based linear model of received baseband signals. Simulation results show that JVPTL, which combines the advantages of both VDFLL and JVTL, performs better than both VDFLL and JVTL in dynamic weak signal environments.展开更多
针对将连续域蚁群优化算法应用于最大似然(maximum likelihood,ML)估计中存在计算量过大的问题,提出一种基于改进蚁群优化(modified ant colony optimization,MACO)算法的最大似然波达方向(maximum likelihood direction of arrival,ML-...针对将连续域蚁群优化算法应用于最大似然(maximum likelihood,ML)估计中存在计算量过大的问题,提出一种基于改进蚁群优化(modified ant colony optimization,MACO)算法的最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计方法.采用精英反向学习策略获得较优初始解群体,结合全局跨邻域搜索和高斯核函数局部搜索对蚁群的寻优方式进行优化,扩大了算法的搜索空间并加快了收敛速度,最终得到ML估计方法的非线性全局最优解.仿真结果表明,与基于粒子群优化(particle swarm optimization,PSO)算法、蚁群优化(ant colony optimization,ACO)算法的ML估计方法相比,ML-MACO算法的收敛速度是ML-ACO算法的4倍,计算量是ML-ACO算法的1/3,分辨成功率高于ML-PSO算法和ML-ACO算法,估计误差小于ML-PSO算法和ML-ACO算法.ML-MACO算法以更低的计算量保持了ML算法的优良估计性能,收敛性能更优且估计精度更高.展开更多
基金supported by Joint Foundation of and China Academy of Engineering Physical (10676006)
文摘To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).
基金supported by the National Natural Science Foundation for Young Scientists of China(61201190)
文摘Vehicle positioning with the global navigation satellite system （GNSS） in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak signals, the coupling between all visible satellite signals is ignored in the absence of navigation state feedback, and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators. The vector delay/frequency locked loop （VDFLL） with navigation state feedback and the joint vector tracking loop （JVTL） with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation （MLE） are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments. A joint vector position tracking loop （JVPTL） directly tracking user position and velocity is proposed to further improve tracking sensitivity. The coherent navigation parameter discriminator of JVPTL, being able to ease the contradiction between thermal noise error and dynamic stress threshold, is based on MLE according to the navigation parameter based linear model of received baseband signals. Simulation results show that JVPTL, which combines the advantages of both VDFLL and JVTL, performs better than both VDFLL and JVTL in dynamic weak signal environments.
文摘针对将连续域蚁群优化算法应用于最大似然(maximum likelihood,ML)估计中存在计算量过大的问题,提出一种基于改进蚁群优化(modified ant colony optimization,MACO)算法的最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计方法.采用精英反向学习策略获得较优初始解群体,结合全局跨邻域搜索和高斯核函数局部搜索对蚁群的寻优方式进行优化,扩大了算法的搜索空间并加快了收敛速度,最终得到ML估计方法的非线性全局最优解.仿真结果表明,与基于粒子群优化(particle swarm optimization,PSO)算法、蚁群优化(ant colony optimization,ACO)算法的ML估计方法相比,ML-MACO算法的收敛速度是ML-ACO算法的4倍,计算量是ML-ACO算法的1/3,分辨成功率高于ML-PSO算法和ML-ACO算法,估计误差小于ML-PSO算法和ML-ACO算法.ML-MACO算法以更低的计算量保持了ML算法的优良估计性能,收敛性能更优且估计精度更高.
