机器学习在河流流量参数估计中的应用

针对经典水位流量关系模型在刻画河流动态变化特性时存在的局限性,提出采用局部加权回归算法估计河流流量;为了提高参数估计精度,提出一种聚类局部加权回归方法。首先对训练样本进行聚类,然后使用k-最近邻方法将新的水位样本划分进最恰当的聚类中,最后估计河流日流量。该方法在估计过程中,避免了不相关信息的干扰,从而提高了日流量数据估计的效率和精度。利用某水文站的实测数据对方法进行测试,仿真结果表明该方法估计精度较高,为水位流量关系模型参数估计提供了新的有效方法。     

一种低复杂度LMMSE信道估计算法

针对传统线性最小均方误差(LMMSE)信道估计器矩阵求逆运算复杂度大的问题,提出了一种以相关带宽为准则提取信道自相关矩阵主要信息的子阵分块算法.根据信道相关带宽所计算的分块尺度将信道自相关矩阵分割为若干子块,包括非重叠分块法和重叠分块法.运算求逆过程中仅利用表征信道主要信息量的低频对角子阵,而忽略其他表征信道高频信息的子阵,可有效降低LMMSE算法中大的自相关信道矩阵求逆运算所带来的复杂度.该算法在频率选择性慢衰落信道下,与LMMSE和低秩估计算法进行了相关性能及运算复杂度对比分析,结果表明,该算法能以较微弱的性能代价换取系统复杂度的明显降低.     

猝发式直扩系统载波捕获技术研究

为了解决猝发式直扩系统中大频偏情况下信号载波的快速捕获问题,该文提出了一种集扫频、匹配滤波器和FFT频率估计技术相结合的快速载波捕获方案.在理论上对该方案进行详细分析后,基于MATLAB平台进行算法仿真验证.结果表明,该方案可以满足系统设计要求,具有较高的实际应用价值.     

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.     

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks. By exploring available detector and signal controller information from neighboring intersections, a dynamic data-driven flow prediction model was developed. The model consists of two prediction components based on the signal states (red or green) for each movement at an upstream intersection. The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted. With an online turning proportion estimation method, along with the predicted travel times, the anticipated vehicle arrivals can be forecasted at the downstream intersection. The model performance was tested at a set of two signalized intersections located in the city of Gainesville, Florida, USA, using the CORSIM microscopic simulation package. Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%–20%, and show a normal distribution. It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.     

Designing similarity measurement with distance measure and application on laterally directional mode flight test

Similarity measure construction has been proposed as fault detection of flight test method in order to obtain the primary control surface stuck and the combination stuck of primary control. Similarity measures were obtained through analyzing the certainty and uncertainty of fuzzy membership functions, which were designed based on well-known Hamming distance. It was applied to the fault detection of primary control surface stuck of uninhabited aerial vehicle (UAV). At post-failure control surface, if the UAV is controllable and trimmable using other control surfaces, the UAV is able to fly or return to the safety region through reconfiguration of flight control system. To detect the fault, similarity measure computations were carried out. This result could be applicable with the real-time parameter estimation method. By monitoring the value of coefficients due to the control surface deviation, it becomes aware that the control surface fault occurs or not. The control surface stuck position and value were separated by comparing the trim value with the reference value. This is the advantage of increasing in reliability without adding sensors or with additional low cost.     

Hypothesis testing analysis and unknown parameter estimation of GPS signal detection

Hypothesis testing analysis and unknown parameter estimation of both the intermediate frequency (IF) and baseband GPS signal detection are given by using the generalized likelihood ratio test (GLRT) approach, applying the model of GPS signal in white Gaussian noise. It is proved that the test statistic follows central or noncentral F distribution. It is also pointed out that the test statistic is nearly identical to central or noncentral chi-squared distribution because the processing samples are large enough to be considered as infinite in GPS acquisition problem. It is also proved that the probability of false alarm, the probability of detection and the threshold are affected largely when the hypothesis testing refers to the full pseudorandom noise (PRN) code phase and Doppler frequency search space cells instead of each individual cell. The performance of the test statistic is also given with combining the noncoherent integration.     

一种基于优先权的自相似流量接纳控制新算法

针对现今网络业务流量的自相似特性,选取具备显著自相似特征的分形布朗运动流（FBM）为自相似流量模型,提出一种基于优先权的接纳控制新算法.首先,应用统计网络演算对自相似业务的带宽需求进行了更准确的估计;其次,对业务进行优先级标记,对网络中需要保护的重要业务赋予较高的优先级,进行优先强制接纳.通过仿真验证和数值分析,结果表明新算法能给予需要保护的业务更加稳定的接纳性能.