Sparse frequency transmit-and-receive waveform design

A computationally efficient algorithm derives complex digital transmit and receive ultra-wideband radar and communication waveforms with excellent arbitrary frequency band suppression and range sidelobe minimization. The transmit waveform minimizes a scalar function penalizing weighted spectral energy in arbitrary frequency bands. Near constant power results from another penalty function for deviations from constant power, or constant power is enforced by a phase-only formulation. Next, a least squares solution for the receive waveform minimizes a weighted sum of suppressed band spectral energy and range sidelobes (for pulse and continuous wave operation), with a mainlobe response constraint. Both waveforms are calculated by iterative algorithms whose updates require only linear order in memory and computation, permitting quick calculation of long pulses with thousands of samples.     

An anticipative adaptive array for frequency-hopping communications

To fully utilize the theoretical processing gain achievable when an adaptive array and frequency hopping are combined, frequency compensation is required. Improved versions of an anticipative adaptive array are examined that provide efficient compensation by adapting the complex weights at each antenna element to the appropriate values for a carrier frequency before that frequency is received. The underlying adaptive algorithm used is the maximum algorithm. Computer simulation results are used to compare the different versions of anticipative processing. These results show that an appropriate version ensures the rapid convergence of weights to values that provide wideband nulling of the interference and noise     

MIMO-OFDM中一种新的接收算法

建立了基于空时分组编码的多输入多输出正交频分复用（MIMO-OFDM）发射模型,提出了基于该发射模型和软输入软输出（SISO）技术的多用户似然迭代接收算法,该算法通过采用分组接收和简化的SISO多用户检测算法,使得接收算法具有较低的复杂度和较快的收敛性能。给出了该算法的具体实现框图,通过仿真实验验证了该算法的快速收敛性和有效性。     

Efficient robust AMF using the FRACTA algorithm

The FRACTA algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists nonhomogeneous clutter, jamming, and dense target clusters. Further developments of the FRACTA algorithm are presented here in which the focus is on the robust, efficient implementation of the FRACTA algorithm. Enhancements to the FRACTA algorithm include a censoring stopping mechanism, an alternative data blocking approach for adaptive power residue (APR) censoring, and a fast reiterative censoring (RC) procedure. Furthermore, a coherent processing interval (CPI) segmentation scheme for computing the adaptive weights is presented as an alternative approach to computing the adaptive matched filter (AMF) weight vector that allows for lower sample support and reduced computational complexity. The enhanced FRACTA algorithm, denoted as FRACTA.E, is applied to the KASSPER I challenge datacube which possesses dense ground target clusters that are known to have a significant deleterious effect on standard adaptive matched filtering (AMF) processors. It is shown that the FRACTA.E algorithm outperforms and is considerably more computationally efficient than both the original FRACTA algorithm and the standard sliding window processing (SWP) approach. Furthermore, using the KASSPER I datacube, the FRACTA.E algorithm is shown to have the same detection performance as the clairvoyant algorithm where the exact range-dependent clutter covariance matrices are known.     

用于飞控系统试飞验证的识别方法

为试飞验证飞控系统的稳定性,改进,确认控制律和评价飞机的飞行品质,提出了富氏系数法（ＦＣ）,直接快速富氏变换－功率谱密度地（ＦＦＴ－ＰＳＤ）和相关函数为－功率谱密度法（ＣＦ－ＰＳＤ）确定系统频率响应,进而采用罗森布洛克无约束极值法和最小二乘－高斯牛顿迭代法进行模型参数拟合,另外,给出适合于上述方法的输入激励设计,通过用Ｔｕ－１５４Ｍ空中模拟试验机人机闭环／手动扫频试验数据和ＢＷ－１变稳机手动／自动     

基于正交函数的神经网络色谱曲线自适应在线滤波和压缩

由于正交基函数神经网络采用Givens正交学习算法，不是迭代算法，因而速度快，加之其无初始权值选择问题，不会陷入局部极小，所以本文选择此神经网络用于色谱数据的自适应在线滤波和压缩。实验结果表明，本方法滤波效果好，压缩比高，可实时完成，非常适用于色谱数据的在线处理需要。     

Direction-of-arrival estimation using signal subspace modeling

A computationally viable algorithm for estimating the direction-of-arrival (DOA) of multiple wavefields that are incident on an array of sensors is developed. The geometry of the array is unrestricted and the incident wavefields may be generated by mixtures of incoherent and coherent emitting sources. In the approach taken, the sensor signals are modeled as a noise-contaminated linear combination of steering vectors which are functionally dependent on a set of DOA parameters. These parameters are to be chosen so that this sensor signal model is most compatible with empirically measured data (i.e., snapshot data). An iterative procedure is developed for selecting the most data compatible set of DOA parameters in both the snapshot domain and the array covariance matrix domain. Critical to the success of such iterative solution procedures is the generation of quality DOA parameters to initialize the algorithm. A sequential beamforming method for this initialization is presented. Numerical examples to illustrate the effectiveness of the proposed algorithmic approach are given     

一类非线性系统的D型在线学习控制

结合对初始状态的学习,给出了一类输出方程不含控制变量的时变非线性系统的D型在线学习控制算法及其收敛条件。其特点是将学习误差在线反馈,且迭代初始状态只需通过理想信号及系统的部分信息即可确定。最后,将所得结论应用于机器人系统,表明本文方法是有效的。     

填充函数法在发动机加力最小油耗模式控制中的应用

主要研究了航空发动机性能寻优控制(PSC)算法问题。提出一种用于解决非线性约束优化问题的基于填充函数方法(FFM)的实时优化控制策略。通过构造填充函数,该算法可以在优化计算过程中能够不断跳出局部最优点,使得算法本身具备了全局寻优能力。详细介绍了其算法主要内容与实现途径,基于上述的填充函数优化算法,以某型涡扇发动机加力最小油耗优化控制模式为仿真算例,验证了该算法在解决航空发动机性能寻优控制问题时,相比传统的序列线性规划方法在全局寻优方面具有更好的效果。     

一种快速有效的薄壁件加工表面误差预测算法

 在切削力作用下，刀具/工件的变形是影响薄壁弱刚度件加工精度与质量的关键因素，快速有效地进行表面误差的预测是实现工艺参数优化及在线刀具路径补偿的前提。针对立铣加工过程，提出了一种考虑刀具/工件变形位置的快速柔性迭代算法，基于此建立了薄壁件加工变形预测的有限元计算模型，并通过等效集中力作用位置的确定、模型分割及最小化网格重划方法进一步提高了模型的计算速度。通过刀具/工件的瞬时接触区域的限定算法、实际切深的修正算法、材料去除效应的模拟等关键技术更提高了模型的计算精度。以典型航空铝合金材料为对象，合理安排试验，并通过数值计算结果和试验数据的对比，表明该方法计算精度高，计算速度较文献方法提高了近2倍。     

An LS-TLS-based sequential identification algorithm: Application toan accelerometer

Total least squares (TLS) parameter estimation is an alternative to least squares (LS) estimation when there are errors in both data matrix and observation vector. Especially, when some of the columns, not all, of the data matrix A are free of error, we call it a mixed LS-TLS problem. Accordingly, a sequential algorithm for solving a mixed LS-TLS problem is proposed here. The proposed algorithm employs an efficient algorithm to locate the minimum eigenpair, instead of singular value decomposition (SVD) which is computationally exacting. The proposed algorithm is applied to an accelerometer model to identify error parameters which are very important in inertial navigation systems (INS)     

A class of near optimal JPDA algorithms

The crucial problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information If an incorrect hit is associated with a track, that track could diverge and prematurely terminate or cause other tracks to also diverge. Most methods for hit-to-track data association fall into two categories: multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA). Versions of MHT use all or some reasonable hits to update a track and delay the decision on which hit was correct. JPDA uses a weighted sum of the reasonable hits to update a track. These weights are the probability that the hit originated from the target in track. The computational load for the joint probabilities increases exponentially as the number of targets increases and therefore, is not an attractive algorithm when expecting to track many targets. Reviewed here is the JPDA filter and two simple approximations of the joint probabilities which increase linearly in computational load as the number of targets increase. Then a new class of near optimal JPDA algorithms is introduced which run in polynomial time. The power of the polynomial is an input to the algorithm. This algorithm bridges the gap in computational load and accuracy between the very fast simple approximations and the efficient optimal algorithms     

一种基于短时机动试飞数据的动力学辨识优化方法

飞行动力学辨识算法的一个关键问题是,如何通过简单的机动获取所关心频率范围的响应特性。短时倍脉冲是一种易于实施的激励信号,兼顾试飞安全性与经济性,但与频域辨识法通常使用的扫频输入激励相比,短时机动频谱范围窄、信噪比低,一般难以得到准确的辨识结果。对如何基于短时机动飞行试验数据,提高辨识结果准确性的问题进行了研究。首先分析了经典Welch谱估计进行时域-频域转换过程中,影响非参数模型辨识精度的主要因素,提出了削减窗函数边缘缩减效应的数据预处理方法,并结合多窗口综合技术,提高频域特性辨识结果的精度。在参数化模型辨识过程中,针对有限频谱范围,提出了利用相干函数和功率谱密度加权综合,确定等效拟配的频率范围和频率节点的自适应方法,使得低阶等效拟配与输入激励信号高度相关,提高参数化模型辨识的精度、一致性和适应性。通过不同类型飞机的大量短时机动和少量扫频飞行试验数据模型辨识的工程应用示例,验证了动力学辨识优化方法算法稳定、结果准确,可满足飞行品质模态特性评价等应用需求。     

基于云自适应粒子群算法的高压转子复杂接触有限元模型参数修正

为建立更加准确的航空发动机高压转子的有限元模型,提出一种修正有限元模型描述航空发动机复杂接触的方法.将修正问题转化为求解定义在时域的误差函数的极小值,运用云自适应方法动态调整粒子群算法的惯性权重,使得算法在接近较优解时,惯性因子分布在云低端,有利于收敛得到更优解;当问题解较差时,其惯性因子分布在云顶端,有利于跳出局部极小点,扩大搜索范围.以仿真算例和实际航空发动机高压转子模型为例,通过与相关算法的修正结果比较,证明该算法是可行且有效的.      

基于多机协作的认知无人机网络能效联合优化

针对无人机（UAV）通信网络中频谱资源紧缺的问题,构建基于认知无线电的多无人机通信网络,通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法,通过联合优化感知时间和判决门限对构建的复杂非凸问题求解,显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况,仿真结果表明,存在最优感知时间使能效获得最大值,且判决门限的选择会影响该能效最优值;提出的高能效迭代算法具有较好收敛性,有效提高了认知无人机网络的能量利用率。     

Newton迭代法的P．C．格式

基于Newton迭代法 ,提出了一种有效的预估校正 (P .C .)迭代格式。本方法把牛顿法中的微分以差商替代 ,因而 ,既可求解具离散根的方程 ,也适用于有重根的方程求解 ,大量的试验结果表明 ,本文算法不仅可有效解决重根问题 ,而且有较高的收敛速度。     

U-D分解的固定点平滑新算法及其在飞行试验中的应用

 本文根据Rauch固定点平滑公式,提出了一种U-D分解的固定点平滑新算法。这一算法不仅具有良好的数值稳定性和可靠性,而且计算量较少;计算效率是Bryson-Ho Y C固定点平滑计算效率的1.5倍以上。将这种新算法用于飞机运动状态初值的确定,提高了飞机气动参数辨识精度。     

解流水车间作业排序问题的一种禁止搜索算法

流水车间的作业排序（Ｆｌｏｗｂｓｈｏｐｓｃｈｅｄｕｌｉｎｇ）问题是生产管理与组合优化领域研究的重要课题。禁止搜索（ＴａｂｕＳｅａｒｃｈ）算法对于这类问题具有较强的寻优能力。本文针对流水车间的作业排序问题设计了一种禁止搜索算法的实现形式，通过大量的算例分析表明，该算法具有良好的寻优性与运算效率。     

深空组阵迭代FX相关合成算法及性能分析

针对火星距离下大规模天线组阵中极微弱接收信号的高效合成问题,在VLBI(Very Long Baseline Interferometry,甚长基线干涉测量)的FX相关器开环处理架构基础上提出了一种新的迭代FX相关合成算法,对算法原理进行了详细理论推导,对数据处理流程、算法合成性能等进行深入分析,与FSC(Full-Spectrum Combining,全频谱合成)、BC(Baseband Combining,基带合成)、SSC(Symbol-Stream Combining,符号流合成)等组阵方案进行性能仿真对比,并给出该算法在36接收天线组阵的实测试验结果。仿真和试验结果表明,迭代FX相关合成算法相比于FSC、BC、SSC等组阵方案具有较好的合成性能,在输入带内信噪比不低于-20d B时,合成效率达到了95%以上,非常适用于火星天线组阵微弱接收信号的组阵合成。