一种加速收敛的LDPC码译码算法

对高斯信道下LDPC(Low-Density Parity-Check)码的传统的译码算法进行分析,指出影响收敛速度的原因,并提出了一种基于整数运算的加速收敛的LDPC码译码算法。该算法融合分层译码(Layered Belief Propagation)算法、带偏移量的最小和算法(Offset Min-Sum)以及量化的优势。仿真验证表明该算法有效地减少了译码复杂度,加速了译码收敛,且性能上同传统的量化最小和算法相比没有下降。     

地理信息系统课程实践教学的改革与探索

"地理信息系统"是一门技术性、实践性很强的新学科,实践教学在该课程的教学中起着重要的作用。这些年来,我们对该课程的实践教学(包括实验课教学、实验室建设、师资培养、教学方式组织、毕业论文及毕业设计等方面)进行了全面的改革,并加强了同生产实践单位的联系。     

Chirp信号参数估计算法性能比较

通过数值仿真定量地比较了三种Chirp信号参数估计算法——解线调法、迭代估计法和局部搜索最大似然法的性能,并定性地比较了算法的运算量。仿真结果表明,在三种算法中,局部搜索最大似然法的估计性能最好,而运算量居中;解线调法运算量最大,但估计性能居中;迭代估计法的估计性能最差,但运算量最小。对于实际系统,应根据不同的估计精度和运算量要求,灵活选择不同的算法。综合考虑估计性能和运算量之间的折衷可以得到结论,在三种算法中局部搜索最大似然法是一种相对较好的选择。     

2D组网雷达目标真实高度的最小二乘估计

针对目标高度估计问题,给出了基于三部以上雷达距离测量的2D组网雷达目标真实高度的最小二乘估计,同时也给出了利用三部雷达计算目标真实高度的非最小二乘算法,并通过大量仿真分析了两种算法的性能。该算法能够在2D组网雷达内实现对目标真实高度的有效估计。     

一种基于随机映射的战斗效能模型

利用随机映射概念提出了一种新的战斗毁伤模型形式———Lanchester战斗网络模型。模型是由一个阶递减的随机映射序列组成的随机有向二部图,它明确、形式地描述了战争整体行为与局部作用之间的关系。理论分析和计算获得的结论表明,模型描述的整体作战效能符合Lanchester平方律,其网络拓扑结构是非同质的,出度和入度分布服从指数幂律。应用模型定量对比了“对称”与“非对称”战斗中全局信息因素对战斗系统整体效能的影响。初步讨论了网络模型研究在战争建模理论、实证和计算方法论上的意义。     

基于扩张观测器的三维有限时间收敛导引律

为了保证视线角速率在弹目碰撞前收敛到零附近的较小邻域内从而达到准平行接近的状态,本文基于自抗扰控制的不确定性估计补偿思想,应用反演控制方法设计了一种考虑导弹自动驾驶仪二阶动态特性和目标机动的三维有限时间收敛导引律。根据有限时间收敛控制理论,严格证明了系统的有限时间收敛特性;为抑制量测噪声,将传统跟踪微分器进行改进并应用于扩张状态观测器与反演控制的设计中。仿真结果表明:在自动驾驶仪响应延迟情况下,所设计的导引律能够导引导弹在有限时间内精确地拦截高速机动目标;改进的跟踪微分器精度高、响应快;基于改进跟踪微分器的扩张观测器估计效果理想。     

遥感影像中采用多维特征的目标定位检测

给出一种在复杂场景的遥感影像中定位人造目标的方法,通过提取多种有利于检测人造目标的特征,并将其组合成特征矢量,采用非参数的均值漂移聚类方法进行分割,从而把目标从复杂场景中分离出来,通过检测影像中的房屋建筑物,结果表明该方法具有很好的稳健性和自动性。     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

基于正态分布曲线的分段式变步长LMS算法

针对传统最小均方误差(Least Mean Square, LMS)自适应滤波算法由于步长固定,在解决稳态误差与收敛性之间的关系时,始终处于矛盾状态的问题,在对传统的固定步长LMS自适应滤波算法分析的基础上,根据变步长LMS自适应滤波算法的步长调整原则,通过构造步长因子与误差信号的非线性函数,提出了一种基于正态分布曲线的分段式变步长LMS自适应滤波算法,并分析了参数取值对算法性能的影响。针对实际信号处理过程中参考信号难以选取的问题,提出了一种基于分裂阵的参考信号选取方法。理论和海试数据分析结果表明:该算法的收敛速度和稳态误差明显优于固定步长的LMS自适应滤波算法和基于Sigmoid函数的变步长LMS自适应滤波算法。     

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression; but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.