一种OFDM系统最大似然信道估计的改进算法

针对传统的信道估计算法中算法性能与复杂度的矛盾,研究了一种低复杂度的最大似然信道估计算法。仿真结果表明,该算法与传统的信道估计算法相比较,具有较低的复杂度和较好的估计性能。     

动态模糊极大似然估计算法

在机器学习中,一个广泛的应用是对模型的参数进行估计,即极大似然估计(MLE),EM算法是根据点估计中的MLE改进的一种迭代算法,是求极大似然估计的一种强有力的工具,但它收敛速度较慢,于是引入α-EM算法,克服了EM算法的缺陷.由于学习的过程中可能存在着大量的缺失数据及其动态模糊性,给出基于不完全数据的动态模糊极大似然估计算法并给出实例验证.     

基于实数遗传算法的波达方向最大似然估计算法

对空间多个窄带信号源的高分辨波达方向估计是雷达,块纳和地震等信号处理中的重要问题之一,为克服一些获取波达方向估计最大似然解算法存在的局部极值问题,提高估计精度,本文以作者提出的实数遗传算法为搜索工具,寻求波达方向最大似然估计的非线性全局最优解,所提出的实数遗传算法由含实数域结构和目标函数信息的实数交叉和变异算子构成,是较理想的获取非线性实变量函数全局最优解的方法,对非相参和全相参信源波达方向估计问题的数字仿真结果表明,本方法的估计精度明显优于交替极值等一些常规方法。     

一种空时分组码最大似然检测的递归算法

空时最大似然序列估计(MLSE)方法是在已知接收的向量信号的情况下,估计已经发送出去的数据序列。论文在分析了空时编码(STBC)原理的基础上,提出了一种空时分组码的最大似然比检测的递归算法。利用STBC的正交结构,简化了最大似然准则,并且推导出递归表达式,接收端采用Viterbi译码,以较低复杂度实现最大似然译码。     

最大似然估计ROC凸包曲线算法

采用列联表表示特定阈值下入侵检测系统(IDS)的性能,使用ROC曲线对不同阈值下IDS的总体性能进行评估。在实际应用中,基于样本数据集,通过计算几何的方式得到ROC凸包曲线(ROCCH)。为降低ROCCH的计算复杂度,在曲线下面积(AUC)最大似然估计的条件下,通过保序回归得到最大似然估计ROC曲线(MLE-ROC)算法。实验表明,MLE-ROC算法在降低计算复杂度的同时提高了AUC的近似程度。     

基于似然估计修正信噪比的编码调制切换算法

AOS通信系统在进行编码调制切换时,由于信道环境和信道衰落的影响,传统的编码调制切换算法难以解决高阶的调制编码方式的高传输效率和低阶的调制编码方式的可靠性的矛盾。提出一种基于似然估计修正信噪比的编码调制切换算法,该方法在方差修正平均信噪比的基础上,根据信道的概率特性,考虑时变衰落信道对传输数据的影响,结合最大似然估计算法和长短期记忆网络来选取参考信噪比和预测信道状态,使系统在提升传输效率的同时保证可靠性。仿真结果表明,与基于方差修正信噪比的编码调制切换算法、基于经验方差修正信噪比的编码调制切换算法相比,该算法能有效提升系统的传输效率和吞吐量,降低系统的误码率。     

宽带最大似然方位估计快速算法水池实验研究

建立了宽带阵列高分辨方位估计水池实验系统,进行了宽带最大似然方位估计快速算法的水池实验研究。研究结果表明,基于马尔可夫链蒙特卡罗方法宽带最大似然方位估计快速算法分辨能力强,突破了二分之一波束宽度,估计出了目标的方位。     

相干信源DOA估计的最大似然算法应用及改进

讨论了阵列天线基于交替投影迭代算法的最大似然估计,实现信号源方位估计以及多个同距同速信源的方位角分离方法。此估计方法可用于相关信号目标的方位估计。由于此方法耗时较长,与斐波那契数列法结合,减少了搜索次数,提高了测角效率。仿真结果表明了此方法的有效性。     

基于最大似然估计的加权质心定位算法

为解决无线传感器网络中节点自身定位问题,针对接收信号强度指示（received signal strength indication,RSSI）测距误差大和质心定位算法精度低的问题,提出一种基于最大似然估计的加权质心定位算法．首先通过计算将估计距离与实际距离之间的最大似然估计值作为权值,然后在权值模型中,引进一个参数k优化未知节点周围锚节点分布,最后计算出未知节点的位置并加以修正．仿真结果表明,基于最大似然估计的加权质心算法具有定位精度高和成本低的特点,优于基于距离倒数的质心加权和基于RSSI倒数的质心加权算法,适用于大面积的室内定位．     

基于最大似然估计的不可区分多目标定位法

传统的最大似然估计定位法无法剔除误差较大或错误的参数,且不适用于多目标情形。结合最大似然估计理论,针对不可区分多目标定位问题,引出了目标位置信息场定位法,通过加入代价函数使得该方法能够剔除错误数据,降低误差大的数据对定位结果估计的影响。仿真结果表明,目标位置信息场定位法具有筛选有用参数信息的能力,且适用于不可区分的多目标定位,能够同时确定目标数目和位置,验证了方法的有效性。     

A low-complexity line-based motion estimation algorithm and its hardware architecture

In this paper, a low-complexity motion estimation algorithm is presented. The proposed algorithm transforms a multiple-bit video sequence into a one-bit representation using a line-based approach. The Full Search Block-Matching Algorithm （FSBMA） is then carried out on the One-Bit- Transformed （1BT） video frames. The advantage of the proposed work is that it reduces the computational complexity of the motion estimation process without causing a significant decrease in the motion estimation performance. This paper also presents a hardware architecture to perform the line-based 1 BT in real-time.     

利用帧头扩展扩频系统多普勒容限的方法

由于伪随机序列具有多普勒频移敏感特性,所以扩频接收机需要在频域和时域同时进行捕获.为缩短捕获时间,可在数据解调支路采用并行多通道进行叉-点积法进行频率估计,但传统的叉-点积法鉴频范围仅为数据速率的1/4,多普勒容限较小.为此,提出了一种利用数据帧头避免叉-点积方法中频率符号反转的方法,可以将数据解调支路多普勒容限扩大一...     

A new Doppler centroid estimation method for high-squint curved-trajectory airborne synthetic aperture radar

ABSTRACT

The average cross-correlation coefficient (ACCC) is a traditional Doppler centroid estimation (DCE) method applied for complex radar data. Though the ACCC method is accurate enough for moving platforms with small acceleration, it cannot be applied for high-squint curved-trajectory synthetic aperture radar because the accompanied larger acceleration will lead to serious Doppler spectrum expansion and result in Doppler aliasing. To solve this problem, a new DCE method is proposed in the paper for the cases with large acceleration. The performance improvement is achieved by employing an additional compensation for the phase and envelope induced by the acceleration. The presented approach is evaluated by the computer simulations.     

一种基于小数倍多普勒信道的OTFS信道估计方法

针对现有正交时频空(OTFS)调制系统的信道估计中存在的高峰均比和小数倍多普勒信道下估计困难及复杂度高的问题,提出了一种基于序列导频的匹配滤波(SMF)信道估计方法。该算法首先将序列导频与数据联合成帧,依靠序列的自相关性获取路径数、时延和整数倍多普勒;然后通过互相关匹配滤波估计小数倍的多普勒抽头和信道增益,从而得到信道状态信息;最后根据小数倍信道整数采样的特征,更新信道增益和信道初始相位。仿真结果表明,该方法相比基于嵌入式脉冲导频的信道估计,改善了峰均比,并提高了信道估计性能。相比于传统的序列导频,该方法可以估计得到小数倍多普勒抽头,估计的信道状态信息更准确。该信道估计方法更具有普遍性。     

A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system

Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches(86% reducing for a 0.5 min visual tracking). Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme(edge detection), and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.     

基于车载信息的多普勒频移估计方法研究

列车高速移动带来的多普勒频移问题增大了无线通信系统的误码率。经典Fitz估计算法已经无法适应高速环境下频偏大且变化速率大的特点。提出基于车载信息的多普勒频移估计方法,首先推导列车在不同行驶条件下的多普勒频偏公式,然后获取实时车载信息估计出多普勒频偏,最后结合改进Fitz估计算法进行第二次精确估计。通过构建无线信道传输误码率的仿真模型,利用matlab的simulink工具箱对列车行驶过程中误码率进行仿真。结果表明联合估计算法可以精确、实时估计列车高速移动带来的多普勒频偏,并且间接地增大了改进Fitz算法频偏估计范围。     

De-noised estimation of the weather Doppler spectrum by the wavelet method

In the aeronautics generally and in the aviation field particularly, the radar detection of hazardous and severe weather phenomenons as winds or microburst is very important. This detection allows the avoidance of these severe phenomenons and consequently will enhance the safety of the aircrafts flights. In this work, we have de-noised the weather radar signals by the wavelet method in order to estimate the mean velocity of winds. The results showed that the application of this method is promising compared with the classical estimators (pulse pair, Fourier, AR and ARMA).     

Efficient importance sampling for ML estimation of SCD models

The evaluation of the likelihood function of the stochastic conditional duration (SCD) model requires to compute an integral that has the dimension of the sample size. ML estimation based on the efficient importance sampling (EIS) method is developed for computing this integral and compared with QML estimation based on the Kalman filter. Based on Monte Carlo experiments, EIS-ML estimation is found to be more precise statistically, but involves an acceptable loss of quickness of computations. The method is illustrated with real data and is shown to be easily applicable to extensions of the SCD model.     

一种用于交织多址系统中的低复杂度的交织方法

针对传统交织多址（IDMA）系统中的交织器问题,提出了一种低复杂度的二维交织方法。该方法基于二维扩频的思想,分别在时域和频域对数据进行交织处理。同时,该方法对所有用户按照时域扩频码进行分组,用户组间采用不同的时域交织器,用户组内采用不同的频域交织器。仿真结果表明,该方法不但可以降低交织器的数量和长度,还可以降低IFFT／FFT操作和频域检测的复杂度。     

A low-complexity microprocessor design with speculative pre-execution

Current superscalar architectures strongly depend on an instruction issue queue to achieve multiple instruction issue and out-of-order execution. However, the issue queue requires a centralized structure and mainly causes globally broadcasting operations to wakeup and select the instructions. Therefore, a large issue queue ultimately results in a low clock rate along with a high circuit complexity. In other words, the increasing demands for a larger issue queue correspondingly impose a significant burden on achieving a higher clock speed.This paper discusses our Speculative Pre-Execution Assisted by compileR (SPEAR), a low-complexity issue queue design. SPEAR is designed to manage the small window superscalar architecture more efficiently without increasing the window size. To this end, we have first recognized that the long memory latency is one of the factors that demand a large window, and we aim at achieving early execution of the miss-causing load instructions using another hierarchy of an issue queue. We pre-execute those miss-causing instructions speculatively as an additional prefetching thread. Simulation results show that the SPEAR design achieves performance comparable to or even better than what would be obtained in superscalar architectures with a large issue queue. However, SPEAR is designed with smaller issue queues which consequently can be implemented with low hardware complexity and high clock speed.