Velocity estimation of moving targets with stepped-frequency radar based on Doppler frequency difference

By analyzing the signal model of stepped-frequency waveform, a novel method for velocity measurement is proposed. The method is based on Doppler frequency difference which is achieved by using Hough transform. As the estimated velocity is inversely proportional to the frequency step size instead of the carrier frequency of the transmitted signal as the pulse-Doppler （PD） processing, the new algorithm can achieve much wider unambiguous velocity range. Furthermore, non-coherent integration of the sub-pulses with different carrier frequencies can be implemented by Hough trans- form to improve the anti-noise performance. Besides, field experimental results show that the high range resolution profile （HRRP） of a bullet with high speed can be reconstructed correctly without distortion.

Video Image Block-matching Motion Estimation Algorithm Based on Two-step Search

Aiming at the shortcoming that certain existing blocking-matching algorithms,such as full search,three-step search,and diamond search algorithms,usually can not keep a good balance between high accuracy and low computational complexity,a block-matching motion estimation algorithm based on two-step search is proposed in this paper.According to the fact that the gray values of adjacent pixels will not vary fast,the algorithm employs an interlaced search pattern in the search window to estimate the motion vector of the object-block.Simulation and actual experiments demonstrate that the proposed algorithm greatly outperforms the well-known three-step search and diamond search algorithms,no matter the motion vector is large or small.Compared with the full search algorithm,the proposed one achieves similar performance but requires much less computation,therefore,the algorithm is well qualified for real-time video image processing.

OPTIMAL DISTRIBUTED FUSION ALGORITHM WITH ONE-STEP OUT-OF-SEQUENCE ESTIMATES

The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of researchers.The existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of theOOSE problem also has good fusion accuracy.Performance analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.

A Novel Two-step Frequency Offset Estimator for OFDM

Based on an orthogonal frequency division multiplexing(OFDM) training symbol with L identical parts, a novel carrier frequency offset (CFO) estimator is proposed for OFDM systems. The CFO is estimated in two steps, fine estimate and coarse estimate. In the first step, the fine estimation is performed based on the principle of minimum variance. However, the fine estimation has ambiguity since its estimate range is limited. In the second step, the coarse estimation is obtained, which results in a larger estimate range but less precision. Using the coarse estimation, the ambiguity of fine estimation is resolved. To fully use the correlation among L identical parts, the fine estimation resolved the ambiguity and the coarse estimation are optimally combined to obtain the final estimation. Furthermore, the estimation variance of the proposed method is derived. Simulation results demonstrate that the novel two-step estimator outperforms the conventional two-step estimator in terms of estimate performance and computational complexity.

Constraints on Estimation of Radius of Double Pulsar PSR J0737-3039A and Its Neutron Star Nuclear Matter Composition

The aspect of formation and evolution of the recycled pulsar（PSR J0737-3039 A/B） is investigated, taking into account the contributions of accretion rate, radius and spin-evolution diagram（- diagram） in the double pulsar system. Accepting the spin-down age as a rough estimate（or often an upper limit） of the true age of the neutron star, we also impose the restrictions on the radius of this system. We calculate the radius of the recycled pulsar PSR J0737-3039 A ranges approximately from 8.14 to 25.74 km, and the composition of its neutron star nuclear matters is discussed in the mass-radius diagram.

Robust estimation algorithm for multiple-structural data

This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.

Empirical Analysis of Value-at-Risk Estimation Methods Using Extreme Value Theory

This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and maximum likelihood estimation (MLE), according to their theoretical bases and computation procedures. Then, the estimation results are analyzed together with those of normal method and empirical method. The empirical research of foreign exchange data shows that the EVT methods have good characters in estimating VaR under extreme conditions and 'two-step subsample bootstrap' method is preferable to MLE.

New Diamond Block Based Gradient Descent Search Algorithm for Motion Estimation in the MPEG- 4 Encoder

Motion estimation is an important part of the MPEG- 4 encoder, due to its significant impact on the bit rate and the output quality of the encoder sequence. Unfortunately this feature takes a significant part of the encoding time especially when the straightforward full search(FS) algorithm is used. In this paper, a new algorithm named diamond block based gradient descent search (DBBGDS) algorithm, which is significantly faster than FS and gives similar quality of the output sequence, is proposed. At the same time, some other algorithms, such as three step search (TSS), improved three step search (ITSS), new three step search (NTSS), four step search (4SS), cellular search (CS) , diamond search (DS) and block based gradient descent search (BBGDS), are adopted and compared with DBBGDS. As the experimental results show, DBBGDS has its own advantages. Although DS has been adopted by the MPEG- 4 VM, its output sequence quality is worse than that of the proposed algorithm while its complexity is similar to the proposed one. Compared with BBGDS, the proposed algorithm can achieve a better output quality.

基于Arrhenius-Weibull模型的ECU步进应力加速试验设计

文章基于Arrhenius-Weibull模型对ECU的加速寿命试验方法展开研究,基于加速模型试验原理选择步进应力施加方式。为满足产品的可靠度指标,通过分析Weibull分布情况计算最小试验样件数量。通过最大似然估计的方法确定产品的Arrhenius-Weibull模型参数,以便完成各个应力阶段的等效转换。通过对步进加速试验设计过程示例,提供一种满足产品寿命参数指标的判断依据,为后续从业者进行相关的加速试验设计提供依据。

基于STEP-NC的数控加工能耗估算方法

为了给低能耗的数控加工提供支持、满足当今低碳制造的需求,提出了一种基于STEP-NC的能耗估算方法。将数控机床按照其组件耗能性质的不同分为基本能耗组件、选择能耗组件以及运动能耗组件。以STEPNC的工步为研究对象,将其执行过程分为特征加工辅助、接近制造特征、特征加工以及远离制造特征4个状态,在对机床组件分类的基础上,给出了每种状态的能耗计算方法,依次相加便可得到单个工步的耗能,进一步即可计算出工件的加工能耗。基于此方法开发了能耗估算软件,通过一个零件的STEP-NC文件进行了测试,结果表明该方法可用于数控加工的能耗估算,并具有一定的实用性。

Numerical simulation of flow separation over a backward-facing step with high Reynolds number

Large eddy simulation (LES) explicitly calculates the large-scale vortex field and parameterizes the small-scale vortices.In this study,LES and κ-ε models were developed for a specific geometrical configuration of backward-facing step (BFS).The simulation results were validated with particle image velocimetry (PIV) measurements and direct numerical simulation (DNS).This LES simulation was carried out with a Reynolds number of 9000 in a pressurized water tunnel with an expansion ratio of 2.00.The results indicate that the LES model can reveal largescale vortex motion although with a larger grid-cell size.However,the LES model tends to overestimate the top wall separation and the Reynolds stress components for the BFS flow simulation without a sufficiently fine grid.Overall,LES is a potential tool for simulating separated flow controlled by large-scale vortices.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

JOINT RANDOM OPTIMIZED THREE-STEP SEARCH ALGORITHM BASED-ON GRAY AND CHROMATIC INFORMATION

In this letter, an improved three-step search algorithm is presented, which uses both gray and chromatic information to boost the performance with random optimization and converge the motion vectors to global optima. Experimental results show that this algorithm can efficiently improve the PSNR after motion compensation.

The convergence ball and error analysis of the two-step Secant method

Under the assumption that the nonlinear operator has Lipschitz continuous divided differences for the first order,we obtain an estimate of the radius of the convergence ball for the two-step secant method.Moreover,we also provide an error estimate that matches the convergence order of the two-step secant method.At last,we give an application of the proposed theorem.

Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures： convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application （VBA）. Maximum likelihood function （MLF） is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model.

Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

To address the intermittent positioning and drift of personnel positioning RTK in the high-frequency signal interference environment of substations, we propose to use IMU as the positioning compensation module of RTK and adopt the joint RTK/PDR positioning method to solve the positioning results. The heading angle is easily scattered in the pedestrian heading projection (PDR) process and the heading angles calculated from the output data of the gyroscope, accelerometer and magnetometer after denoising are input into the complementary filter for fusion. To improve the accuracy of step estimation in the PDR process, an improved step estimation model is used. For RTK/PDR data fusion, the extended Kalman filter (EKF) method is used, which helps to achieve outdoor full-scene high-accuracy positioning. The final simulation results show that RTK can be effectively compensated by PDR under the interference of high-frequency signals, and the positioning accuracy reaches 0.02 m.

An Improved Binary Wolf Pack Algorithm Based on Adaptive Step Length and Improved Update Strategy for 0-1 Knapsack Problems

Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.

基于多模型融合的室内行人航迹推算建模与性能分析

针对行人航位推算(pedestrian dead reckoning,PDR)室内信号易受到环境和多径效应干扰的问题,提出一种基于多模型融合的室内PDR优化建模方法.给出多模型融合的室内PDR建模方法系统模型,包括步数检测、步长推算、方向推算以及位置推算4个关键阶段.该方法在步数检测阶段融合了峰值检测算法、局部最大值算法以及提前过零检测算法;在步长推算阶段融合Weinberg方法和Kim方法,并利用卡尔曼滤波算法校正步数检测和步长推算的误差.基于不同场景从步数、步长、方向、位置误差方面与传统算法进行比较.结果表明,该组合模型结合了传统步数检测和步长推算算法的特征识别结果,可实现对步数检测、步长推算过程中信号特征的优化处理;在手持场景下,步数检测识别准确,步长推算中值误差在0.060 m以内,方向推算平均绝对误差最小为3.06°,位置推算平均误差为0.2353 m,取得较好的室内步行状态识别与定位性能.

考虑不确定性和定价补偿的源荷储联合调峰优化

针对大规模新能源并网带来的系统调峰和消纳难题,文中在分析源荷储调峰能力及其互补性的基础上,挖掘系统的调峰能力,促进新能源消纳。首先,考虑火电、独立储能和电动汽车的调峰定价及辅助服务补偿,分析调峰费用分摊与补偿机制,建立深度调峰模型;其次,考虑风光出力的不确定性与相关性,基于核密度估计和Frank Copula函数生成典型风光出力序列,并建立阶梯型需求响应模型以实现响应量的分级补偿,提高需求侧用户的响应积极性;最后,以总运行成本最小为目标,构建考虑不确定性和定价补偿的源荷储联合调峰优化模型,并以改进的IEEE 30节点系统为例进行分析。结果表明,所提模型能够挖掘源荷储三侧资源的灵活调峰潜力,提升系统调峰能力和新能源消纳水平。