Region-based classification by combining MS segmentation and MRF for POLSAR images

Speckle effects on classification results can be suppressed to some extent by introducing the contextual information.An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar(POLSAR) images based on the mean shift(MS) segmentation and Markov random field(MRF).First,polarimetric features are exacted by target decomposition for MS segmentation.An initial classification is executed by using the target decomposition and the agglomerative hierarchical clustering algorithm.Thereafter,a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment.Under the MRF framework,the smoothness term is defined according to the distance between neighboring areas.By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory,the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.     

Adaptive monopulse beamforming with partial parallel structure

A new recursive algorithm with the partial parallel structure based on the linearly constrained minimum variance (LCMV) criterion for adaptive monopulse systems is proposed. The weight vector associated with the original whole antenna array is decomposed into several adaptive weight sub-vectors firstly. An adaptive algorithm based on the conventional LCMV principle is then deduced to update the weight sub-vectors for sum and difference beam, respectively. The optimal weight vector can be obtained after convergence. The required computational complexity is evaluated for the proposed technique, which is on the order of O(N) and less than that of the conventional LCMV method. The flow chart scheme with the partial parallel structure of the proposed algorithm is introduced. This scheme is easy to be implemented on a distributed computer/digital signal processor (DSP) system to solve the problems of the heavy computational burden and vast data transmission of the large-scale adaptive monopulse array. Then, the monopulse ratio and convergence rate of the proposed algorithm are evaluated by numerical simulations. Compared with some recent adaptive monopulse estimation methods, a better performance on computational complexity and monopulse ratio can be achieved with the proposed adaptive method.     

Joint TDOA and AOA location algorithm

For the joint time difference of arrival(TDOA) and angle of arrival(AOA) location scene,two methods are proposed based on the rectangular coordinates and the polar coordinates,respectively.The problem is solved perfectly by calculating the target position with the joint TDOA and AOA location.On the condition of rectangular coordinates,first of all,it figures out the radial range between target and reference stations,then calculates the location of the target.In the case of polar coordinates,first of all,it figures out the azimuth between target and reference stations,then figures out the radial range between target and reference stations,finally obtains the location of the target.Simultaneously,simulation analyses show that the theoretical analysis is correct,and the proposed methods also provide the application of the joint TDOA and AOA location algorithm with the theoretical basis.     

Grover quantum searching algorithm based on weighted targets

The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal. To solve this problem, a Grover searching algorithm based on weighted targets is proposed. First, each target is endowed a weight coefficient according to its importance. Applying these different weight coefficients, the targets are represented as quantum superposition states. Second, the novel Grover searching algorithm based on the quantum superposition of the weighted targets is constructed. Using this algorithm, the probability of getting each target can be approximated to the corresponding weight coefficient, which shows the flexibility of this algorithm. Finally, the validity of the algorithm is proved by a simple searching example.     

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.     

Method of target tracking with Doppler blind zone constraint

Doppler blind zone （DBZ） has a bad influence on the airborne early warning radar, although it has good detection performance for low altitude targets with pulse Doppler （PD） technology. In target tracking, the blind zone can cause target tracking breakage easily. In order to solve this problem, a parallel particle filter （PF） algorithm based on multi-hypothesis motion models （MHMMs） is proposed. The algorithm produces multiple possible target motion models according to the DBZ constraint. Particles are updated with the constraint in each motion model. Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model, the measurementtrack association succeeds and track breakage is avoided. The simulation results show that on the condition of different DBZ ranges, a high association ratio can be got for targets with different maneuverability levels, which accordingly improves the tracking quality.     

基于改进粒子滤波的运动平台定位算法

研究了运动平台对辐射源目标的定位问题。该问题是一个典型的非线性问题,因此很多定位算法对目标位置初始值的选择比较敏感,甚至造成运算结果不收敛。为解决这个问题,首先提出了一种最小均方误差预滤波改进粒子滤波算法,并将改进算法应用于运动平台对辐射源目标的定位,取得了比较好的效果。

Abstract：

The problem of emitter target location was considered using a moving platform. Because this problem is a non-linear problem,many position algorithms are too sensitive to the initial coordinate value of emitter target. The accurate position is difficult to estimate. To solve this problem,a modified particle filter approach based on Minimum Mean Square Error was proposed. Then the modified method was applied on the problem of emitter target location. Its performance is better in the simulation.     

Narrow-band tomographic radar imaging of precession cone targets

The principle and method of the narrow-band tomo-graphic radar imaging (NBTRI) of the precession cone target are studied. Firstly, the motion model and electromagnetic scattering characteristics of the precession cone target are introduced. Secondly, based on the traditional NBTRI algorithm, a novel narrow-band tomography clean radar imaging (NBTCRI) algorithm is proposed to enhance the image quality of NBTRI results. In addition, the resolution performance of the NBTRI algorithm is analyzed. Finally, based on the ideal scattering point model, this paper discusses the relationship between the precession angle and the estimated target size from NBTRI results. By using the target’s chamber data, NBTRI and NBTCRI results of the precession cone target are further analyzed, which indicates the effectiveness of the proposed method.     

Robust location algorithm for NLOS environments

One of the main problems facing accurate location in wireless communication systems is non-line-of- sight （NLOS） propagation. Traditional location algorithms are based on classical techniques under minimizing a least-squares objective function and it loses optimality when the NLOS error distribution deviates from Gaussian distribution. An effective location algorithm based on a robust objective function is proposed to mitigate NLOS errors. The proposed method does not require the prior knowledge of the NLOS error distribution and can give a closed-form solution. A comparison is performed in different NLOS environments between the proposed algorithm and two additional ones （LS method and Chan＇s method with an NLOS correction）. The proposed algorithm clearly outperforms the other two.     

Multiple interferences suppression with space-polarization null-decoupling for polarimetric array

The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain.When there is the interference in mainlobe,it will cause serious mainlobe distortion,that the target detection suffers from.To overcome this problem and make radar cope with the complex multiple interferences scenarios,we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar.Specifically,the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization(DoP),and a filtering criterion based on the proposed linear constraint.The signal preprocessing provides the accurate estimated parameters of the interference,which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe.The proposed method can reduce the mainlobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences.The effectiveness of the proposed method is verified by simulations.     

基于合作目标的外辐射源雷达发射站直接定位

外辐射源雷达利用已知位置信息的第三方发射站发射的信号作为照射源,对运动目标进行探测定位。但在实际应用中,发射站精确位置信息往往难以预先获取,给外辐射源雷达在未知环境中的快速应用带来困难。为了解决该问题,将已知轨迹的无人机作为合作目标。利用实时获取的无人机位置信息,建立了发射站位置未知时的目标直接定位模型。并进一步构建了基于目标位置的代价函数,从而实现对发射站位置的精确估计。该方法不仅可以克服常用两步定位算法中存在信息损失的不足,可达到更高的定位精度,而且利用了外辐射源雷达直达波信号,较传统直接定位方法性能更优。最后,通过仿真验证了该算法的有效性。     

基于混沌序列的MIMO雷达互补相位编码波形设计

多发多收(multiple-input multiple-output,MIMO)雷达使用相互正交的波形作为发射波形。用混沌序列可以在短时间内产生任意数量、任意长度的正交混沌相位编码波形。由于互相关和自相关旁瓣的影响,混沌相位编码波形作为发射信号时的脉冲压缩输出具有较高的距离旁瓣。提出一种通过遗传算法联合迭代算法,搜索设计混沌相位编码发射波形的最优互补波形的方法,使得这种互补发射结构能够有效地抑制高脉冲压缩输出距离旁瓣,从而提高MIMO雷达的目标检测性能,特别是对相邻弱小目标的检测性能。仿真部分基于4种常用混沌序列,与相关文献中波形进行比较,证明了该方法的有效性。     

基于发射分集的MIMO雷达相参信号处理方法

针对多个运动的慢起伏目标情况,研究了发射分集多入多出(multiple input multiple output,MIMO)雷达的相参信号处理方法.提出了一种重复利用回波数据的目标参数估计算法.发射分集MIMO雷达的发射阵元间距满足空间分集条件,其接收端采用阵元间距为半波长的均匀线阵.按距离门对其回波信号进行脉压-Capon谱估计,可提取出各目标对应不同发射阵元的初步位置信息.然后利用回波数据及目标的初步位置信息,把同一目标对应不同发射阵元的脉压数据矢量经多普勒频移和相位补偿后相加,再进行Capon谱估计,获得高精度的目标方位角估计值.仿真结果表明,该算法可对雷达观测区域内的多个动目标进行高精度定位.     

基于时间差的无线传感器网络节点定位方法

在分析RSSI、ToA、TDoA和AoA测距方法优缺点的基础上,给出一种基于时间差的节点定位方法.该方法无需锚节点与锚节点之间,以及锚节点与目标节点之间的时间同步,根据三个锚节点的定位信号到达目标节点的时间差,即可完成对目标节点的定位.以三个锚节点呈等边直角三角形布置为例,对定位误差进行计算并得出了提高目标节点时钟精度和增加锚节点之间的距离可减小定位误差的结论.最后,利用无线传感器网络测试床进行算法有效性验证,结果表明了所提定位算法的有效性.     

改进的分形算法在弱小目标检测中的应用

针对单帧图像中的弱小目标检测问题,提出了一种改进的基于分形的快速检测方法。该方法首先利用图像的局部熵信息对目标进行粗定位,以得到一个包含目标的感兴趣区域,然后利用分形理论构造该区域的分维像,最后对分维像采用自适应阈值分割即可将弱小目标精确检测出来。与传统分形算法相比,提出的改进算法包含粗定位和细定位两部分,它将分形算法要处理的区域缩减到局部熵所估计的小范围内,从而克服了传统分形方法计算量大、抗噪性差的缺点。仿真实验结果表明,该方法能够稳健、快速、有效地检测弱小目标。     

基于三维模型的多传感车辆定位与识别研究

车辆的定位与识别是智能车研究的重要环节。针对二维算法及单传感的不足,提出了一种基于三维模型的多传感车辆定位与识别算法。定位系统中,通过将雷达的位置估计和图像的灰度信息进行融合来获得位移参数,并将改进的Hausdorff距离和模拟退火算法相结合得到旋转参数。在此基础上,识别系统计算每个模型与目标车辆的匹配值,根据其在采样时段内的平均值确定车辆类型。实验证明,该方法能有效地实现目标车辆的定位和识别。     

基于反双曲正切函数的跟踪微分器设计与应用

通过引入反双曲函数与终端吸引子函数,构造了一种新型跟踪微分器。反双曲正切函数在平衡点较近处优异的线性特性可以保证系统收敛的平滑性,远离平衡点处的非线性特性能保证收敛的快速性。通过引入终端吸引子函数,降低了高频信号引起的抖振,增强了噪声抑制能力。通过扫频测试总结得出了参数整定规则后,与典型的跟踪微分器进行对比仿真,测试了所设计新型跟踪微分器的性能。最后,在控制器设计时,基于所提出的新型跟踪微分器构造了干扰观测器。通过仿真对比,证明所构造的干扰观测器实现了对模型不确定项的有效估计。     

复杂地形下传感器网络中传感器的放置模型与算法

有效的传感器放置能改善网络覆盖、减小网络中的通讯量,是传感器组网过程中必须解决的关键问题.研究和分析了不同网格结构的性能,建立了用于传感器放置的地形模型,提出了-种基于矢量地图数据的传感器放置算法.算法以优化传感器数目为目标,将传感器放置在能带来最大有效变化的网格节点上.仿真实际表明,与现有传感器放置算法相比,算法具有更高的效率.     

基于滤波器网格失配的分布式相参雷达目标参数估计方法

针对分布式相参雷达(distributed coherent aperture radar, DCAR)精确的目标参数估计问题,首先建立了以多普勒分复用(Doppler division multiple access, DDMA)波形作为发射波形、存在滤波器网格失配的DCAR信号模型,接着分析并验证了滤波器网格失配严重影响目标参数估计进而降低DCAR信号相参合成性能,最后提出了联合全局-局域搜索和基于稀疏傅里叶变换(sparse Fourier transform, SFT)的两种DCAR目标参数估计方法来降低滤波器网格失配,联合全局-局域搜索的方法通过对滤波器局域加密的方式降低网格失配。为了降低运算量,利用目标信号频率相对于整体频域是稀疏的特性,采用基于SFT搜索的方法通过梯度下降的方式避免滤波器的遍历搜索。仿真实验验证了所提方法的有效性。     

基于TOA和方位角测量的三维运动目标被动定位跟踪算法研究

本文提出了一种基于TOA(到达时间)和方位角测量的算法,详细分析了该技术的定位原理和可行性。在某些接收机以被动方法对三维运动目标定位跟踪的双基地雷达系统中,往往难以测量目标的俯仰角;因此本文提出的算法对于双基地被动定位系统的实用性明显地具有很重要的意义。文中通过典型目标航迹的计算机仿真 (按照Monte Carlo方法)对算法的性能做了评估。