A Void Reference Sensor-Multiple Signal Classification Algorithm for More Accurate Direction of Arrival Estimation of Low Altitude Target

There exists MUSIC （multiple signal classification） algorithm for direction of arrival （DOA） estimation. This paper is to present a different MUSIC algorithm for more accurate estimation of low altitude target. The possibility of better performance is analyzed using a void reference sensor （VRS） in MUSIC algorithm. The following two topics are discussed： 1） the time delay formula and VRS-MUSIC algorithm with VRS located on the minus of z-axes; 2） the DOA estimation results of VRS-MUSIC and MUSIC algorithms. The simulation results show VRS-MUSIC algorithm has three advantages compared with MUSIC： 1） When the signal to noise ratio （SNR） is more than - 5 dB, the direction estimation error is 1/2 as much as that obtained by MUSIC; 2） The side lobe is more lower and the stability is better; 3） The size of array that the algorithm requires is smaller.     

Detection Algorithm for LFM Echo of Underwater Moving Targets Based on Discrete Fractional Fourier Transform

The mismatch between echo and replica caused by underwater moving target（UMT）＇s radial velocity degrades the detection performance of the matched filter（MF） for the linear frequency modulation（LFM） signal. By using the focusing property of fractional Fourier transform（FRFT） to that signal, a detection algorithm for UMT＇s LFM echo based on the discrete fractional Fourier transform（DFRFT） is proposed. This algorithm is less affected by the target＇s radial velocity compared with the other MF detection algorithm utilizing zero radial velocity replica（ZRVR）, and the mathematical relation between the output peak positions of these two algorithms exists in the case of existence of target echo. The algorithm can also estimate the target distance by using this relation. The simulation and experiment show that this algorithm＇s detection performance is better than or equivalent to that of the other MF algorithm utilizing ZRVR for the LFM echo of UMT with unknown radial velocity under reverberation noise background.     

LNN Blind Multi-user Detection Algorithm for Multi-path-fading CDMA Channels

A blind multi-user detection algorithm （BMUD） which is suitable for multi-path-fading Channels based on Lagrange neural network （LNN） is proposed. Based on the minimum output energy （MOE） criterion, the blind detection algorithm is formulated as a constrained optimization problem inherently and is then resolved efficiently using the neural network . Compared with the previous RLS（recursive least squares ）-MOE blind detection algorithm or for multi-path channel, the BMUD based on LNN has better performances： lower computational complexity, faster convergence speed and capability in the multi-path-fading channel. The bit error rate （BER） and signal-to-interference-and-noise ratio （SINR） performances of the detection algorithm in multi-path channel are close to that in single path channel.     

A Correlation Tracking Algorithm Based on Template Partition Motion Estimation

A correlation tracking algorithm based on template partition motion estimation proposed for improving real time performance of the conventional correlation matching algorithms. The target trajectory fitted using the least square with equal space in whole interval and the target prediction point is found out. According to the requirements of block motion estimation （BME） algorithm, the template divided into some macro blocks. The searching process is conducted by using diamond search algorithm around the prediction point and the optimal motion vector of each block is calculated. A point corresponding to the motion vector with the best matching is taken as a rough matching point of the template. The rela- tion of relative position between the block with matching point and the searching area determined to decide whether to conduct precise matching search or to construct a new search area in the gradient direction. The target tracking experiment results show that over 70 % time cost can be reduced caompared with the conventional correlation matching algorithm based on full search method.     

Underwater Localization for Multiple AUVs Based on Bearing and Range Measurements

A novel underwater localization algorithm for autonomous underwater vehicle （AUVs） is proposed. Taking aim at the high cost of the traditional ＂leader-follower＂ positioning, a ＂parallel＂ model is adopted to describe the localization problem. Under an unknown-but-bounded assumption for sensor noise, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merged these constraints, a convex polyhedron representing the set of all configurations consistent with the sensor measurements can be induced. Estimates for the,uncertainty in the position of a single AUV or the relative positions of two or more AUVs can then be obtained by projecting this polyhedron into appropriate subspaces of the configuration space. The localization uncertain region for each AUV can be recovered by an approximation algorithm to realize underwater localization for multiple AUVs. The deduced theoretically and the simulated results show that it is an economical and practical localization method for the AUV swarm.     

Turbo-DFH Coding and Iterative Decoding

We combine the Turbo codes with differential frequency hopping （DFH） technique and propose a new technique called Turbo-DFH coding. The structures of Turbo-DFH encoder and decoder are given. Taking aim at characteristic of Turbo-DFH system, the modified Log-MAP algorithm is presented and used for iterative decoding of Turbo-DFH decoder. Simulation results show that because the ideas of random coding and iterative decoding are used, the bit error rate （BER） performance of the Turbo-DFH system is better than that of the conventional DFH system.     

An Improved Scene-based Nonuniformity Correction Algorithm for Infrared Focal Plane Arrays Using Neural Networks

The improved scene-based adaptive nonuniformity correction （NUC） algorithms using a neural network （NNT） approach for infrared image sequences are presented and analyzed. The retlna-like neural networks using steepest descent model was the first proposed infrared focal plane arrays （IRFPA） nonuniformity compensation method, which can perform parameter estimation of the sensors over time on a frame by frame basis. To increase the strength and the robustness of the NNT algorithm and to avoid the presence of ghosting artifacts, some optimization techniques, including momentum term, regularization factor and adaptive learning rate, were executed in the parameter learning process. In this paper, the local median filtering result of Xij （ n ） is proposed as an alternative value of desired network output of neuron Xij （ n ）, denoted as Tij （ n ）, which is the local spatial average of Xij （ n ） in traditional NNT methods. Noticeably, the NUC algorithm is inter-frame adaptive in nature and does not rely on any statistical assumptions on the scene data in the image sequence. Applications of this algorithm to the simulated video sequences and real infrared data taken with PV320 show that the correction results of image sequence are better than that of using original NNT approach, especially for the short-time image sequences （several hundred frames） subjected to the dense impulse noises with a number of dead or saturated pixels.     

Vibration Severity Monitoring and Evaluation of Armored Vehicle Transmission

Vibration monitoring and vibration severity evaluation of armored vehicle transmission are realized by additional sensors. An algorithm of vibration severity in frequency domain is presented. The algorithm has powerful applicability for signal type and flexible selectivity for frequency range, and avoids the processing of signal conversion used calculus and filtering compared to the algorithm of vibration severity in time domain. An applied example is given in company with attentive proceedings and measures for improving evaluation effect.     

An Automatic Interference Recognition Method in Spread Spectrum Communication System

An algorithm to detect and recognize interferences embedded in a direct sequence spread spectrum （DSSS） communication system is proposed. Based on Welch＇ s averaging modified periodogram method and fractional Fourier transfor- mation （FRFT）, the paper proposes a decision tree-based algorithm in which a set of decision criteria for identifying different types of interferences is developed. Simulation results demonstrate that the proposed algorithm provides a high recognition rate and is robust for various ISR and SNR.     

Super-resolution Restoration of Remote-sensing Images

A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint （MPMAP） combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that superresolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function （PSF） value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.     

基于线性最小二乘估计的双基地声纳定位优化算法

针对双基地声纳系统存在冗余信息的问题,基于线性最小二乘估计理论提出了一种定位优化算法,建立了T/R？R型声纳系统水下定位的数学模型。通过MATLAB软件的仿真分析,给出了影响该算法性能的各个因素,并与传统TOL算法进行了对比。仿真结果表明,采用线性最小二乘估计算法能更好地利用系统测量的冗余信息,显著提高双基地声纳基线区和侧边区的定位精度。该算法具有受基线长度和站址误差影响小等优点,是一种具有工程应用价值的双基地声纳定位算法。本文的研究可为多基地声纳定位优化问题的研究提供理论基础。     

双基地加权最小二乘估计算法定位精度研究

本文通过将双基地声呐中发射和接收站的测量数据进行融合处理,提出了基于加权最小二乘估计(WLS)的双基地声呐定位优化算法,给出了算法的原理和误差分布.通过数值仿真,研究了系统参数对算法定位精度的影响,给出了不同条件下定位误差的几何分布.并将该算法与单纯基于声波到达时间算法的定位性能进行了比较.仿真结果表明,双基地WLS算法充分利用了双基地系统的冗余信息,具有较高的定位精度.该研究为多基地声呐水下目标的探测、定位和跟踪提供了理论依据.     

基于非线性最小二乘的双基地声纳定位优化算法

双基地声纳定位是一种重要的定位方法,采用优化算法来提高定位精度是其重要的研究内容。本文采用了将双基地声纳定位方程转化为非线性最小二乘(NLS)问题的技术方法,并后置最优化方法LMF算法,研究了基于多基地接收源的目标定位性能和定位精度的几何稀释(GDOP),给出了定位误差分布。通过数值仿真,研究了不同基线长度时双基地声纳的定位精度,并将NLS拟和算法的多接收源定位性能与纯时间估计定位性能进行了比较。研究表明,本文的基于多基地接收源的目标定位方法相对于纯时间估计定位方法提高了定位精度,特别是在相对短的基线(7.4 km基线长度)情况下,可提高30%的精度。该研究为最优化方法在水声定位中的应用提供了依据。     

双基地声纳混响建模分析

在浅海环境下,混响是影响双基地声纳系统性能的主要因素,基于双基地声纳的几何特点,对接收端的混响进行了建模,并在接收端具有指向性的条件下,得出了混响面积以及接收端信混比的计算公式,对不同条件下接收端的混响强度以及信混比的变化进行了仿真分析,得到了不同条件下双基地声纳接收混响强度与收发位置、信号脉宽以及接受指向性的关系。     

多基地声纳配置策略研究

本文提出了一种基于检测覆盖范围的多基地声纳配置策略。首先，根据声纳的作用距离公式，分析了双基地声纳的检测覆盖范围特性，它为卡西尼卵形线包围的区域；然后，将此特性推广到三基地和多基地声纳，指出多基地声纳的检测覆盖范围主要与发射、接收机的位置有关，且检测覆盖区域主要集中在发射、接收机附近；最后，在对多基地声纳模型进行部分简化的基础上，提出一种用圆来代替检测覆盖区的多基地声纳配置算法。该算法经过仿真验证，表明配置效果较好。     

双基地声纳探测范围分析

双基地声纳在一定程度上综合了单基地主动和被动声纳的优点,具有扩大探测范围的优势,但并不能够在全方位上都获得探测距离优势,在某些方位上甚至不如等效单基地声纳。在双基地声纳系统中,根据能量关系推导出双基地声纳系统最大可探测范围的数学模型,并进一步定量分析了基线长度、等效半径与探测范围和各个方位上的探测距离的关系,提出了优势区域、优势角的概念。在此基础上,给出了不同情况下双基地声纳的几种优化使用配置方案。     

T2-R型多基地声纳定位精度研究

建立一种T²-R型多基地声纳定位的几何模型,提出利用时间-方位信息进行目标定位（MTBL)算法,并给出算法定位误差的几何分布。通过数值仿真,重点研究了基线长度、声源配置和测量误差对算法定位精度的影响,并对其他影响因素也作了定性分析。仿真分析结果表明：随着基线长度增加,大部分区域定位精度提高;在一定的声源配置方式下,算法精度对时间测量误差比较敏感,而方位测量误差对定位精度影响较小;合理进行声源配置,可以提高探测区域的平均定位精度。该研究为T²-R型多基地声纳的水下定位提供了理论依据。     

基于最佳线性无偏估计的双基地雷达走位方法

利用坐标转换后的伪测量值，根据最佳线性无偏估计原理，提出一种双基地雷达目标的定位方法并给出了算法定位误差的Cramer-Rao理论下限表达式，在不同噪声条件下对算法进行仿真计算，结果表明该方法可以显著提高双基地雷达目标的定位精度，其理论下限和仿真结果都明显优于最小二乘方法。     

声纳低截获技术方法与策略研究

根据声纳低截获因子方程,结合雷达与通信领域低截获经验和声纳自身特点,提出了低截获信号发射和双基地（多基地）两项低截获声纳技术的实现策略。并围绕空、时、频以及能量这4大要素,介绍了低频信号技术、大时间带宽积技术、复合频（码）制技术、随机或非线性体制技术以及时频捷变技术等5项低截获声纳信号设计方法,深入分析了LPI波形的特性,设计出了一些具有良好低截获性能的波形,并以降频梳状信号（几何comb）和非线性调频脉冲串信号（NLFM）为例进行了仿真验证。结果表明,通过这种信号设计方法设计出来的信号具有较强的抗截获性能。     

浅水低频主动声呐数据的探测和跟踪技术

低频主动声呐(LFAS)系统受到浅海海域混响的影响。声音与海底的相互作用引起大量的声呐接触,从而导致高虚警率。提出了一种能够减少由静止物体或噪音引起的声呐接触的分类运算法则,该分类运算法则用于收集海上试验数据。运用接收器工作特性曲线来评价该运算法则的性能。此外,在对数据进行分类计算前后,采用多假设跟踪算法处理。