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Tracking maneuvering target based on neural fuzzy network with incremental neural leaning

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Journal of Systems Engineering and Electronics 2006年第2期17卷 343-349页

作者： Liu Mei quan taifan Yao Tianbin Dept. of Electronic and Communication Engineering Harbin Inst. of Technology Harbin 150001 P. R. China

The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

关键词： neural fuzzy network incremental neural learning maneuvering target tracking.

A Tracking Filter in Spherical Coordinates Enhanced by De-noising of Converted Doppler Measurements

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Chinese Journal of Aeronautics 2012年第5期25卷 757-765页

作者： ZHOU Gongjian YU Changjun CUI Naigang quan taifan School of Electronics and Information Engineering Harbin Institute of Technology Harbin 150001 China School of Information and Electronic Engineering Harbin Institute of Technology at Weihai Weihai 264209 China School of Astronautics Harbin Institute of Technology Harbin 150001 China

Tracking problem in spherical coordinates with range rate （Doppler） measurements, which would have errors correlated to the range measurement errors, is investigated in this paper. The converted Doppler measurements, constructed by the product of the Doppler measurements and range measurements, are used to replace the original Doppler measurements. A de-noising method based on an unbiased Kalman filter （KF） is proposed to reduce the converted Doppler measurement errors before updating the target states for the constant velocity （CV） model. The states from the de-noising filter are then combined with the Cartesian states from the converted measurement Kalman filter （CMKF） to produce final state estimates. The nonlinearity of the de-noising filter states are handled by expanding them around the Cartesian states from the CMKF in a Taylor series up to the second order term. In the mean time, the correlation between the two filters caused by the common range measurements is handled by a mini- mum mean squared error （MMSE） estimation-based method. These result in a new tracking filter, CMDN-EKF2. Monte Carlo simulations demonstrate that the proposed tracking filter can provide efficient and robust performance with a modest computa- tional cost.

关键词： target tracking converted Doppler range rate nonlinear Kalman filtering

Sequential nonlinear tracking filter without requirement of measurement decorrelation

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Journal of Systems Engineering and Electronics 2015年第6期26卷 1135-1141页

作者： Gongjian Zhou Junhao Xie Rongqing Xu taifan quan School of Electronics and Information Engineering Harbin Institute of Technology

Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.

关键词： sequential filter Doppler measurement measurementdecorrelation minimum mean squared error (MMSE).

Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning

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Journal of Systems Engineering and Electronics 2006年第3期17卷 495-501页

作者： Zhang Yinan Sun Qingwei quan He Jin Yonggao quan taifan Dept. of Electronics and Communication Engineering Harbin Inst. of Technology Harbin 150001 P. R. China Mobile Communications Corporation of China Harbin 150001 P. R. China Dept. of Electronics and Information Engineering of Yanbian Univ. Yanbian 617100 P. R. China

In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.

关键词： uncertain information information fusion neural networks fuzzy inference robust estimate.

Particle filter initialization in non-linear non-Gaussian radar target tracking

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Journal of Systems Engineering and Electronics 2007年第3期18卷 491-496页

作者： Wang Jian Jin Yonggao Dai Dingzhang Dong Huachun quan taifan Dept. of Electronics and Communication Engineering Harbin Inst. of Technology Harbin 150001 P. R. China Dept. of Electronics and Information Engineering Yanbian Univ. Yanbian 133002 P. R. China

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

关键词： radar target tracking particle filter initialization.

HF radar sea clutter rejection by nonlinear projections

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Journal of Systems Engineering and Electronics 2005年第4期16卷 733-737页

作者： Zhou Gongjian Jin Yonggao Dong Huachun quan taifan Inst. of Electronic Engineering Harbin Inst. of Technology Harbin 150001 P. R. China Dept. of Electronic Information Engineering Coll. of Science and Engineering Yanbian Univ. Yanji 133002 P. R. China

In the background of signal detection for high frequency （I/F） radar, the sea clutter is quite significant and can mask some weak target signals. A new clutter rejection method named “nonlinear projection” is given to improve the SNR of the target. This approach is based on the recent observation that HF sea clutter may be modeled as a nonlinear deterministic dynamical system. After approximating the multidimensional reconstruction of the clutter by a low-dimensional attractor, projections onto this attractor can separate the clutter from other components. Real sea clutter, simulated target data and real target data are used to show that a nonlinear clutter rejection method is a promising technique to suppress sea clutter and enhances target detection.

关键词： HF radar target detection clutter rejection nonlinear projection.

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高频雷达海杂波混沌特性的实验研究

高技术通讯 2000年第3期10卷 41-44页

作者：董华春刘梅权太范哈尔滨工业大学电子工程技术研究所哈尔滨150001

利用高频地波雷达实测数据 ,从实验角度应用动力学方法对高频海杂波的动力特性作出分析 ,首次得到了高频海杂波呈现混沌特性的结论。这一结论对高频雷达目标探测和海态遥感以及海洋气象的研究都具有重要意义。

关键词： HF雷达海杂波混沌分形信号检测处理

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提高目标跟踪批数的一种方法

哈尔滨工业大学学报 1989年第5期21卷 32-37页

作者：刘永坦权太范哈尔滨工业大学电子工程技术研究所

目标跟踪批数是多目标跟踪系统的一个重要指标.本文提出了应用奇异值分解定理,建立降阶模型,并在保证跟踪精度下,调节观测数据采样周期,提高目标跟踪批数的方法.为此本文提出了二次新息序列概念,并利用它进行目标机动特性的判别.本文为... 详细信息

目标跟踪批数是多目标跟踪系统的一个重要指标.本文提出了应用奇异值分解定理,建立降阶模型,并在保证跟踪精度下,调节观测数据采样周期,提高目标跟踪批数的方法.为此本文提出了二次新息序列概念,并利用它进行目标机动特性的判别.本文为提高目标跟踪批数提供了一种有效方法.

关键词：多目标跟踪雷达奇异值分解批数

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高频雷达抗瞬时干扰研究

现代雷达 1999年第2期21卷 1-6页

作者：权太范李健巍哈尔滨工业大学电子工程研究所

高频雷达（ＨＦＲａｄａｒ）作为指示超视距目标的有效手段在海防武器系统中担当重要的角色。高频段电磁环境恶劣，各种瞬时干扰破坏了ＨＦ雷达的正常工作。本文首次揭示了瞬时干扰在ＨＦ雷达多普勒域呈现的奇异性特征，提出了利用小波... 详细信息

高频雷达（ＨＦＲａｄａｒ）作为指示超视距目标的有效手段在海防武器系统中担当重要的角色。高频段电磁环境恶劣，各种瞬时干扰破坏了ＨＦ雷达的正常工作。本文首次揭示了瞬时干扰在ＨＦ雷达多普勒域呈现的奇异性特征，提出了利用小波变换在多普勒域检测这种干扰的方法，并在此基础上进一步提出基于线性预测模型的多普勒信号恢复方法，成功地抑制了瞬时干扰对高频雷达的影响。现场实验表明，本文提出的方案有效地提高了ＨＦ雷达抗瞬时干扰的能力。

关键词：高频雷达小波变换多普勒信号 AR模型