基于多级模糊综合评判的雷达网目标识别技术

由于目前的战场环境非常复杂,目标识别具有重要意义。有关雷达的基于模糊综合评判的目标识别技术目前研究比较多,通过类似的方法可以进一步进行雷达网的目标识别,即采取两个单级模糊综合评判方法进行雷达网的目标识别,但这种方法具有一定的弱点,文中根据雷达网探测目标的特点,提出了一种基于多级模糊综合评判的雷达网目标识别方法。对这种方法与雷达网基于两个单级模糊综合评判进行目标识别的方法进行了仿真比较,结果表明前者较之后者具有计算量小、识别精确、便于工程应用的特点。     

一种基于观测站数目最小化的TDOA/FDOA无源定位算法

在三维（3D）运动目标无源定位系统中,无模糊定位最少需要4个观测站。而传统的两步加权最小二乘（TSWLS）及其改进的闭式算法至少需要5个观测站进行求解,当减少一个观测站时,这些闭式算法往往无法提供可靠解。针对这一问题,提出一种最小化观测站数目的到达时间差（TDOA）与到达频率差（FDOA）定位算法。该算法是一种闭式解法并且能够在三维场景下仅使用4个观测站进行定位。该算法分为两步：第1步分离传统的TSWLS算法中未知参数空间,建立了一组新的等式,并且利用加权最小二乘（WLS）算法得到目标位置与速度的初始值;第2步利用泰勒级数展开算法将中间变量线性化,对目标位置和速度初始值进一步校正。理论分析证明了在适当的噪声水平下该算法能够达到克拉美罗界（CRLB）。此外,计算机仿真表明仅使用4个观测站时,该算法对于近场以及远场目标参数的估计精度在测量噪声较小时可以实现CRLB;并且还表明使用5个观测站估计时,该算法比TSWLS及其改进算法能更好地适应大的测量噪声。     

雷达网对压制式干扰的识别和对目标定位

针对威胁雷达的各种干扰进行分析,得出压制式干扰是雷达网的主要威胁干扰。根据压制式干扰的特征以及雷达网中雷达工作方式,对支援干扰(SOJ)、随队干扰(ESJ)、自卫干扰(SSJ)进行识别。文中给出对目标、干扰机进行定位跟踪的方法,为雷达网跟踪目标提供一定的依据。     

基于傅里叶变换的航迹对准关联算法

研究了在组网雷达存在系统误差情况下的目标航迹关联问题,理论分析了雷达系统误差对目标航迹的影响,并将该影响表示为目标航迹的旋转和平移量。在此基础上,提出了一种基于傅里叶变换的系统误差配准前航迹对准关联算法,该算法将组网雷达的航迹数据看做为一种整体信息,采用傅里叶变换理论来估计和补偿组网雷达目标航迹数据到融合中心航迹数据的相对旋转量和平移量,将雷达网中雷达上报的目标航迹数据对准到融合中心,从而不依赖于估计雷达网系统误差,实现了误差配准前的航迹准确关联,能够为后端的系统误差配准提供可靠的关联目标航迹数据。     

利用重要性采样的时差-频差联合估计算法

针对无源定位中参考信号真实值未知的时差(TDOA)-频差(FDOA)联合估计问题,构建了一种新的时差-频差最大似然(ML)估计模型,并采用重要性采样(IS)方法求解似然函数极大值,得到时差-频差联合估计。算法通过生成时差-频差样本,并统计样本加权均值得到估计值,克服了传统互模糊函数(CAF)算法只能得到时域和频域采样间隔整数倍估计值的问题,且不存在期望最大化(EM)等迭代算法的初值依赖和收敛问题。推导了时差-频差联合估计的克拉美罗下界(CRLB),并通过仿真实验表明,算法的计算复杂度适中,估计精度优于CAF算法和EM算法,在不同信噪比条件下估计误差接近CRLB。     

不完全量测下一类非线性光电跟踪系统滤波器设计

 随着不完全量测条件下探测概率的下降,传统光电跟踪系统的跟踪性能显著降低。为此,本文考虑将俯仰和偏航两个方向的角速度量测引入传统光电跟踪系统,并设计了不完全量测下基于置信度融合的目标跟踪滤波器。首先针对这类新型的光电跟踪系统建立了系统的量测模型,利用嵌套条件方法推导了转换量测误差前两阶矩的一致性估计;然后针对位置探测通道与角速度探测通道的4种数据探测情形设计了4个子滤波器,并根据探测通道的探测情况计算出各子滤波器的置信度,进而对各子滤波器的输出按置信度进行加权融合,得到了跟踪滤波器的全局输出;最后给出了非线性跟踪系统统计意义下的Cramer-Rao下界(CRLB)。Monte-Carlo仿真表明：在不完全量测下,相比传统光电跟踪系统,附加角速度量测的光电跟踪系统的跟踪性能有了显著提高,并且滤波器估计误差均方差(RMSE)已逼近非线性跟踪系统统计意义下的CRLB。     

传感器位置误差条件下的约束总体最小二乘时差定位算法

 现代定位系统中,传感器往往被安放在运动平台上,其位置无法精确得知,存在估计误差,将严重影响对目标的定位精度。针对这一问题,提出基于约束总体最小二乘(CTLS)的到达时差(TDOA)定位算法。首先通过引入中间变量,将非线性TDOA定位方程转化为伪线性方程,再利用CTLS技术,全面考虑伪线性方程所有系数中的噪声。在此基础上推导了定位方程的目标函数,再根据牛顿迭代方法,进行数值迭代,快速得到精确解。采用一阶小噪声扰动分析方法,对该算法的理论性能进行了分析,证明了算法的无偏性和逼近克拉美-罗下限(CRLB)。仿真实验表明,该算法克服了现有总体最小二乘(TLS)算法不能达到CRLB、两步加权最小二乘(two-step WLS)算法在较高噪声时性能发散的缺陷,在较高噪声时定位精度仍然能达到CRLB。     

雷达网中采用的关键技术研究

分析了军用雷达面临的四大威胁,指出了雷达网的优势及组成,对雷达网中的新技术进行了研究。     

雷达站站址误差对多机协同航迹欺骗干扰的影响分析

多机协同航迹欺骗干扰是专门针对雷达网的一种新的电子干扰手段。文章在介绍了多机协同航迹欺骗干扰基本原理的基础上,重点对雷达网内雷达站址误差对航迹欺骗干扰的影响进行了分析,推导了有关理论模型,并以融合中心采用K近似域(K-NN)航迹关联准则为背景,仿真分析了雷达站址误差对航迹欺骗干扰的影响,得出了雷达站址误差对航迹欺骗干扰性能有重要影响、但此影响随电子战飞机与雷达站间的距离增大而降低的结论。     

防空雷达网体系结构与关键技术

提出了区域防空雷达网应具有的功能体系结构、类型配置体系结构及数据处理体系结构,并对雷达网数据接入、数据融合以及网内雷达实时控制与管理技术进行了分析.     

Accurate single-observer passive coherent location estimation based on TDOA and DOA

This paper investigates the problem of target position estimation with a single-observer passive coherent location(PCL) system. An approach that combines angle with time difference of arrival(ATDOA) is used to estimate the location of a target. Compared with the TDOA-only method which needs two steps, the proposed method estimates the target position more directly. The constrained total least squares(CTLS) technique is applied in this approach. It achieves the Cramer–Rao lower bound(CRLB) when the parameter measurements are subject to small Gaussian-distributed errors. Performance analysis and the CRLB of this approach are also studied. Theory verifies that the ATDOA method gets a lower CRLB than the TDOA-only method with the same TDOA measuring error. It can also be seen that the position of the target affects estimating precision.At the same time, the locations of transmitters affect the precision and its gradient direction.Compared with the TDOA, the ATDOA method can obtain more precise target position estimation.Furthermore, the proposed method accomplishes target position estimation with a single transmitter,while the TDOA-only method needs at least four transmitters to get the target position. Furthermore,the transmitters' position errors also affect precision of estimation regularly.     

基于变分贝叶斯的星载雷达非线性滤波

星载雷达由于其探测范围广、距离远、全天候等优点，在预警防御系统中占有十分重要的地位。然而，由于观测平台的高速运动以及摄动干扰、传感器观测非线性等问题，使得星载雷达目标高精度跟踪带来严峻挑战。针对星载雷达非线性状态估计问题，采用一种基于变分贝叶斯的非线性滤波方法，该方法通过将非线性状态估计问题转化为优化问题，通过迭代优化获得了闭环解析解。此外，针对坐标变换中俯仰角量测缺失问题，提出了一种基于先验目标高度的俯仰角估计方法。通过数值仿真，验证了所提方法较传统非线性滤波方法，如扩展卡尔曼滤波、不敏卡尔曼滤波、转换量测卡尔曼滤波，具有更好的估计精度。     

Monopulse DOA estimation of two unresolved Rayleigh targets

This paper provides for new approaches to the processing of unresolved measurements as two direction-of-arrival (DOA) measurements for tracking closely spaced targets rather than the conventional single DOA measurement of the centroid. The measurements of the two-closely spaced targets are merged when the target echoes are not resolved in angle, range, or radial velocity (i.e., Doppler processing). The conditional Cramer Rao lower bound (CRLB) is developed for the DOA estimation of two unresolved Rayleigh targets using a standard monopulse radar. Then the modified CRLB is used to give insight into the boresight pointing for monopulse DOA estimation of two unresolved targets. Monopulse processing is considered for DOA estimation of two unresolved Rayleigh targets with known or estimated relative radar cross section (RCS). The performance of the DOA estimator is studied via Monte Carlo simulations and compared with the modified CRLB     

Precision large scale air traffic surveillance using IMM/assignmentestimators

We present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on interacting multiple model (IMM) state estimation combined with a 2-dimensional assignment for data association. The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FAA radars, which are asynchronous, heterogeneous, and geographically distributed over a large area. Both secondary radar data (beacon returns from cooperative targets) as well as primary radar data (skin returns from noncooperative targets) are used. The target IDs from the beacon returns are not used in the data association. The surveillance region includes about 800 targets that exhibit different types of motion. The performance of an IMM estimator with linear motion models is compared with that of the Kalman filter (KF). A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown that the IMM estimator performs better than the KF. The advantage of fusing multisensor data is quantified. It is also shown that the computational requirements in the multisensor case are lower than in single sensor case, Finally, an IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods over the IMM with linear models     

Moving target localization in distributed MIMO radar with transmitter and receiver location uncertainties

In this paper, the problem of moving target localization from Bistatic Range(BR) and Bistatic Range Rate(BRR) measurements in a Multiple-Input Multiple-Output(MIMO) radar system having widely separated antennas is investigated. We consider a practically motivated scenario,where the accurate knowledge of transmitter and receiver locations is not known and only the nominal values are available for processing. With the transmitter and receiver location uncertainties,which are usually neglected in MIMO radar systems by prior studies, taken into account in the measurement model, we develop a novel algebraic solution to reduce the estimation error for moving target localization. The proposed algorithm is based on the pseudolinear set of equations and two-step weighted least squares estimation. The Cramer-Rao Lower Bound(CRLB) is derived in the presence of transmitter and receiver location uncertainties. Theoretical accuracy analysis demonstrates that the proposed solution attains the CRLB, and numerical examples show that the proposed solution achieves significant performance improvement over the existing algorithms.     

Dynamic Cramer-Rao bound for target tracking in clutter

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.     

Accuracy of angle estimation with monopulse processing using twobeams

Expressions are provided for the accuracy of monopulse angle estimation using two beams. It is shown that, if the signal angle is halfway between the angles of the beams, the Cramer-Rao lower bound (CRLB) for monopulse processing is almost as small as the CRLB obtained if the entire array of sensors is used. The monopulse CRLB is considerably poorer if the angle of the signal is equal to that of one of the two beams. The expressions in this correspondence are for a uniformly weighted linear array of M equally spaced sensors, for which N⩾M beams are formed     

Radar power multiplier for acquisition of low observables using anESA radar

In this paper the acquisition of a low observable (LO) incoming tactical ballistic missile using the measurements from a surface based electronically scanned array (ESA) radar is presented. We present a batch maximum likelihood (ML) estimator to acquire the missile while it is exo-atmospheric. The proposed estimator, which combines ML estimation with the probabilistic data association (PDA) approach resulting in the ML-PDA algorithm to handle false alarms, also uses target features. The use of features facilitates target acquisition under low signal-to-noise ratio (SNR) conditions. Typically, ESA radars operate at 13-20 dB, whereas the new estimator is shown to be effective even at 4 dB SNR (in a resolution cell, at the end of the signal processing chain) for a Swerling III fluctuating target, which represents a significant counter-stealth capability. That is, this algorithm acts as an effective “power multiplier” for the radar by about an order of magnitude. An approximate Cramer-Rao lower bound (CRLB), quantifying the attainable estimation accuracies and shown to be met by the proposed estimator, is derived as well     

Multisensor multitarget bias estimation for general asynchronous sensors

A novel solution is provided for the bias estimation problem in multiple asynchronous sensors using common targets of opportunity. The decoupling between the target state estimation and the sensor bias estimation is achieved without ignoring or approximating the crosscovariance between the state estimate and the bias estimate. The target data reported by the sensors are usually not time-coincident or synchronous due to the different data rates. Since the bias estimation requires time-coincident target data from different sensors, a novel scheme is used to transform the measurements from the different times of the sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow bias estimation as well as the evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases in any scenario. Monte Carlo simulation results show that the new method is statistically efficient, i.e., it meets the CRLB. The use of this technique for scale and sensor location biases in addition to the usual additive biases is also presented.