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雷达网对压制式干扰的识别和对目标定位 总被引:1,自引:1,他引:0
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。 相似文献
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Maybeck P.S. Herrera T.D. Evans R.J. 《IEEE transactions on aerospace and electronic systems》1994,30(3):758-768
A missile target tracker is designed using a filter/correlator (with adaptive target shape identification) based on forward-looking infrared (FLIR) sensor measurements to track the center-of-intensity of the hardbody/plume combination, and another filter using Doppler and/or speckle information in the return from a low-power laser illuminator to estimate the offset between the intensity centroid and the hardbody center-of-mass. The Doppler information is shown to yield smaller bias and error variance from the tracker than the speckle information. Performance of trackers based on just Doppler or both Doppler and speckle information from the laser return is portrayed as a function of important parameters in the tracking environment 相似文献
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基于卫星平台的无源定位系统可以通过卫星的绕地运动和轨道覆盖,实现对全球地面辐射源的被动侦察,具有侦察隐蔽性强、侦测范嗣广和不受地理位置限制的优点。把一种新的修正协方差扩展卡尔曼滤波(MVEKF)方法引入三星对运动目标的时差无源定位跟踪中,克服了EKF受初始状态和测量误差影响大的缺点,也不用像MGEKF一样需要观测方程的修正函数,仿真表明该滤波方法相对来说收敛速度更快,跟踪性能更好。 相似文献
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在以前的研究中,无偏转测量误差协方差阵是基于当前测量值得到的.为了能利用所有历史数据以得到更精确的转换测量误差协方差阵估计,文中在均方意义下,推导了三维雷达的最优无偏转换测量误差协方差阵. 相似文献
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通过对微多普勒效应的研究,提出了一种新的逆合成孔径雷达(ISAR)散射波干扰方法。将拖曳式干扰机和ISAR接收机分别等效为双基ISAR的发射站和接收站,干扰机对截获的ISAR信号进行微动信息调制并转发至目标,由其散射至ISAR接收机产生散射波干扰效果。干扰信号经ISAR接收机处理后可在真实目标回波成像结果附近产生假目标,且在方位向形成干扰条带。实验结果表明:通过控制干扰机转发参数及微动调制参数可分别实现不同的压制干扰效果。由于拖曳式干扰机与目标距离较近,干扰信号可获得较大功率,且与真实目标回波相参,可获得ISAR二维脉冲压缩处理增益,与传统射频噪声压制干扰方法相比成本较小。 相似文献
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卫星导航抗干扰的过程中,对空间信号波达方向估计、干扰个数检测、最优权矢量的求解直接影响着导航接收机的抗干扰性能,而协方差矩阵的特征分解是这些算法实现的核心部分。根据自适应阵列天线获得的协方差矩阵的特性,基于双边并行Jacobi算法,实现了基于FPGA的协方差矩阵特征值和特征向量的求解,并通过在信号波达方向估计的应用进行了验证。另外,在实现的过程中对直接调用CORDIC IP核的方式进行了精度误差分析,并用一种双精度浮点的方式进行修正,提高了矩阵特征分解FPGA的实现精度,为导航抗干扰接收机性能的提升提供了有效的工程基础。 相似文献
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Airborne surveillance radars must detect and localize targets in diverse interference environments consisting of ground clutter, conventional jamming, and terrain scattered jammer multipath. Multidimensional adaptive filtering techniques have been proposed to adaptively cancel this interference. However, a detailed analysis that includes the effects of multipath nonstationarity has been elusive. This work addresses the nonstationary nature of the jammer multipath and its impact on clutter cancellation and target localization. It is shown that the weight updating needed to track this interference will also modulate sidelobe signals. At the very least, this complicates the localization of targets. At the worst, it also greatly complicates the rejection of clutter. Several techniques for improving cancellation of jammer multipath and clutter are proposed, including 1) weight vector interpolation, extrapolation, and updating; 2) filter architecture, constraint, and beamspace selection; 3) prefilters; 4) 3-D STAP architectures; and 5) multidimensional sidelobe target editing 相似文献
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This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter. 相似文献
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Algorithms are presented for managing sensor information to reduce the effects of bias when tracking interacting targets. When targets are close enough together that their measurement validation gates overlap, the measurement from one target can be confused with another. Data association algorithms such as the joint probabilistic data association (JPDA) algorithm can effectively continue to track targets under these conditions, but the target estimates may become biased. A modification of the covariance control approach for sensor management can reduce this effect. Sensors are chosen based on their ability to reduce the extent of measurement gate overlap as judged by a set of heuristic parameters derived in this work. Monte Carlo simulation results show that these are effective methods of reducing target estimate bias in the JPDA algorithm when targets are close together. An analysis of the computational demands of these algorithms shows that while they are computationally demanding, they are not prohibitively so. 相似文献
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Seong-Taek Park Jang Gyu Lee 《IEEE transactions on aerospace and electronic systems》1998,34(4):1337-1344
The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem 相似文献
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Adaptive digital beamforming for angle estimation in jamming 总被引:2,自引:0,他引:2
A radar digital beamforming (DBF) architecture and processing algorithm is described for nulling the signal from a mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The architecture consists of a sidelobe jamming (SLJ) cancelling adaptive array (AA) followed by a mainlobe jamming (MLJ) canceller. A mainlobe maintenance (MLM) technique or constrained adaptation during the sidelobe cancellation process is imposed so that the results of the SLJ cancellation process do not distort the subsequent mainlobe cancellation process. The SLJ signals and the MLJ signals are thus cancelled sequentially in separate processes. This technique was developed for improving radar processing in determining the angular location of a target, and specifically for improving the monopulse technique by maintaining the accuracy of the target echo monopulse ratio in the presence of electronic jamming by adaptive suppression of the jamming signals before forming the monopulse sum and difference beams 相似文献
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Tracking with classification-aided multiframe data association 总被引:7,自引:0,他引:7
Bar-Shalom Y. Kirubarajan T. Gokberk C. 《IEEE transactions on aerospace and electronic systems》2005,41(3):868-878
In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association. 相似文献
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IMMPDAF for radar management and tracking benchmark with ECM 总被引:2,自引:0,他引:2
Kirubarajan T. Bar-Shalom Y. Blair W.D. Watson G.A. 《IEEE transactions on aerospace and electronic systems》1998,34(4):1115-1134
A framework is presented for controlling a phased array radar for tracking highly maneuvering targets in the presence of false alarms (FAs) and electronic countermeasures (ECMs). Algorithms are presented for track formation and maintenance; adaptive selection of target revisit interval, waveform and detection threshold; and neutralizing techniques for ECM, namely, against a standoff jammer (SOJ) and range gate pull off (RGPO). The interacting multiple model (IMM) estimator in combination with the probabilistic data association (PDA) technique is used for tracking. A constant false alarm rate (CFAR) approach is used to adaptively select the detection threshold and radar waveform, countering the effect of jammer-induced false measurements. The revisit interval is selected adaptively, based on the predicted angular innovation standard deviations. This tracker/radar-resource-allocator provides a complete solution to the benchmark problem for target tracking and radar control. Simulation results show an average sampling interval of about 2.5 s while maintaining a track loss less than the maximum allowed 4% 相似文献
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It is shown that partial information about the airborne/spacebased (A/S) clutter covariance matrix (CCM) can be used effectively to significantly enhance the convergence performance of a block-processed space/time adaptive processor (STAP) in a clutter and jamming environment. The partial knowledge of the CCM is based upon the simplified general clutter model (GCM) which has been developed by the airborne radar community. A priori knowledge of parameters which should be readily measurable (but not necessarily accurate) by the radar platform associated with this model is assumed. The GCM generates an assumed CCM. The assumed CCM along with exact knowledge of the thermal noise covariance matrix is used to form a maximum likelihood estimate (MLE) of the unknown interference covariance matrix which is used by the STAP. The new algorithm that employs the a priori clutter and thermal noise covariance information is evaluated using two clutter models: 1) a mismatched GCM, and 2) the high-fidelity Research Laboratory STAP clutter model. For both clutter models, the new algorithm performed significantly better (i.e., converged faster) than the sample matrix inversion (SMI) and fast maximum likelihood (FML) STAP algorithms, the latter of which uses only information about the thermal noise covariance matrix. 相似文献
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针对机动目标跟踪中航迹信息提取精度不高的问题,提出一种ECEF坐标系下基于交互多模型的多机协同跟踪算法。首先,各载机以ECEF坐标系为融合中心对目标量测进行无偏转换处理,以有效减小量测转换误差对目标跟踪的影响;然后,利用交互多模型的方法对目标进行融合跟踪,以进一步提高目标机动时的跟踪精度;最后,通过二次滤波的方法,来有效实现目标航迹信息的精确提取。仿真结果表明,该算法可较好地提高目标机动时的跟踪精度和航迹信息提取精度。 相似文献
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Tonissen S.M. Bar-Shalom Y. 《IEEE transactions on aerospace and electronic systems》1998,34(3):796-809
An important problem in target tracking is the detection and tracking of targets in very low signal-to-noise ratio (SNR) environments. In the past, several approaches have been used, including maximum likelihood. The major novelty of this work is the incorporation of a model for fluctuating target amplitude into the maximum likelihood approach for tracking of constant velocity targets. Coupled with a realistic sensor model, this allows the exploitation of signal correlation between resolution cells in the same frame, and also from one frame to the next. The fluctuating amplitude model is a first order model to reflect the inter-frame correlation. The amplitude estimates are obtained using a Kalman filter, from which the likelihood function is derived. A numerical maximization technique avoids problems previously encountered in “velocity filtering” approaches due to mismatch between assumed and actual target velocity, at the cost of additional computation. The Cramer-Rao lower bound (CRLB) is derived for a constant, known amplitude case. Estimation errors are close to this CRLB even when the amplitude is unknown. Results show track detection performance for unknown signal amplitude is nearly the same as that obtained when the correct signal model is used 相似文献
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The concept of maneuvering target tracking which is presented by K. Demirbas, (see ibid., vol.AES-23, p.757-66, 1987) is used to track maneuvering targets whose observations contain interference representing jamming or clutter signals. The resulting tracking approach produces state estimates that closely follow the actual state values, as in target tracking in a clear environment 相似文献
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Jie Cai Sinha A. Kirubarajan T. 《IEEE transactions on aerospace and electronic systems》2005,41(3):1030-1048
Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well. 相似文献