Circular array STAP

Traditionally, space-time adaptive processing (STAP) for airborne early warning (AEW) radar has been applied to uniform linear arrays (ULAs). However, when considering the overall radar system, electronically scanned circular arrays have advantages: a better combination of even and continual angular and temporal coverage, and mechanical simplicity because it does not need to rotate. This paper answers the question “How well does STAP perform when applied to a circular array?” This paper shows that for the AEW mission, circular arrays are indeed STAP compatible. However, when conventional STAP algorithms are used there may be a small loss in performance when compared with a ULA. With some care in the choice and implementation of the STAP algorithm, the majority of the degradation is at close ranges, where the target returns are relatively strong. At long ranges performance is barely affected. A STAP algorithm which compensates for the circular array environment and provides better performance than existing algorithms is presented     

基于二维资源管理的多功能雷达任务调度算法

动态孔径分割技术为相控阵雷达针对不同任务灵活分配孔径资源提供了可能,而传统的资源调度方法仅基于单一孔径条件研究了时间资源的优化分配问题。针对雷达搜索、跟踪与成像任务的自适应调度问题,提出了一种基于时间-孔径二维资源管理的雷达资源调度算法。该算法建立了雷达孔径分割条件下的二维资源调度模型,确立了能量资源约束条件;利用基于压缩感知的稀疏孔径逆合成孔径雷达（ISAR）成像技术,使雷达在完成目标搜索和跟踪任务的同时实现对目标的成像;定义了调度算法性能的评价指标。在仿真实验中将该算法与另外2种算法进行对比,验证了所提算法在高度成功率、二维资源利用率与任务并行度这3种性能指标上具有优越性。     

Bayesian and Dempster-Shafer target identification for radarsurveillance

This paper considers the problem of target track identification in a radar surveillance system. To build a target identifier alongside a tracker, four features which are available for real-time processing in an air surveillance system are used here: target identity (TID) from a friend-and-foe identification (IFF) system, elevation measurement from the radar, target speed, and acceleration estimated by a tracker. These four features are combined to classify air targets into five different air target categories: friendly commercial, friendly military, hostile commercial (or unknown airline), hostile military, and false targets (clutter). Two popular statistic-based techniques, namely, the Bayesian and Dempster-Shafer methods, are applied to develop radar target identification algorithms for our application. Real-life as well as simulated air surveillance radar data are used to evaluate the practicality and effectiveness of this track identification approach in a radar surveillance system     

Modeling and performance of HF/OTH radar target classificationsystems

The effects of a class of multipath propagation channels on the performance of a over-the-horizon (OTH) radar target classification system are considered. A Rician frequency-selective fading channel model is employed to characterize the effects of the multipath propagation medium and evaluate the performance of radar target classification systems. The performance of classification algorithms that employ relative amplitude, relative phase, and absolute amplitude measurements as features is investigated. Performance estimates of the various classification algorithms for interesting sets of channel parameters are obtained by means of Monte-Carlo simulations     

Classification of radar targets using synthetic neural networks

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier     

SAR ATR performance using a conditionally Gaussian model

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program     

不确定环境下多无人机任务分配

针对复杂战场环境下无人机与攻击目标之间距离的不确定性,将该距离抽象为一个区间数。在此基础上,构建了不确定环境下多无人机任务分配的数学模型。根据多无人机任务分配问题的特殊性,重新设计了差分进化算法的编码方式、变异操作、交叉操作等。其中,选择操作中,在区间数排序方法的基础上,依照可能度来计算候选解被选中的概率。鉴于差分进化算法中不同变异策略的内在特点和适用场合不尽相同,提出了 3种多变异策略的差分进化算法,以便最大限度地发挥各种变异策略的技术优势。针对 CEC2013测试函数和多无人机任务分配问题分别开展仿真实验,实验结果表明,多变异策略的差分进化算法其性能改进显著,非常适合于求解多无人机任务分 配问题。     

频率步进雷达数字信号处理

综述了频率步进雷达系统设计与信息处理中的主要问题,包括系统参数设计、目标抽取算法、与Chirp子脉冲的兼容性、与圆锥扫描体制的兼容性等等,并论述了不同情况下多普勒效应的影响及其解决方法,提出了相应的数字信号处理方案。     

高超声速飞机热管理系统控制模型构建与仿真

以高超声速飞机为研究对象,提出了一种基于单相流体回路的热管理系统(TMS)模型,通过热控制策略与热沉调度模型实现热沉制冷能力最大化目标,解决新型高速飞机日益彰显的冷源不足问题。热控制策略利用系统辨识与热载荷预测算法,提出基于能量平衡与温度反馈配合的热控制模型,解决热惯性带来的控制延迟问题。基于热沉冷却能力评估与热载荷匹配提出热沉调度模型,旨在合理利用各种冷源,解决飞行后期冷源不足的问题。研究通过MATLAB/Simulink仿真验证模型及算法,结果表明:所设计的TMS能够满足高超声速飞机长时间飞行需求;考虑能量平衡的控制模型在超调量及衰减比方面均优于温度反馈控制模型;基于热沉调度策略能够降低冷源消耗速率,更充分地利用各种机载热沉。      

Maneuvering Target Tracking Using Adaptive State Estimation

Two approaches to a nonlinear state estimation problem are presented. The particular problem addressed is that of tracking a maneuvering target in three-dimensional space using spherical observations (radar data). Both approaches rely on semi-Markov modeling of target maneuvers and result in effective algorithms that prevent the loss of track that often occurs when a target makes a sudden, radical change in its trajectory. Both techniques are compared using real and simulated radar measurements with emphasis on performance and computational burden.     

A branch-and-bound algorithm applied to optimal radar search pattern

In the article, the radar acquisition problem, e.g. the determination of a directional energy allocation sequence, is studied. The radar search pattern goal is the detection of a moving target whose initial location is approximately known. We have turned towards the general search theory where the observer allocates indivisible search efforts while the target presence probability spreads due to its dynamics. A few years ago, a Branch and Bound algorithm was proposed to determine the optimal sequence for a conditionally deterministic target. This operational research algorithm supposes a negative exponential detection function and a one over N detection logic, meaning that the target is declared detected if it has been detected once over a horizon of N looks. We have applied it to a narrow-beam tracking radar attempting to acquire a ballistic target. Non-trivial search patterns, such as expanding-contracting spirals, are obtained.     

Radar target classification using doppler signatures of human locomotion models

The problem of target classification for ground surveillance Doppler radars is addressed. Two sources of knowledge are presented and incorporated within the classification algorithms: 1) statistical knowledge on radar target echo features, and 2) physical knowledge, represented via the locomotion models for different targets. The statistical knowledge is represented by distribution models whose parameters are estimated using a collected database. The physical knowledge is represented by target locomotion and radar measurements models. Various concepts to incorporate these sources of knowledge are presented. These concepts are tested using real data of radar echo records, which include three target classes: one person, two persons and vehicle. A combined approach, which implements both statistical and physical prior knowledge provides the best classification performance, and it achieves a classification rate of 99% in the three-class problem in high signal-to-noise conditions.     

Beam resource scheduling for a VHF-MIMO radar network in low-angle tracking

The low-angle tracking in multipath interference is a challenging problem for the Very High Frequency (VHF) radar. The colocated Multi-Input Multi-Output (MIMO) technique can remedy such a defect. In this paper, a Joint Beam-Target Assignment and Power Allocation (JBTAPA) strategy is proposed for the VHF-MIMO radar network tracking low-angle targets. The core of the JBTAPA strategy is to improve the worst tracking accuracy among multiple targets by assigning appropriate beams to targets and allocating the power resource in each beam using the feedback information in the tracking cycle. Taking into account the transmit multipath and receive multipath, we derive the Cramér-Rao Lower Bound (CRLB) on angle estimate, which is then incorporated in the Predicted Conditional CRLB (PC-CRLB). A more accurate and consistent lower bound is provided as the optimization metric since the PC-CRLB is based on the most recently realized measurements. A two-stage-based technique is proposed to solve the JBTAPA problem, which is originally NP-hard. Simulation results verify the effectiveness and efficiency of the proposed method. The results also imply that the target reflectivity plays one of the important roles in resource allocation.     

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

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Moving target imaging and trajectory computation using ISAR

Novel algorithms for moving target imaging and trajectory computation using a two-receiver radar are presented. The range-bin alignment is implemented with an adaptive method using the envelope correlation feature of different returns and the angular trajectory equation is solved using a linear least squares method combined with a unique phase-unwrapping technique. The angular positions of the synthetic array elements are determined from the trajectory computation. Three target models moving along a perturbed straight line are used to verify the proposed algorithms     

Distributed weather radar using X-band active arrays

Dense networks of short-range radars capable of mapping storms and detecting atmospheric and airborne hazards are described. Comprised of physically small, low-power antennas, these networks defeat the Earth curvature blockage that limits today's long-range radar networks and enable high resolution views that extend from the lower-troposphere to the tops of storms. The networks are comprised of 1-meter antennas that transmit 10's of W peak power and are capable of high-speed electronic beam-steering. A system architecture is described that maximizes the value accrued to users of radar data through utility functions that specify dynamic, optimal allocation of resources in response to the needs of multiple end-users and associated information retrieval algorithms.     

On the resolvability of sinusoidal parameter estimates [andapplication to SAR target features]

The resolvability of 2-D (two-dimensional) sinusoidal parameter estimates is studied. These sinusoids describe the target features in SAR (synthetic aperture radar) applications. We analyze the resolvability by considering the frequency estimates of the sinusoids. Our results may be used by target classification algorithms to better classify radar targets in SAR applications     

Equal allocation scheduling for data intensive applications

A new analytical model for equal allocation of divisible computation and communication load is developed. Equal allocation of load is attractive in multiple processor systems when real time information on processor and link capacity that is necessary for optimal scheduling is not available. The model includes a detailed accounting of solution reporting time. Equal allocation scheduling is compared with sequential scheduling and a new type of multi-installment scheduling. Aerospace applications include the processing of satellite imagery, radar, and sensor networks.     

Effectiveness of the Nash strategies in competitive multi-team target assignment problems

The Nash strategy in game theory has often been criticized as being ineffective in competitive multi-team target assignment problems, especially when compared with other simplistic strategies such as the random or greedy targeting strategies. This criticism arises from the fact that the Nash strategies may yield unpredictable results when paired with non-Nash strategies in non-zero sum games. In addition, the Nash equilibrium is generally more difficult to compute than strategies which do not attempt to anticipate the strategy of the other side. The authors seek to show that in multi-team target assignment problems the Nash strategy is superior to such simplistic strategies while also remaining computationally feasible. To demonstrate this point, an attrition model was considered, consisting of two teams of nonhomogeneous fighting units simultaneously targeting each other and compare the outcomes when various combinations of four targeting strategies are used on each side. The four strategies are: 1) the random strategy where each unit selects its target randomly, 2) the unit greedy strategy where each unit chooses the target that optimizes its own performance only, 3) the team optimal strategy where the units coordinate their choice of targets so as to optimize the overall team performance while ignoring the possible strategy choices by the other team, and 4) the team Nash strategy, calculated under the assumption that the other team is also using a Nash strategy. Because the computational requirements for calculating the team Nash strategies may become unfeasibly large, an efficient method was discussed for approximating the Nash strategies using a neighborhood search algorithm called unit level team resource allocation (ULTRA). The results were compared for all 16 possible combinations of these four targeting strategies and show that for each team the Nash strategy outperforms all other strategies irrespective of the strategy employed by the other team     

Precision tracking algorithms for ISAR imaging

The simultaneous imaging and tracking of a moving target is one of the most difficult problems in inverse synthetic aperture radar (ISAR) signal processing. The problem is addressed here using a two-antenna imaging radar. The target range shift is sensed with an exponentially averaged envelope correlation algorithm while the angle change is measured with a differential phase shift algorithm. The three state Kalman filters are used in range and azimuth dimensions to provide both a filtered estimate and a one-sample-ahead prediction for the purpose of target tracking. The filtered range shift provides an accurate information for range bin alignment, and the target angle change provides the angular positions of the synthetic array elements. Therefore, the ISAR imaging simply becomes processing of a circular-arc aperture. The algorithms are verified both by computer simulations and also with experimental data processing