Multisensor covariance control strategies for reducing bias effects in interacting target scenarios

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.     

Adaptive Phased-Array Tracking in ECM using Negative Information

Target tracking with adaptive phased-array radars in the presence of standoff jamming presents both challenges and opportunities to the track filter designer. A measurement likelihood function is derived for this situation which accounts for the effect of both positive and negative contact information. This likelihood function is approximated a? a weighted sum of Gaussian terms consisting of both positive and negative weights, accounting for the positive and negative contact information. Additionally, recent theoretical results have been reported which have derived an accurate measurement error covariance in the vicinity of the jammer when adaptive beamforming is used by the radar to null the effects of the jammer. We compare the impact of using a likelihood function that accounts for negative contact information and the corrected measurement error covariance by comparing five Kalman filter-based trackers in five different scenarios. We show that only those track filters which use both the negative contact information and the corrected measurement error covariance are effective in maintaining track on a maneuvering target as it passes through the jamming region. This approach can also be generalized to any target tracking problem where the sensor response is anisotropic.     

Canonical transform for tracking with kinematic models

A canonical transform is presented that converts a coupled or uncoupled kinematic model for target tracking into a decoupled dimensionless canonical form. The coupling is due to non-zero off-diagonal terms in the covariance matrices of the process noise and/or the measurement noise, which can be used to model the coupling of motion and/or measurement between coordinates. The decoupled dimensionless canonical form is obtained by simultaneously diagonalizing the noise covariance matrices, followed by a spatial-temporal normalization procedure. This canonical form is independent of the physical specifications of an actual system. Each subsystem corresponding to a canonical coordinate is characterized by its process noise standard deviation, called the maneuver index as a generalization of the tracking index for target tracking, which characterizes completely the performance of a steady-state Kalman filter. A number of applications of this canonical form are discussed. The usefulness of the canonical transform is illustrated via an example of performance analysis of maneuvering target tracking in an air traffic control (ATC) system.     

基于CDKF的快速协方差交叉融合跟踪算法研究

随着目标抗干扰能力的增强，单一寻的制导方式很难完成对目标的稳定跟踪和精确打击，需采用多种探测器作为传感器，提供多种观测数据以实现对目标的稳定跟踪和精确打击。建立了适当的目标运动模型和观测模型，利用中心差分卡尔曼滤波（CDKF）变换处理模型的非线性问题，避免了求解复杂的雅克比矩阵。对于分布式多传感器融合，传统的方法多采用协方差交叉（CI）融合方法，但是这类方法需要寻优求解。而快速协方差交叉（FCI）则不需要进行寻优过程，且计算量小。在此基础上，提出了用于多传感器目标跟踪的CDKF-FCI融合算法。最后，对算法进行了仿真分析，并进一步验证了提出算法的有效性。     

An improved multiple target angle tracking algorithm

An improved version of a multiple-target-angle tracking algorithm using sensor array outputs is presented. While retaining all the good features of the original algorithm, the improved version greatly reduces the error propagation due to the use of recursive approximations in updating target angle estimates. The assumption of a constant signal covariance matrix is no longer necessary. The improved performance of the proposed algorithm is demonstrated by computer simulations dealing with the tracking of two moving targets     

Multisensor resource deployment using posterior Cramer-Rao bounds

The development of a general framework for the systematic management of multiple sensors in target tracking in the presence of clutter is described. The basis of the technique is to quantify, and subsequently control, the accuracy of target state estimation. The posterior Cramer-Rao lower bound (PCRLB) provides the means of achieving this aim by enabling us to determine a bound on the performance of all unbiased estimators of the unknown target state. The general approach is then to use optimization techniques to control the measurement process in order to achieve accurate target state estimation. We are concerned primarily with the deployment and utilization of limited sensor resources. We also allow for measurement origin uncertainty, with sensor measurements either target-generated or false alarms. An example in which the aim is to track a submarine by deploying a series of constant false-alarm rate passive sonobuoys is presented. We show that by making some standard assumptions, the effect of the measurement origin uncertainty can be expressed as a state-dependent information reduction factor which can be calculated off-line. This enables the Fisher information matrix (FIM) to be calculated quickly, allowing Cramer-Rao bounds to be utilized for real-time, dynamic sensor management. The sensor management framework is shown to determine deployment strategies that enable the target to be accurately localized, and at the same time efficiently utilize the limited sensor resources.     

基于CCD视觉传感的焊缝自动跟踪程控系统的研究

为保证航天技术自动焊接中的焊缝质量 ,研究一种智能型焊缝跟踪系统 ,用VB语言开发出准确识别焊缝位置的图象处理软件 ,可通过工控机将CCD视觉传感检测到的焊缝准确提取中心 ,再反馈到可编程控制器PLC输出控制信号 ,达到高精度的焊缝跟踪     

Theory of partially adaptive radar

This work extends the recently introduced cross-spectral metric for subspace selection and dimensionality reduction to partially adaptive space-time sensor array processing. A general methodology is developed for the analysis of reduced-dimension detection tests with known and unknown covariance. It is demonstrated that the cross-spectral metric results in a low-dimensional detector which provides nearly optimal performance when the noise covariance is known. It is also shown that this metric allows the dimensionality of the detector to be reduced below the dimension of the noise subspace eigenstructure without significant loss. This attribute provides robustness in the subspace selection process to achieve reduced-dimensional target detection. Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detector can outperform the full-dimension detector when the noise covariance is unknown, closely approximating the performance of the matched filter.     

Bayesian Approach to Extended Object and Cluster Tracking using Random Matrices

In algorithms for tracking and sensor data fusion the targets to be observed are usually considered as point source objects; i.e., compared with the sensor resolution their extension is neglected. Due to the increasing resolution capabilities of modern sensors, however, this assumption is often no longer valid as different scattering centers of an object can cause distinct detections when passing the signal processing chain. Examples of extended targets are found in short-range applications (littoral surveillance, autonomous weapons, or robotics). A collectively moving target group can also be considered as an extended target. This point of view is the more appropriate, the smaller the mutual distances between the individual targets are. Due to the resulting data association and resolution conflicts any attempt of tracking the individual objects within the group seems to be no longer reasonable. With simulated sensor data produced by a partly unresolvable aircraft formation the addressed phenomena are illustrated and an approximate Bayesian solution to the resulting tracking problem is proposed. Ellipsoidal object extensions are modeled by random matrices, which are treated as additional state variables to be estimated or tracked. We expect that the resulting tracking algorithms are also relevant for tracking large, collectively moving target swarms.     

基于PSO的多观测器轨迹优化研究

在多观测站目标被动跟踪过程中,多观测器运动轨迹对定位跟踪精度有重要影响.为提高对运动目标的跟踪精度,将目标滤波协方差的迹作为目标优化函数,提出一种基于粒子群算法(Particle Swarm Optimi-zation,PSO)的多观测器轨迹优化方法,并将轨迹优化加入运动目标被动跟踪过程中,实现了观测器自适应运动下的运...     

The Tracking Index: A Generalized Parameter for ? ? ? and ? ? ? ? Target Trackers

A generalized, optimal filtering solution is presented for the target tracking problem. Applying optimal filtering theory to the target tracking problem, the tracking index, a generalized parameter proportional to the ratio of the position uncertainty due to the target maneuverability to that due to the sensor measurement, is found to have a fundamental role not only in the optimal steady-state solution of the stochastic regulation tracking problem, but also in the track initiation process. Depending on the order of the tracking model, the tracking index solution yields a closed form, consistent set of generalized tracking gains, relationships, and performances. Using the tracking index parameter, an initializing and tracking procedure in recursive form, realizes the accuracy of the Kalman filter with an algorithm as simple as the well-known ? ? ? filter or ? ? ? ? ? filter depending on the tracking order.     

Adaptive Correlation Regions for Alpha-Beta Tracking Filters

The design of correlation regions for track-while-scan systems is examined, assuming the requirement to maintain a constant probability of successful correlation. Starting with the assumption of independent and Gaussian-distributed range and azimuth errors in the sensor and assuming a constant-coefficient isotropic ?-? tracking filter, it is shown how the correlation region design must include such factors as sensor errors, timing jitter, tracking errors, and the asynchronous operation of the tracking function with respect to the sensor measurements. For a maneuvering target, it is shown that the size of the correlation region must be equal to the sum of the radius used for the straight-line case plus the magnitude of any tracking bias which results from deviation from the straight-line trajectory assumed in the tracking filter. An upper bound is derived for the magnitude of the bias which could reasonably be expected in typical maneuvers. By specifying the size of the correlation region on a constant probability basis, it is possible to obtain better discrimination against false targets and improved detection of maneuvers by sensing the development of tracking biases.     

MULTISENSOR TRACKING SYSTEM WITH ATTITUDE MEASUREMENTS

ＭＵＬＴＩＳＥＮＳＯＲＴＲＡＣＫＩＮＧＳＹＳＴＥＭＷＩＴＨＡＴＴＩＴＵＤＥＭＥＡＳＵＲＥＭＥＮＴＳＤｉｎｇＣｈｉｂｉａｏ，ＭａｏＳｈｉｙｉ（ＤｅｐａｒｔｍｅｎｔｏｆＥｌｅｃｔｒｏｎｉｃＥｎｇｉｎｅｅｒｉｎｇ，ＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅ...     

Use of map information for tracking targets on airport surface

A generic and novel approach for integrating airport map information with sensor measurements in the track estimation process is proposed and evaluated. The surface restrictions imposed by the network of roads, taxiways, and runways, represented by a simplified geometric model, are included in both the target observation and the dynamic models, to derive the target state estimates. The performance of the methods proposed is illustrated in representative airport surface scenarios, taking as a reference for comparison other tracking alternatives such as VS-IMM (variable structure interacting multiple model estimator) ground target tracking, or standard ones that do not make use of ground information.     

Differential-game-based guidance law using target orientation observations

Modern 4th generation air-to-air missiles are quite capable of dealing with today's battlefield needs. Advanced aerodynamics, highly efficient warheads and smart target acquisition systems combine to yield higher missile lethality than ever. However, in order to intercept highly maneuverable targets, such as future unmanned combat air vehicles (UCAV), or to achieve higher tracking precision for missiles equipped with smaller warheads, further improvement in the missile guidance system is still needed. A new concept is presented here for deriving improved differential-game-based guidance laws that make use of information about the target orientation, which is acquired via an imaging seeker. The underlying idea is that of using measurements of the target attitude as a leading indicator of target acceleration. Knowledge of target attitude reduces the reachable set of target acceleration, facilitating the computation of an improved estimate of the zero-effort miss (ZEM) distance. In consequence, missile guidance accuracy is significantly improved. The new concept is applied in a horizontal interception scenario, where it is assumed that the target maneuver direction, constituting a partial attitude information, can be extracted via processing target images, acquired by an imaging sensor. The derivation results in a new guidance law that explicitly exploits the direction of the target acceleration. The performance of the new guidance law is studied via a computer simulation, which demonstrates its superiority over existing state-of-the-art differential-game-based guidance laws. It is demonstrated that a significant decrease in the miss distance can be expected via the use of partial target orientation information.     

轨迹跟踪伺服系统的鲁棒复合控制

针对控制输入饱和受限且带有未知扰动的电机伺服系统,提出一种实现曲线轨迹准确跟踪的鲁棒复合控制方案。该方案引入一个参考信号生成器和一个扩展状态观测器,其中参考信号生成器可根据目标轨迹信号构造出对应的状态量,扩展状态观测器用于对系统的状态量和扰动进行估计, 采用反馈与前馈相结合构成最终的控制律。利用Lyapunov理论对闭环系统的稳定性进行了严格分析。在MATLAB中进行了仿真研究,随后在一个DSP控制的永磁直线电机二维伺服平台进行了试验验证。结果表明:所提的控制方案能在轨迹跟踪任务中取得优越的瞬态性能和稳态准确性,而且对目标轨迹和扰动的幅值差异具有较好的鲁棒性。     

Analytical solution of discrete colored noise ECA tracking filter

New analytical solutions of steady-state Kalman gains are presented for a discrete-time tracking filter with correlation in both the measurement noise and the target maneuver. The measurement noise model is a first-order discrete Markov process characterized by a correlation coefficient ρ. The target motion is examined for an exponentially correlated acceleration maneuver type in which the vehicle oscillation such as wind-induced-bending is also considered. The present solution method is based on factorizing the observed spectral density matrix Ψ(z) in frequency domain. The algorithm proposed here gives the Kalman gain matrix directly. For a case when the steady-state error covariance matrix is desired, such gains can be incorporated with the algebraic Riccati equation     

STUDY ON ALGORITHM OF SENSOR MANAGEMENT BASED ON FUNCTIONS OF EFFICIENCY AND WASTE

The sensor management system is a subsys-tem of a multisensor data fusion system,and itspurpose is to satisfy requests of multitarget andscanned space by using the limited sensor resourcesin order to gain optimal measurement values of allspecified characteristics ( detection and captureprobability,emission power of sensor,trackingprecision or target losing probability and so on) .By the optimal principle listed above,sensor re-sources are distributed in science and reason.In aword,itis a key p…     

Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets

The majority of tactical weapons systems require that manned maneuverable vehicles, such as aircraft, ships, and submarines, be tracked accurately. An optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets. Using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when maneuvering targets are to be tracked by sensors providing any combination of range, bearing, and elevation measurements.     

Heading and Speed Errors for x, y Tracking Filters

Heading and speed errors are analytically determined for noneumavering targets at the output of an x, y tracking filter which processes range and bearing measurements from a radar sensor in a track-while-scan (TWS) operation. These errors are shown to depend upon target range and speed, the angle between the radius and velocity vectors, sensor accuracies, and tracking filter parameters. eters. Depending upon the tracking filter implementation, these errors may also be a function of target bearing.