Use of measurements from an imaging sensor for precision targettracking

The extraction of measurements for precision tracking of the centroid of a target from a forward-looking infrared imaging sensor is presented. The size of the image of the target is assumed to be small, i.e. around 10 pixels. The statistical characterization of the centroid of the target is obtained. Similarly, the statistical properties of the image correlation of two frames, which measures the target offset, are derived. Explicit expressions that map the video noise statistics into measurement noise statistics are obtained. The offset measurement noise is shown to be autocorrelated. State variable models for tracking the target centroid with these measurements are then presented. Simulation results and quantitative conclusions about achievable subpixel tracking accuracy are given. It is shown that the filter that models the autocorrelated measurement noise provides the best performance     

Method for Optical-Flow-Based Precision Missile Guidance

A new precision guidance law is presented for three-dimensional intercepts against a moving target. In contrast to previously published guidance laws, it does not require knowledge of the range to the target. This makes it appropriate for use on platforms which have an imaging device, such as a video camera, as a primary sensor. We prove that with idealized dynamic model, the guidance law results in zero miss distance, and a formula is given for impact angle error which tends to zero as does target speed, making this method particularly suitable against slow moving targets. Computer simulations are used to test the law with a more realistic model, with a video camera and optical-flow algorithm providing target information. It is shown to perform well compared with another law from the literature, despite requiring less information.     

Radar target identification of aircraft using polarization-diversefeatures

The characteristics and target identification potential of a representation of the information from two polarization-diverse measurements of the radar backscatter of an unknown target are considered. The locus of these two polarization-diverse waveforms, termed the transient polarization response (TPR), has been shown to be closely related to the geometry of the scattering centers of the target. The polarization-related components of features derived from the TPR concur well with the shape and orientation of the major scattering centers distributed in the downrange profile of the object. This illustrates the intuitively appealing result that the polarization of the backscatter, as represented by the TPR and mapped onto the modified polarization chart, is determined by target geometry. It is shown that both polarization-related and amplitude-related features derived from the TPR are useful for target identification. By using distance measures that depend on various components, the elliptical parametrization information alone is sufficient to allow satisfactory target identification at SNRs of 0 dB and above. The significance of this result is that the absolute amplitudes of the received horizontally polarized and vertically polarized channels need not be known. However, if amplitude information is known, target identification percentages improve     

单载机相位差无源定位系统可观测性分析(英文)

With a pair of antennas spaced apart, an airborne passive location system measures phase differences of emitting signals. Regarded as cyclic ambiguities, the moduli of the measurements traditionally are resolved by adding more antenna elements. This paper models the cyclic ambiguity as a component of the system state, of which the observability is analyzed and compared to that of the bear- ings-only passive location system. It is shown that the necessary and sufficient observability condition for the bearings-only passive location system is only the necessary observability condition for the passive location system with phase difference measurements, and that when the system state is observable, the cyclic ambiguities can be estimated by accumulating the phase difference measurements, thereby making the observer able to locate the emitter with high-precision.     

EM-ML algorithm for track initialization using possibly noninformative data

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.     

An efficient method for determining a distance at direction finding of target with partially-known parameters

An efficient method for determining the slant distance from a rectilinearly moving target by two bearing measurements is described using a priori information about the probable magnitude of velocity, acceleration, and others. Also given are the assessment of method effectiveness and an illustrative example.     

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     

Track Biases and Coalescence with Probabilistic Data Association

It is shown that probabilistic data association yields biased tracks in a multitarget environment, and the magnitude of the bias is investigated. A critical target separation distance exists, within which the tracks tend to coalesce completely. The problem of track initiation on new targets is also discussed.     

Airborne GMTI radar position bias estimation using static-rotatortargets of opportunity

In target tracking systems: using GMTI (ground moving target indicator) radars on airborne platforms, the locations of these platforms are available from GPS-based estimates. However, these estimated locations are subject to errors that are, typically, stationary autocorrelated random processes, i.e., slowly varying biases. In situations where there are no known-location targets to estimate these biases, the next best recourse is to use targets of opportunity at fixed but unknown locations. Such targets can be, e.g., static rotators (ground-based radars with rotating antenna), which yield detections in moving target indicator (MTI) radars. It is shown that these biases can be estimated in such a scenario, i.e., they meet the complete observability condition. Following this, the achievable accuracy for a generic scenario is evaluated. It is shown that accurate georegistration can be obtained even with a small number of measurements     

基于变结构控制理论的航向平面导引规律设计

基于变结构控制理论．针对小型水面机动目标，设计了一种新的导弹航向平面导引律．并对导引律中所需要的目标运动信息给出了估算方法。由于导弹的动态特性对脱靶量有较大影响，为此．本文还研究了导弹动态特性补偿的方法。仿真结果表明．在导弹的动态响应特性较好的情况下，该导引律能提供很高的命中精度；在导弹的动态响应特性很差的情况下，对动态特性补偿后，仍可得到很高的命中精度。     

Multiple target tracking using products of position measurements

The continued development of the symmetric measurement equation (SME) filter for track maintenance in multiple target tracking (MTT) is considered, focusing on the case in which the SMEs are generated by forming sums of products of the original position measurements. The SME filter is developed for the case of N targets whose motions consist of random perturbations about constant-velocity trajectories. It is assumed that measurements of x-coordinate positions are available, and that the number of measurements is equal to the number of targets. Various analytical properties of the SME filter are studied. It is shown that under a very weak condition, the estimation error equation is locally exponentially stable. The performance of the SME filter is investigated by comparing it with an optimal (minimum-variance) estimator and by generating a computer simulation in the six-target case     

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.     

Image-enhanced tracking

Measurement of the angular orientation of the target or the target designation makes possible the design of high-accuracy tracking and prediction systems. Such measurements are best accomplished with an imaging sensor. A model is presented for the image-based observation link, along with the equations used to convert a sequence of image processor outputs into an estimate of target status. An example examines the interrelationship which exists between the rate of feature changes of a target, the frame rate of the imager, and the sensitivity of the sensor-image processor. It is shown that even at high frame rates, anomalous events may occur     

Constant gain tracker with variable frame time

A three-parameter constant-gain recursive filter is augmented by a residual-dependent frame time algorithm that automatically increases sampling rates when a target maneuvers. Computer simulations show that tracking performance is essentially independent of the particular target trajectory. It is found that radial distance errors remain effectively constant over different trajectories. It is the number of observations dictated by the adaptive frame time algorithm that is trajectory-dependent. The filter equations along with the frame time adjustment algorithm are first described, and a comparison made with a similar procedure. Examples given use the nonlinear observations generated by a passive sensor system     

A New Approach to Geometry of Range Difference Location

Loran, as an example of a ?hyperbolic? range difference location system, provides hyperbolic lines of position on which the target must lie. It is shown in this paper that lines of position need not be hyperbolic, i.e., an exact straight line of position on the plane or the surface of a sphere (where the solution is a great circle) is derived and computer-generated error maps are given.     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

Observability of an integrated GPS/INS during maneuvers

An observability analysis of a GPS/INS system during manoeuvers is presented based upon a perturbation model with respect to the Earth-centered-Earth-fixed (ECEF) coordinate system. Analysis is performed on two types of manoeuvers, linear acceleration and steady turn. These manoeuvers could be used for in-flight INS alignment using GPS. During the constant linear acceleration without rotation relative to the Earth, the linear system model is shown to be time-invariant. The observability analysis for the time invariant linear system model shows that linear acceleration does not change the number of observable modes but rather the structure of the observable space. For a nonconstant linear acceleration or a steady turn, the perturbation linear system becomes time varying. For this time-varying system, three types of observability are considered, complete, differential, and instantaneous observability. Instantaneous observability is the strongest properties and means that the state of the system at any time may be determined instantaneously from observation of the output and its derivatives. Instantaneous observability is important for fast in-flight INS alignment. It is shown that the number of instantaneously observable moded is increased by at least 2 during a maneuver. Hence, some linear combinations of the attitude angles become instantaneously observable.     

Manoeuvring target tracking in clutter using particle filters

A particle filter (PF) is a recursive numerical technique which uses random sampling to approximate the optimal solution to target tracking problems involving nonlinearities and/or non-Gaussianity. A set of particle filtering methods for tracking and manoeuvering target in clutter from angle-only measurements is presented and evaluated. The aim is to compare PFs to a well-established tracking algorithm, the IMM-PDA-EKF (interacting multiple model, probabilistic data association, extended Kalman filter), and to provide an insight into which aspects of PF design are of most importance under given conditions. Monte Carlo simulations show that the use of a resampling scheme which produces particles with distinct values offers significant improvements under almost all conditions. Interestingly, under all conditions considered here,using this resampling scheme with blind particle proposals is shown to be superior, in the sense of providing improved performance for a fixed computational expense, to measurement-directed particle proposals with the same resampling scheme. This occurs even under conditions favourable to the use of measurement-directed proposals. The IMM-PDA-EKF performs poorly compared with the PFs for large clutter densities but is more effective when the measurements are precise.     

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     

基于OTHR与纯角度传感器群数据关联的模式辨识与定位精度提升

 天波超视距雷达(OTHR)目标跟踪面临着"三低"(低检测概率、低数据率和低测量精度)和"多径"(多条传播路径)的挑战,因此传播模式的准确辨识与目标定位精度提升是改善跟踪能力的关键。首先利用纯角度传感器群获得目标地理位置的初步估计,然后采用极大似然估计建立了OTHR的传播模式和杂波模式的辨识规则,进而利用最小方差估计准则实现OTHR和纯角度传感器群的量测融合。仿真结果表明,此算法的模式辨识正确率很高,能明显提升方位角的测量精度,但是不能明显提升径向距的精度。