This paper develops a State Space model of a feed-forward control system in the frequency domain, and time domain. The results of the mathematical model are implemented and the responses of the Elevation and Azimuth servo controller in a tracking telescope called a Cine-Sextant developed for the Utah Test and Training Range.

Mirrors are commonly used to stabilize images and point the line-of-sight (LOS) for a variety of electro-optical applications such as surveillance, target tracking and laser pointing. However, the law of reflection introduces an inherent 2:1 relationship in the axis perpendicular to the LOS that must be addressed. Because of this relationship, simply stabilizing the mirror in inertial space will not stabilize the LOS. Also, the 2:1 relationship renders the mirror stabilization configuration particularly susceptible to base motions. Presented are several different techniques that use a combination of mechanisms and inertial / relative-motion sensors that can be used to deal with these limitations. The techniques are described along with a discussion of the characteristics and tradeoffs associated with each.

This paper considers the problem of tracking and intercepting a potentially moving ground-based RF source with an autonomous guided munition that has a passive bearings-only sensor located on its nose. It is assumed that the munition has lost GPS signal lock and that it relies only on its noisy inertial measurement unit (IMU) for guidance and navigation. Bearings-only target motion analysis (TMA) algorithms are used to obtain a position and velocity estimate for the RF source using the position, velocity and attitude estimates of the munition as well as the azimuth and elevation measurements obtained from the bearings-only RF sensor. Six degree-of-freedom (6-DOF) and three degree-of-freedom (3-DOF) munition models are used to evaluate the tracking and intercept/seeker performance of a hybrid coordinate (HC) extended Kalman filter (EKF), a particle filter (PF), a multiple hypothesis (MH) HC-EKF, and a pseudo-linear least squares (PLLS) filter

The phase of the development engineering life cycle in which the greatest risk traditionally emerges is that of integration and test, culminating in final acceptance. Attention is often paid to integration and test aspects too late to influence the earlier phases of the life cycle, where the seeds are sown for success or failure. This paper presents a strategy that actively addresses integration aspects as early as possible, to mitigate these risks, where possible, well ahead of commencement of the implementation of the integration phase. A multi-sensor Naval fire-control system is taken as an example, and this is used to focus on the essential elements of the strategy for the successful integration of Radar and EO sensors. These include: design for integration, a robust and accurate method of aligning the sensors, and test cases which reflect the in service usage of the system. The methods of alignment both in the dockyard and at sea are described, together with a threads analysis approach to determining system functionality, user operational requirements and hence determining system functional test coverage. Finally conclusions are drawn, comparing the classical approach to the one described in the paper, showing the benefits to de-risking the engineering life cycle and achieving an in-service system which has the functionality and performance the user is expecting.

Star tracking at Ball Aerospace has reached a new level of performance with the development of the High Accuracy Star Tracker (HAST). This new tracker builds on the experience gained from the Chandra Aspect Camera which attained better than 0.2 arc-seconds on-orbit overall performance with a stationary star field. The HAST is optimized to achieve similar performance on stars moving between 0.07 and 1.0 degrees/second and can maintain track on stars moving 0 to 4 degrees/second. HAST is capable of tracking multiple stars, further enhancing the accuracy performance. This paper examines the principal error components that affect star tracking accuracy. We further examine the physical basis for three error sources: 1) boresight alignment bias, 2) transit dependent bias, and 3) random measurement. We develop the design trade space used to optimize tracker performance within these error constraints. Finally, we present the working specifications for HAST, describe the HAST implementation, and review the flight qualification test results.

Many trackers make use of correlation techniques to provide an estimate of the shift between incoming imagery and a stored reference image. One efficient method for estimating this shift is based on a least squares approach that makes use of gradient and difference imagery to avoid the computationally expensive construction of a correlation surface. A problem with this method is that it tends to underestimate image shifts when there is significant noise in the reference image-which is often the case. An alternative method makes use of a generalized least squares approach that takes the noise in the reference image into account when estimating the image shift. This paper describes these two correlation algorithms and presents the results of an empirical comparison of their performance under varying noise conditions for a variety of test imagery.

A new imaging technique is introduced, based on tomographic imaging principles applied to the output from a conical scan reticle system. The concept allows for a simple, low cost system consisting of simple scanning optics, a single element detector and a signal-processing unit to act as an imaging sensor. Potential applications of the tomographic scanning imaging (TOSCA) seeker include missile seekers, smart munitions, and other devices using low-cost imagers.

Bearings-only tracking is widely used in the defense arena. Its value can be exploited in systems using optical sensors and sonar, among others. Non-linearity and non-Gaussian prior statistics are among the complications of bearings-only tracking. Several filters have been used to overcome these obstacles, including particle filters and multiple hypothesis extended Kalman filters (MHEKF). Particle filters can accommodate a wide range of distributions and do not need to be linearized. Because of this they seem ideally suited for this problem. A MHEKF can only approximate the prior distribution of a bearings-only tracking scenario and needs to be linearized. However, the likelihood distribution maintained for each MHEKF hypothesis demonstrates significant memory and lends stability to the algorithm, potentially enhancing tracking convergence. Also, the MHEKF is insensitive to outliers. For the scenarios under investigation, the sensor platform is tracking a moving and a stationary target. The sensor is allowed to maneuver in an attempt to maximize tracking performance. For these scenarios, we compare and contrast the acquisition time and mean-squared tracking error performance characteristics of particle filters and MHEKF via Monte Carlo simulation.

This paper describes a closed-loop tracking system using multiple co-located sensors to develop multi-sensor track histories on multiple targets. The use of multiple, co-aligned sensors to track multiple, possibly maneuvering targets, presents a number of tracker design challenges and opportunities. Many of these problems have been addressed individually in the published literature from a theoretical point of view. However, no one has yet addressed the design and implementation of a specific tracker to meet all of these requirements at once. Specific questions addressed in this paper include how to assign N detections in a current frame to M active tracks, how to initiate new tracks and terminate dead tracks, how to combine information from multiple sensors into a single integrated picture, represented by a global track file, and how to perform these functions in a timely manner to support a precision closed loop tracking system.

In this note we introduce the idea of adaptive scheduling based on a cost function methodology. As the warfare environment becomes more complex, individual sensor resources are stretched, and the usage of the sensors has grown. In a multi-ship multi-platform environment, one has the potential to share information across platforms. This would dramatically increase the strategic and tactical picture available to mission planners and commanders at all force levels. In order to accomplish this mission, the sensors must all be coordinated so adaptability and multi-force tasking can be accomplished with netted sensors. Adaptive sensor management expands group capabilities by freeing up resources such as dwells/energy management. Savings arise by effective usage of tracking resources by revisiting threats with radar resources only when needed. This can be done by introducing analytic cost functions of the revisit time that enable one to minimize revisit time while maintaining error within acceptable bounds.

The accuracy of tracking can be enhanced by the incorporation of prior information in the form of a local road map. For instance, the performance of ground target tracking algorithms can be improved by incorporating hard constraints (describing roads and junctions) into the tracking model. We describe an approach to enhancing tracking algorithms that uses 'probability fields' to represent the local road map information. The resulting 'Bayes-filter' equations are solved using particle filters and compared with a basic particle filter with no additional map information.

The Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm proposed by Streit and Luginbuhl in 1995 is adapted here for use in active sonar applications. PMHT is a batch technique that uses the Expectation-Maximization (EM) algorithm to obtain MAP estimates of the sequence of target states. Probabilistic Multi-Hypothesis Tracking for Active Sonar (PMHTAS) modifies PMHT for detecting and tracking a maneuvering target in clutter. Tactical active sonar systems often transmit several different waveforms simultaneously, and the detection performance with individual waveforms typically varies with the target Doppler and reverberation level. PMHTAS is a multi-waveform tracking algorithm: measurements from all waveforms are used to update each batch of track state estimates in a scheme to adapt the appropriate echo amplitude models to the target Doppler and local reverberation level. PMHTAS also incorporates developments presented by Willett, Ruan, and Streit in 1998 to utilize echo amplitude information and to handle clutter and target maneuvers. A new detection test statistic based on the objective function that is optimized in the EM algorithm has also been developed along with an initialization procedure for starting new tracks. This paper describes the key components of the PMHTAS algorithm design and presents some results using active sonar sea trial data.

Tracking maneuvering target using multiple models is an attractive approach that is an alternative to a design that needs logic for both maneuver detection and filter re-initialization. Common current practice in multiple model tracking uses a switching Markov model. Recently the authors developed a multiple model approach to tracking maneuvering targets, but without using a switching Markov model. The models used work independently, while the process noise of each is adjusted online based on a relative likelihood function. The performance and consistency of the newly developed filter are compared to an IMM tracker.

Most modern fast jet aircraft have at least one infrared camera, a Forward Looking Infra Red (FLIR) imager. Future aircraft are likely to have several infrared cameras, and systems are already being considered that use multiple imagers in a distributed architecture. Such systems could provide the functionality of several existing systems: a pilot flying aid, a modern laser designator/targeting system and a missile approach warning system. This paper considers image-processing techniques that could be used in a distributed aperture vision system, concentrating on the harmonisation of high resolution, narrow field of view cameras with low-resolution cameras with wide fields of view. In this paper, consideration is given to the accuracy of the registration and harmonisation processes in situations where the complexity of the scene varies over different terrain types, and possible use of supplementary motion information from inertial measurement systems.

A multiscale differential fractal feature of an image is proposed and a small target detection method from complex nature clutter is presented. Considering the speciality that the fractal features of man-made objects change much more violently than that of nature's when the scale is varied, fractal features at multiple scales used for distinguishing man-made target from nature clutter should have more advantages over standard fractal dimensions. Multiscale differential fractal dimensions are deduced from typical fractal model and standard covering-blanket method is improved and used to estimate multiscale fractal dimensions. A multiscale differential fractal feature is defined as the variation of fractal dimensions between two scales at a rational scale range. It can stand out the fractal feature of man-made object from natural clutters much better than the fractal dimension by standard covering-blanket method. Meanwhile, the calculation and the storage amount are reduced greatly, they are 4/M and 2/M that of the standard covering-blanket method respectively (M is scale). In the image of multiscale differential fractal feature, local gray histogram statistical method is used for target detection. Experiment results indicate that this method is suitable for both kinds background of land and sea. It also can be appropriate in both kinds of infrared and TV images, and can detect small targets from a single frame correctly. This method is with high speed and is easy to be implemented.

In this paper, a novel radar management strategy technique suitable for RADAR/IRST track fusion, which is based on Fisher Information Matrix (FIM) and fuzzy stochastic decision approach, is put forward. Firstly, optimal radar measurements' scheduling is obtained by the method of maximizing determinant of the Fisher information matrix of radar and IRST measurements, which is managed by the expert system. Then, suggested a "pseudo sensor" to predict the possible target position using the polynomial method based on the radar and IRST measurements, using "pseudo sensor" model to estimate the target position even if the radar is turned off. At last, based on the tracking performance and the state of target maneuver, fuzzy stochastic decision is used to adjust the optimal radar scheduling and retrieve the module parameter of "pseudo sensor". The experiment result indicates that the algorithm can not only limit Radar activity effectively but also keep the tracking accuracy of active/passive system well. And this algorithm eliminates the drawback of traditional Radar management methods that the Radar activity is fixed and not easy to control and protect.

Detection and tracking algorithms for a Hemispherical Optical Sensor Tracker (HOST) are described. The HOST unit provides simultaneous and continuous viewing of a region spanning 360° of azimuth and -7° to zenith in elevation. Maximum likelihood derived, multi-frame detection algorithms are described for use with a stationary HOST surveillance sensor. A multi-target tracking architecture is also described. Due to the nature of the optical system, tracking in Line Of Sight (LOS) angular coordinates is much easier than using pixel coordinate tracking. This in turn provides an opportunity for multi-HOST unit triangulation to obtain 3D position estimates. This paper provides a brief overview of the HOST optical system, describes the multi-frame detection algorithm, the pixel to LOS conversion, and the multi-target track algorithm.