When considering advances in “smart” weapons it is clear that air-launched systems have adopted an integrated approach to meet rigorous requirements, whereas air-defence systems have not. The demands on sensors, state observation, missile guidance, and simulation for air-defence is the subject of this research. Historical reviews for each topic, justification of favoured techniques and algorithms are provided, using a nomenclature developed to unify these disciplines. Sensors selected for their enduring impact on future systems are described and simulation models provided. Complex internal systems are reduced to simpler models capable of replicating dominant features, particularly those that adversely effect state observers. Of the state observer architectures considered, a distributed system comprising ground based target and own-missile tracking, data up-link, and on-board missile measurement and track fusion is the natural choice for air-defence. An IMM is used to process radar measurements, combining the estimates from filters with different target dynamics. The remote missile state observer combines up-linked target tracks and missile plots with IMU and seeker data to provide optimal guidance information. The performance of traditional PN and CLOS missile guidance is the basis against which on-line trajectory optimisation is judged. Enhanced guidance laws are presented that demand more from the state observers, stressing the importance of time-to-go and transport delays in strap-down systems employing staring array technology. Algorithms for solving the guidance twopoint boundary value problems created from the missile state observer output using gradient projection in function space are presented. A simulation integrating these aspects was developed whose infrastructure, capable of supporting any dynamical model, is described in the air-defence context. MBDA have extended this work creating the Aircraft and Missile Integration Simulation (AMIS) for integrating different launchers and missiles. The maturity of the AMIS makes it a tool for developing pre-launch algorithms for modern air-launched missiles from modern military aircraft.

Target tracking using Wireless Sensor Networks (WSNs) has drawn lots of attentions after the recent advances of wireless technologies. Target tracking aims at locating one or several mobile objects and depicting their trajectories over time. The applications of Object Tracking Sensor Networks (OSTNs) include but not limited to environmental and wildlife monitoring, industrial sensing, intrusion detection, access control, traffic monitoring, patient monitoring in the health-related studies and location awareness in the battle eld. One of the most rewarding applications of target tracking is wildlife monitoring. Wildlife monitoring is used to protect the animals which are endangered to extinction. Road safety applications are another popular usage of wildlife monitoring using WSNs. In this thesis, the issues and challenges of energy-efficient wildlife monitoring and target tracking using WSNs are discussed. This study provides a survey of the proposed tracking algorithms and analyzes the advantages and disadvantages of these algorithms. Some of the tracking algorithms are proposed to increase the energy e ciency of the tracking algorithm and to prolong the network lifetime; while, other algorithms aim at improving the localization accuracy or decreasing the missing rate. Since improving the energy efficiency of the system provides more alive sensors over time to locate the target; it helps to decrease the missing rate as the network ages. Thus, this study proposes to adjust the sensing radius of the sensor nodes in real-time to decrease the sensing energy consumption and prolong the network lifetime. The proposed VAriable Radius Sensor Activation (VARSA) mechanism for target tracking using wireless sensor networks tackles the energy consumption issues due to resource constraints of the WSNs. VARSA reduces the radio covered area of each sensor node to only cover the Area of Interest (AoI) which is the location of the target in tracking applications. Thus, VARSA aims at decreasing the sensing energy consumption which leads to encreasing the network life time. In addition, VARSA decreases the missing rate over time as it provides more alive sensors to detect the target compared to previous activation algorithms as the network ages. VARSA is compared to PRediction-based Activation (PRA) and Periodic PRediction-based Activation (PPRA) algorithms which are two of the most promising algorithms proposed for sensor activation. The simulation results show that VARSA outperforms PRA and PPRA. VARSA prolongs the lifetime of the network and decreases the missing rate of the target over time.

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, long-term tracking, especially without GPS, due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. This work presents a solution to the moving-target handoff problem in the absence of GPS. The proposed solution (a) a nonlinear complementary filter for self-pose estimation using only an IMU, (b) a particle filter for relative pose estimation between UAS using a relative range (c) visual target tracking using a gimballed camera when the target is close to the handoff UAS, and (d) track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present hardware results of the self-pose estimation and visual target tracking, as well as an extensive simulation result that demonstrates the effectiveness of our full system, and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

No description available.

The importance of physical motion in simulators for pilot training is strongly debated. The present experiment isolated different types of motion, a potentially important variable contributing to the controversy. Participants used a joystick to perform a target tracking task in a motion simulator built using a MOOG Stewart platform. Five training conditions compared training without motion (as one would train in a stationary simulator), with correlated motion, with disturbance motion, with disturbance motion isolated to the visual display, and with both correlated and disturbance motion. The test condition involved the full motion model with both correlated and disturbance motion. We analyzed speed and accuracy across training and test as well as strategic differences in joystick control. We found that training with disturbance provided better transfer to test conditions that included disturbance motion for accuracy, but not speed, and that training with disturbance motion produced different joystick control strategies compared to training without disturbance. / Thesis / Master of Science (MSc)

No description available.

The main idea of this thesis is to formulate the smooth variable structure filter (SVSF) for target tracking applications in the presence of measurement origin uncertainty. Tracking, by definition is the recursive estimation of the states of an unknown target from indirect, inaccurate and uncertain measurements. The measurement origin uncertainty introduces the data association problem to the tracking system. The SVSF estimation strategy was first presented in 2007. This filter is based on sliding mode concepts formulated in a predictor-corrector form. Essentially, the SVSF uses an existence subspace and smoothing boundary layer to bind the estimated state trajectory to within a subspace around the true trajectory. The SVSF is demonstrated to be robust to modeling uncertainties and provide extra measures of performance such as magnitude of the chattering signal. Therefore, with respect to specific nature of car tracking problems that involves modeling uncertainty, it was hypothesized that a robust estimation strategy such as the SVSF, would improve the performance of the tracking system and give more robust tracking results. Also, having the extra information provided by the SVSF strategy, i.e. the chattering magnitude signal, would lead to algorithms that could better account for measurement origin uncertainty in the context of the data association process. Further to these hypotheses, this research has focused on investigating the performance of the SVSF in the target tracking problems, advancing the development of the SVSF, and employing its characteristics to deal with data association problems. The performance of the SVSF, in its current form, can be improved when there is fewer measurements than states by using its error covariance in target tracking. As the first contribution in this research, the SVSF is formulated in the context of target tracking in clutter and combined with data association algorithms, resulting in the SVSF-based probabilistic data association (PDA) and joint probabilistic data association (JPDA) for non-maneuvering and maneuvering targets. The results are promising in the tracking scenarios with modeling uncertainties. Therefore, the thesis is then expanded by generalizing the covariance of the SVSF for the cases where the number of measurements is less than the number of states. The generalized covariance formulation is then used to derive a generalized variable boundary layer (GVBL) SVSF. This new derivation gives an estimation method that is optimal in the MMSE sense and in the meantime preserves the robustness of the SVSF. The proposed algorithm improves the performance measures and makes a more reliable tracking algorithm. This thesis explores the hypothesis that multiple target tracking performance can be substantially improved by including chattering information from SVSF-based filtering in the data association method. A Bayesian framework is used to formulate a new set of augmented association probabilities which include the chattering information. The simulation and experimental results demonstrate that the proposed augmented probabilistic data association improves the performance of the tracking system including maneuvering cars, in particular for highly cluttered environments. The derived methods are applied on simulations and also on real data from an experimental setup. This thesis is made up of a compilation of papers that include three conference papers and three journal papers. / Thesis / Doctor of Philosophy (PhD)

Sensors are getting smaller, inexpensive and sophisticated, with an increased availability. Compared to 25 years ago, an object tracking system now can easily achieve twice the accuracy, a much larger coverage and fault tolerance, without any significant changes in the overall cost. This is possible by simply employing more than just one sensor and processing measurements from individual sensors sequentially (or even in a batch form). %This is the centralized scheme of multi-sensor target tracking wherein the sensors send their individual detections to a central facility, where tracking related tasks such as data association, filtering, and track management etc. are performed. This is also perhaps the simplest solution for a multi-sensor approach and also optimal in the sense of minimum mean square error (MMSE) among all other multi-sensor scenario. In sophisticated sensors, the number of detections can reach thousands in a single frame. The communication and computation load for gathering all such detections at the fusion center will hamper the system's performance while also being vulnerable to faults. A better solution is a distributed architecture wherein the individual sensors are equipped with processing capabilities such that they can detect measurements, extract clutter, form tracks and transmit them to the fusion center. The fusion center now fuses tracks instead of measurements, due to which this scheme is commonly termed track-level fusion. In addition to sub-optimality, the track-level fusion suffers from a very coarse problem, which occurs due to correlations between the tracks to be fused. Often, in realistic scenarios, the cross-correlations are unknown, without any means to calculate them. Thus, fusion cannot be performed using traditional methods unless extra information is transmitted from the fusion center. This thesis proposes a novel and generalized method of fusing any two probability density functions (pdf) such that a positive cross-correlation exists between them. In modern tracking systems, the tracks are essentially pdfs and not necessarily Gaussian. We propose harmonic mean density based fusion and prove that it obeys all the necessary requirements of being a viable fusion mechanism. We show that fusion in this case is a classical example of agreement between the fused and participating densities based on average $\chi^2$ divergence. Compared to other such fusion techniques in the literature, the HMD performs exceptionally well. Transmitting covariance matrices in distributed architecture is not always possible in cases for e.g. tactical and automotive systems. Fusion of tracks without the knowledge of uncertainty is another problem discussed in the thesis. We propose a novel technique for local covariance reconstruction at the fusion center with the knowledge of estimates and a vector of times when update has occurred at local sensor node. It has been shown on a realistic scenario that the reconstructed covariance converges to the actual covariance, in the sense of Frobenius norm, making fusion without covariance, possible. / Thesis / Doctor of Philosophy (PhD)

A multi-vehicle system is composed of interconnected vehicles coordinated to complete a certain task. When controlling such systems, the aim is to obtain a coordinated behaviour through local interactions among vehicles and the surrounding environment.One motivating application is the monitoring of algal blooms; this phenomenon occurs frequently and has a substantial negative effect on the environment such as large-scale mortality of fish. In this thesis, we investigate control of multiple unmanned surface vehicles (USVs) for mobile target circumnavigation and tracking, where the target can be an algal bloom area.A protocol based on local measurements provided by the vehicles is developed to estimate the target's location and shape.Then a control strategy is derived that brings the vehicle system to the target while forming a regular polygon. More precisely, we first consider the problem of tracking a mobile target while circumnavigating it with multiple USVs. A satellite image indicates the initial location of the target, which is supposed to have an irregular dynamic shape well approximated by a circle with moving center and varying radius. Each USV is capable of measuring its distance to the boundary of the target and to its center. We design an adaptive protocol to estimate the circle's parameters based on the local measurements. A control protocol then brings the vehicles towards the target boundary as well as spreads them equidistantly along the boundary. The protocols are proved to converge to the desired state. Simulated examples illustrate the performance of the closed-loop system. Secondly, we assume that the vehicles can only measure the distance to the boundary of the target and not to its center. We propose a decentralised least-squares method for target estimation suitable for circular targets. Convergence proofs are given for also this case. An example using simulated algal bloom data shows that the method works well under realistic settings. Finally, we investigate how to extend our protocols to a quite general irregular mobile target. In this case, each vehicle communicates only with its two nearest neighbors and estimates the curvature of the target boundary using their collective measurements. We validate the performance of the protocol under various settings and target shapes through a numerical study. / Multi-fordon-styrsystem består av sammankopplade fordon som koordinerar för att slutföra en given uppgift. I sådana styrsystem är målet att få ett koordinerat beteende via lokala interaktioner mellan fordonen och miljön de vistas i. Ett motiverande exempel är övervakning av algblomning, ett fenomen som inträffar frekvent och har omfattande negativa effekter såsom kraftig mortalitet hos fiskar. I denna rapport undersöker vi hur Unmanned Surface Vehicles (USVs) kan styras för att cirkulera och spåra ett givet mobilt objekt, till exempel en yta med algblomning. Ett protokoll är utvecklat för att estimera det mobila objektets position och form, baserat på lokala mätningar utförda av fordonen, samt en reglerstrategi tas fram som styr systemet med fordon till objektet samtidigt som de formar en regelbunden polygon.   Mer precist undersöker vi först problemet att samtidigt spåra och cirkulera ett mobilt objekt med USVs. En satellitbild indikerar startpositionen av objektet, antaget att ha en irreguljär tidsvarierande form som kan approximeras väl av en cirkel med tidsberoende center och radie. Varje USV kan mäta avståndet till objektets rand och center. Vi designar ett adaptivt protokoll för att estimera cirkelns parametrar baserat på lokala mätningar. Ett reglerprotokoll styr sedan fordonen mot objektets rand samt sprider ut dem ekvidistant kring randen. Vi bevisar att protokollen konvergerar mot önskat tillstånd. Två simuleringar visar det slutna systemets prestanda.   Sedan antar vi att fordonen endast kan mäta avståndet till randen på objektet, men inte tills dess center. Vi tar fram en decentraliserad minstakvadratmetod för att estimera objektet, lämpligt för cirkulära objekt. Konvergens bevisas även i detta fall. Ett exempel med data från en simulerad algblomning visar att metoden fungerar bra under realistiska scenarion.   Slutligen undersöker vi hur protokollen kan vidareutvecklas för mobila objekt med tämligen generella irreguljära former. I detta fall antar vi att fordonen endast kan kommunicera med sina två närmaste grannar och estimera kurvan för objektets rand från deras samlade mätningar. Vi validerar protokollen via två simuleringar. / <p>QC 20200217</p>