Tracking the Tracker from its Passive Sonar ML-PDA Estimates

Target motion analysis with wideband passive sonar has received much attention. Maximum likelihood probabilistic data-association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well-suited for low-observable targets; the results presented here apply to situations with higher signal to noise ratio as well, including of course the situation of a deterministic target observed via clean measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a two-leg motion model) from the results of the target estimation process, i.e. the estimated target state and the Fisher information matrix, quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results.Comment: To appear in IEEE Transactions on Aerospace and Electronic System

Performance Analysis of Bearings-only Tracking Problems for Maneuvering Target and Heterogeneous Sensor Applications

State estimation, i.e. determining the trajectory, of a maneuvering target from noisy measurements collected by a single or multiple passive sensors (e.g. passive sonar and radar) has wide civil and military applications, for example underwater surveillance, air defence, wireless communications, and self-protection of military vehicles. These passive sensors are listening to target emitted signals without emitting signals themselves which give them concealing properties. Tactical scenarios exists where the own position shall not be revealed, e.g. for tracking submarines with passive sonar or tracking an aerial target by means of electro-optic image sensors like infrared sensors. This estimation process is widely known as bearings-only tracking. On the one hand, a challenge is the high degree of nonlinearity in the estimation process caused by the nonlinear relation of angular measurements to the Cartesian state. On the other hand, passive sensors cannot provide direct target location measurements, so bearings-only tracking suffers from poor target trajectory estimation accuracy due to marginal observability from sensor measurements. In order to achieve observability, that means to be able to estimate the complete target state, multiple passive sensor measurements must be fused. The measurements can be recorded spatially distributed by multiple dislocated sensor platforms or temporally distributed by a single, moving sensor platform. Furthermore, an extended case of bearings-only tracking is given if heterogeneous measurements from targets emitting different types of signals, are involved. With this, observability can also be achieved on a single, not necessarily moving platform. In this work, a performance bound for complex motion models, i.e. piecewisely maneuvering targets with unknown maneuver change times, by means of bearings-only measurements from a single, moving sensor platform is derived and an efficient estimator is implemented and analyzed. Furthermore, an observability analysis is carried out for targets emitting acoustic and electromagnetic signals. Here, the different signal propagation velocities can be exploited to ensure observability on a single, not necessarily moving platform. Based on the theoretical performance and observability analyses a distributed fusion system has been realized by means of heterogeneous sensors, which shall detect an event and localize a threat. This is performed by a microphone array to detect sound waves emitted by the threat as well as a radar detector that detects electromagnetic emissions from the threat. Since multiple platforms are involved to provide increased observability and also redundancy against possible breakdowns, a WiFi mobile ad hoc network is used for communications. In order to keep up the network in a breakdown OLSR (optimized link state routing) routing approach is employed

Integration of fault tolerance and hardware redundancy techniques into the design of mobile platforms

This work addresses the development of a fault-tolerant mobile platform. Fault-tolerant mechanical system design is an emerging technology that attempts to build highly reliable systems by incorporating hardware and software architectures. For this purpose, previous work in fault-tolerant were reviewed. Alternate architectures were evaluated to maximize the fault tolerance capabilities of the driving and steering systems of a mobile platform. The literature review showed that most of the research work on fault tolerance has been done in the area of kinematics and control systems of robotic arms. Therefore, hardware redundancy and fault tolerance in mobile robots is an area to be researched. The prototype constructed as part of this work demonstrated basic principles and uses of a fault-tolerant mechanism, and is believed to be the first such system in its class. It is recommended that different driving and steering architectures, and the fault-tolerant controllers\u27 performance be tested on this prototype

The 21st Aerospace Mechanisms Symposium

During the symposium technical topics addressed included deployable structures, electromagnetic devices, tribology, actuators, latching devices, positioning mechanisms, robotic manipulators, and automated mechanisms synthesis. A summary of the 20th Aerospace Mechanisms Symposium panel discussions is included as an appendix. However, panel discussions on robotics for space and large space structures which were held are not presented herein

Study of extravehicular protection and operations

Extravehicular protection and operation

Study of tooling concepts for manufacturing operations in space Final report

Mechanical linkage device for manufacturing operations with orbital workshop

Tracking and Estimation Algorithms for Bearings Only Measurements

The Bearings-only tracking problem is to estimate the state of a moving object from noisy observations of its direction relative to a sensor. The Kalman filter, which provides least squares estimates for linear Gaussian filtering problems is not directly applicable because of the highly nonlinear measurement function of the state, representing the bearings measurements and so other types of filters must be considered. The shifted Rayleigh filter (SRF) is a highly effective moment-matching bearings-only tracking algorithm which has been shown, in 2D, to achieve the accuracy of computationally demanding particle filters in situations where the well-known extended Kalman filter and unscented Kalman filter often fail. This thesis has two principal aims. The first is to develop accurate and computationally efficient algorithms for bearings-only tracking in 3D space. We propose algorithms based on the SRF, that allow tracking, in the presence of clutter, of both nonmaneuvering and maneuvering targets. Their performances are assessed, in relation to competing methods, in highly challenging tracking scenarios, where they are shown to match the accuracy of high-order sophisticated particle filters, at a fraction of the computational cost. The second is to design accurate and consistent algorithms for bearings-only simultaneous localization and mapping (SLAM). The difficulty of this problem, originating from the uncertainty in the position and orientation of the sensor, and the absence of range information of observed landmarks, motivates the use of advanced bearings-only tracking algorithms. We propose the quadrature-SRF SLAM algorithm, which is a moment-matching filter based on the SRF, that numerically evaluates the exact mean and covariance of the posterior. Simulations illustrate the accuracy and consistency of its estimates in a situation where a widely used moment-matching algorithm fails to produce consistent estimates. We also propose a Rao-Blackwellized SRF implementation of a particle filter, which, however, does not exhibit favorable consistency properties

Sensor Array Processing with Manifold Uncertainty

<p>The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered. </p><p>First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments. </p><p>As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360$^\circ$ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.</p><p>Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.</p>Dissertatio

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980