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

Comparative Analysis of Non Linear Estimation Schemes used for Undersea Sonar Applications

    The performance evaluation of various passive underwater target tracking algorithms like Pseudo Linear Estimator, Maximum Likelihood Estimator, Modified Gain Bearings-only Extended Kalman Filter, Unscented Kalman Filter, Parameterized Modified Gain Bearings-only Extended Kalman Filter and Particle Filter coupled with Modified Gain Bearings-only Extended Kalman Filter using bearings-only measurements is carried out with various scenarios in Monte Carlo Simulation. The performance of Parameterized Modified Gain Bearings-only Extended Kalman Filter is found to be better than all estimates

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

Precision Pointing Control System (PPCS) system design and analysis

The precision pointing control system (PPCS) is an integrated system for precision attitude determination and orientation of gimbaled experiment platforms. The PPCS concept configures the system to perform orientation of up to six independent gimbaled experiment platforms to design goal accuracy of 0.001 degrees, and to operate in conjunction with a three-axis stabilized earth-oriented spacecraft in orbits ranging from low altitude (200-2500 n.m., sun synchronous) to 24 hour geosynchronous, with a design goal life of 3 to 5 years. The system comprises two complementary functions: (1) attitude determination where the attitude of a defined set of body-fixed reference axes is determined relative to a known set of reference axes fixed in inertial space; and (2) pointing control where gimbal orientation is controlled, open-loop (without use of payload error/feedback) with respect to a defined set of body-fixed reference axes to produce pointing to a desired target

Trajectory optimization for target localization using small unmanned aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 189-197).Small unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance missions. One particular mission of interest involves computing location estimates for targets detected by onboard sensors. Combining UAV state estimates with information gathered by the imaging sensors leads to bearing measurements of the target that can be used to determine the target's location. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods produce biased and uncertain estimates, occasionally leading to filter instabilities. Careful selection of the measurement locations greatly enhances filter performance, motivating the development of UAV trajectories that minimize target location estimation error and improve filter convergence. The objective of this work is to develop guidance algorithms that enable the UAV to fly trajectories that increase the amount of information provided by the measurements and improve overall estimation observability, resulting in proper target tracking and an accurate target location estimate. The performance of the target estimation is dependent upon the positions from which measurements are taken relative to the target and to previous measurements. Past research has provided methods to quantify the information content of a set of measurements using the Fisher Information Matrix (FIM). Forming objective functions based on the FIM and using numerical optimization methods produce UAV trajectories that locally maximize the information content for a given number of measurements. In this project, trajectory optimization leads to the development of UAV flight paths that provide the highest amount of information about the target, while considering sensor restrictions, vehicle dynamics and operation constraints.(cont.) The UAV trajectory optimization is performed for stationary targets, dynamic targets and multiple targets, for many different scenarios of vehicle motion constraints. The resulting trajectories show spiral paths taken by the UAV, which focus on increasing the angular separation between measurements and reducing the relative range to the target, thus maximizing the information provided by each measurement and improving the performance of the estimation. The main drawback of information based trajectory design is the dependence of the Fisher Information Matrix on the true target location. This issue is addressed in this project by executing simultaneous target location estimation and UAV trajectory optimization. Two estimation algorithms, the Extended Kalman Filter and the Particle Filter are considered, and the trajectory optimization is performed using the mean value of the target estimation in lieu of the true target location. The estimation and optimization algorithms run in sequence and are updated in real-time. The results show spiral UAV trajectories that increase filter convergence and overall estimation accuracy, illustrating the importance of information-based trajectory design for target localization using small UAVs.by Sameera S. Ponda.S.M

Sensor Path Planning for Emitter Localization

The localization of a radio frequency (RF) emitter is relevant in many military and civilian applications. The recent decade has seen a rapid progress in the development of small and mobile unmanned aerial vehicles (UAVs), which offer a way to perform emitter localization autonomously. The path a UAV travels influences the localization significantly, making path planning an important part of a mobile emitter localization system. The topic of this thesis is path planning for a UAV that uses bearing measurements to localize a stationary emitter. Using a directional antenna, the direction towards the target can be determined by the UAV rotating around its own vertical axis. During this rotation the UAV is required to remain at the same position, which induces a trade-off between movement and measurement that influences the optimal trajectories. This thesis derives a novel path planning algorithm for localizing an emitter with a UAV. It improves the current state of the art by providing a localization with defined accuracy in a shorter amount of time compared to other algorithms in simulations. The algorithm uses the policy rollout principle to perform a nonmyopic planning and to incorporate the uncertainty of the estimation process into its decision. The concept of an action selection algorithm for policy rollout is introduced, which allows the use of existing optimization algorithms to effectively search the action space. Multiple action selection algorithms are compared to optimize the speed of the path planning algorithm. Similarly, to reduce computational demand, an adaptive grid-based localizer has been developed. To evaluate the algorithm an experimental system has been built and the algorithm was tested on this system. Based on initial experiments, the path planning algorithm has been modified, including a minimal distance to the emitter and an outlier detection step. The resulting algorithm shows promising results in experimental flights

Development and Flight of a Robust Optical-Inertial Navigation System Using Low-Cost Sensors

This research develops and tests a precision navigation algorithm fusing optical and inertial measurements of unknown objects at unknown locations. It provides an alternative to the Global Positioning System (GPS) as a precision navigation source, enabling passive and low-cost navigation in situations where GPS is denied/unavailable. This paper describes two new contributions. First, a rigorous study of the fundamental nature of optical/inertial navigation is accomplished by examining the observability grammian of the underlying measurement equations. This analysis yields a set of design principles guiding the development of optical/inertial navigation algorithms. The second contribution of this research is the development and flight test of an optical-inertial navigation system using low-cost and passive sensors (including an inexpensive commercial-grade inertial sensor, which is unsuitable for navigation by itself). This prototype system was built and flight tested at the U.S. Air Force Test Pilot School. The algorithm that was implemented leveraged the design principles described above, and used images from a single camera. It was shown (and explained by the observability analysis) that the system gained significant performance by aiding it with a barometric altimeter and magnetic compass, and by using a digital terrain database (DTED). The (still) low-cost and passive system demonstrated performance comparable to high quality navigation-grade inertial navigation systems, which cost an order of magnitude more than this optical-inertial prototype. The resultant performance of the system tested provides a robust and practical navigation solution for Air Force aircraft

Guidance and control for defense systems against ballistic threats

A defense system against ballistic threat is a very complex system from the engineering point of view. It involves different kinds of subsystems and, at the same time, it presents very strict requirements. Technology evolution drives the need of constantly upgrading system’s capabilities. The guidance and control fields are two of the areas with the best progress possibilities. This thesis deals with the guidance and control problems involved in a defense system against ballistic threats. This study was undertaken by analyzing the mission of an intercontinental ballistic missile. Trajectory reconstruction from radar and satellite measurements was carried out with an estimation algorithm for nonlinear systems. Knowing the trajectory is a prerequisite for intercepting the ballistic missile. Interception takes place thanks to a dedicated tactical missile. The guidance and control of this missile were also studied in this work. Particular attention was paid on the estimation of engagement’s variables inside the homing loop. Interceptor missiles are usually equipped with a seeker that provides the angle under which the interceptor sees its target. This single measurement does not guarantee the observability of the variables required by advanced guidance laws such as APN, OGL, or differential games-based laws. A new guidance strategy was proposed, that solves the bad observability problems and returns satisfactory engagement performances. The thesis is concluded by a study of the interceptor most suitable aerodynamic configuration in order to implement the proposed strategy, and by the relative autopilot design. The autopilot implements the lateral acceleration commands from the guidance system. The design was carried out with linear control techniques, considering requirements on the rising time, actuators maximum effort, and response to a bang-bang guidance command. The analysis of the proposed solutions was carried on by means of numerical simulations, developed for each single case-study

Maneuvering Intruder Passive Ranging for Detect-and-Avoid

For the last two decades, unmanned aircraft systems (UAS) has seen much interest from both the civilian and military sector. As civilian applications expand, the issue of safety becomes more apparent. One major technical challenge currently facing UAS operations is properly sharing the national airspace with conventional aircraft. For safety purposes, it is necessary that UAS be able to properly detect intruding aircraft, including manned and unmanned aircraft, and avoid them. This requirement has been termed Detect-and-Avoid (DAA). We investigate the orbiting intruder passive ranging problem, where an ownship aircraft is moving with a constant velocity and the intruding aircraft is conducting an orbiting maneuver. We assume that the ownship measures the bearing angles to the intruder aircraft. We approach the problem utilizing a filter bank algorithm parameterized with respect to the range, the heading of the intruder, and the angular velocity. We test the performance of the filter bank algorithm using two different system models. The first system model comprises of the relative position in Cartesian coordinates and velocities in polar coordinates. The second system model is the modified polar coordinates. We conduct Monte Carlo simulations and utilize the root mean square error over time to determine the best parameterization of the filter algorithm for both system models. The results show that the system model in Cartesian coordinates performs better when estimating the range while the modified polar coordinates achieves better estimates for the heading of the intruder. We find that the filter in the modified polar coordinates exhibits more divergent behavior than the system in Cartesian coordinates. After an investigation of the orbiting intruder problem, we investigate the maneuvering intruder problem. Often the intruder's trajectory will follow segments of straight legs and orbits legs. We introduce a way to integrate our filter bank algorithm onto a Interacting multiple models framework. We utilize a constant velocity model on a single EKF. We implement a mixing strategy, where the IMM mixing stage will mix at a certain rate. We conduct a simulation study to identify the effects of varying mixing rate and the values of the model transition probability. We find that the model transition probability has the largest effect on performance. Finally, we show preliminary results of our algorithm's performance on flight test data.Mechanical and Aerospace Engineerin

Single Transponder Range Only Navigation Geometry (STRONG) applied to REMUS autonomous under water vehicles

Submitted in partial fulfillment of the requirements for Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 2005A detailed study was conducted to prove the concept of an iterative approach to single transponder navigation for REMUS Autonomous Underwater Vehicles (AUVs). Although the concept of navigation with one acoustic beacon is not new, the objective was to develop a computer algorithm that could eventually be integrated into the REMUS architecture. This approach uses a least squares fit routine coupled with restrictive geometry and simulated annealing vice Kalman filtering and state vectors. In addition, to provide maximum flexibility, the single transponder was located on a GPS equipped surface ship that was free to move instead of the more common single bottom mounted beacon. Using only a series of spread spectrum ranges logged with time stamp, REMUS standard vehicle data, and reasonable initial conditions, the position at a later time was derived with a figure of merit fit score. Initial investigation was conducted using a noise model developed to simulate the errors suspected with the REMUS sensor suite. Results of this effort were applied to a small at sea test in 3,300 meters with the REMUS 6000 deep water AUV. A more detailed test was executed in Buzzard's Bay, Massachusetts, in 20 meters of water with a REMUS 100 AUV focusing on navigation in a typical search box. While deep water data was too sparse to reveal conclusive results, the Buzzard's Bay work strongly supports the premise that an iterative algorithm can reliably integrate REMUS logged data and an accurate time sequence of ranges to provide position fixes through simple least squares fitting. Ten navigational legs up to1500 meters in length showed that over 90% of the radial position error can be removed from an AUV's position estimate using the STRONG algorithm vice dead reckon navigation with a magnetic compass and Doppler Velocity Log alone (DVL)