A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

ATC Trajectory Reconstruction for Automated Evaluation of Sensor and Tracker Performance

Currently most air traffic controller decisions are based on the information provided by the ground support tools provided by automation systems, based on a network of surveillance sensors and the associated tracker. To guarantee surveillance integrity, it is clear that performance assessments of the different elements of the surveillance system are necessary. Due to the evolution suffered by the surveillance processing chain in the recent past, its complexity has been increased by the integration of new sensor types (e.g., automatic dependent surveillance-broadcast [ADS-B], Mode S radars, and wide area multilateration [WAM]), data link applications, and networking technologies. With new sensors, there is a need for system-level performance evaluations as well as methods for establishing assessment at each component of the tracking evaluation.This work was funded by contract EUROCONTROL’s TRES, by the Spanish Ministry of Economy and Competitiveness under grants CICYT TEC2008-06732/TEC and CYCIT TEC2011-28626, and by the Government of Madrid under grant S2009/TIC-1485 (CONTEXTS).Publicad

Hybrid Deep Neural Network for Data Driven Missile Guidance with Maneuvering Target

Missile guidance, owing to highly complex and non-linear relative movement between the missile and its target, is a challenging problem. This is further aggravated in case of a maneuvering target which changes its own flight path while attempting to escape the incoming missile. In this study, to achieve computationally superior and accurate missile guidance, a deep learning is employed to propose a self-tuning technique for a fractional-order proportional integral derivative (FOPID) controller of a radar-guided missile chasing an intelligently maneuvering target. A multi-layer two-dimensional architecture is proposed for a deep neural network that combines the prediction feature of recurrent neural networks and estimation feature of feed-forward artificial neural networks. The proposed deep learning based missile guidance scheme is non-intrusive, data-based, and model-free wherein the parameters are optimized on-the-run while predicting the target’s maneuvering tactics to correct for processing time and loop delays of the system. Using deep learning for online optimization with minimal computational burden is the core feature of the proposed technique. Dual-core parallel simulations of missile-target dynamics and the control system were performed to demonstrate superiority of the proposed scheme in feasibility, adaptability, and the ability to effectively minimize the miss-distance in comparison with traditional and neural offline-tuned PID and FOPID based techniques. Compared to state-of-the-art offline-tuned neural control, the miss-distance was reduced by 68.42% for randomly maneuvering targets. Furthermore, a minimum miss-distance of 0.97 m was achieved for intelligently maneuvering targets for which the state-of-the-art method failed to hit the target. Overall, the proposed technique offers a novel approach for addressing the challenges of missile guidance in a computationally efficient and effective manner

A scheme on indoor tracking of ship dynamic positioning based on distributed multi-sensor data fusion

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference global positioning system (DGPS) and ultrasonic sensors. Other important factors, including the indoor temperature, position and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications

The Constant Information Radar

abstract: The constant information radar, or CIR, is a tracking radar that modulates target revisit time by maintaining a fixed mutual information measure. For highly dynamic targets that deviate significantly from the path predicted by the tracking motion model, the CIR adjusts by illuminating the target more frequently than it would for well-modeled targets. If SNR is low, the radar delays revisit to the target until the state entropy overcomes noise uncertainty. As a result, we show that the information measure is highly dependent on target entropy and target measurement covariance. A constant information measure maintains a fixed spectral efficiency to support the RF convergence of radar and communications. The result is a radar implementing a novel target scheduling algorithm based on information instead of heuristic or ad hoc methods. The CIR mathematically ensures that spectral use is justified

Small UAS Detect and Avoid Requirements Necessary for Limited Beyond Visual Line of Sight (BVLOS) Operations

Potential small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) operational scenarios/use cases and Detect And Avoid (DAA) approaches were collected through a number of industry wide data calls. Every 333 Exemption holder was solicited for this same information. Summary information from more than 5,000 exemption holders is documented, and the information received had varied level of detail but has given relevant experiential information to generalize use cases. A plan was developed and testing completed to assess Radio Line Of Sight (RLOS), a potential key limiting factors for safe BVLOS ops. Details of the equipment used, flight test area, test payload, and fixtures for testing at different altitudes is presented and the resulting comparison of a simplified mathematical model, an online modeling tool, and flight data are provided. An Operational Framework that defines the environment, conditions, constraints, and limitations under which the recommended requirements will enable sUAS operations BVLOS is presented. The framework includes strategies that can build upon Federal Aviation Administration (FAA) and industry actions that should result in an increase in BVLOS flights in the near term. Evaluating approaches to sUAS DAA was accomplished through five subtasks: literature review of pilot and ground observer see and avoid performance, survey of DAA criteria and recommended baseline performance, survey of existing/developing DAA technologies and performance, assessment of risks of selected DAA approaches, and flight testing. Pilot and ground observer see and avoid performance were evaluated through a literature review. Development of DAA criteria—the emphasis here being well clear— was accomplished through working with the Science And Research Panel (SARP) and through simulations of manned and unmanned aircraft interactions. Information regarding sUAS DAA approaches was collected through a literature review, requests for information, and direct interactions. These were analyzed through delineation of system type and definition of metrics and metric values. Risks associated with sUAS DAA systems were assessed by focusing on the Safety Risk Management (SRM) pillar of the SMS (Safety Management System) process. This effort (1) identified hazards related to the operation of sUAS in BVLOS, (2) offered a preliminary risk assessment considering existing controls, and (3) recommended additional controls and mitigations to further reduce risk to the lowest practical level. Finally, flight tests were conducted to collect preliminary data regarding well clear and DAA system hazards

Adaptive MIMO Radar for Target Detection, Estimation, and Tracking

We develop and analyze signal processing algorithms to detect, estimate, and track targets using multiple-input multiple-output: MIMO) radar systems. MIMO radar systems have attracted much attention in the recent past due to the additional degrees of freedom they offer. They are commonly used in two different antenna configurations: widely-separated: distributed) and colocated. Distributed MIMO radar exploits spatial diversity by utilizing multiple uncorrelated looks at the target. Colocated MIMO radar systems offer performance improvement by exploiting waveform diversity. Each antenna has the freedom to transmit a waveform that is different from the waveforms of the other transmitters. First, we propose a radar system that combines the advantages of distributed MIMO radar and fully polarimetric radar. We develop the signal model for this system and analyze the performance of the optimal Neyman-Pearson detector by obtaining approximate expressions for the probabilities of detection and false alarm. Using these expressions, we adaptively design the transmit waveform polarizations that optimize the target detection performance. Conventional radar design approaches do not consider the goal of the target itself, which always tries to reduce its detectability. We propose to incorporate this knowledge about the goal of the target while solving the polarimetric MIMO radar design problem by formulating it as a game between the target and the radar design engineer. Unlike conventional methods, this game-theoretic design does not require target parameter estimation from large amounts of training data. Our approach is generic and can be applied to other radar design problems also. Next, we propose a distributed MIMO radar system that employs monopulse processing, and develop an algorithm for tracking a moving target using this system. We electronically generate two beams at each receiver and use them for computing the local estimates. Later, we efficiently combine the information present in these local estimates, using the instantaneous signal energies at each receiver to keep track of the target. Finally, we develop multiple-target estimation algorithms for both distributed and colocated MIMO radar by exploiting the inherent sparsity on the delay-Doppler plane. We propose a new performance metric that naturally fits into this multiple target scenario and develop an adaptive optimal energy allocation mechanism. We employ compressive sensing to perform accurate estimation from far fewer samples than the Nyquist rate. For colocated MIMO radar, we transmit frequency-hopping codes to exploit the frequency diversity. We derive an analytical expression for the block coherence measure of the dictionary matrix and design an optimal code matrix using this expression. Additionally, we also transmit ultra wideband noise waveforms that improve the system resolution and provide a low probability of intercept: LPI)