Kaushik Roy

Target tracking and Estimated Time of Arrival (ETA) Prediction for Arrival Aircraft

The problem of developing a unifled algorithm for arrival aircraft target tracking and Estimated Time of Arrival (ETA) prediction is approached from a hybrid linear systems approach. Discrete-time hybrid state models are derived and two state estimation algorithms, the Interacting Multiple Model (IMM) and particle flltering with resampling, are implemented for target tracking. Along with the standard Markov chain model for discrete mode changes, the idea of autonomous transitions, or mode changes which depend on the continuous state, are utilized in flltering. The IMM algorithm with autonomous transitions incorporated in discrete mode estimation is developed as an efiective ETA predictor. The IMM algorithm is also found to be more e‐cient than particle fllters in terms of run-time and target tracking accuracy. Tracking is performed on observed and simulated data to have RMS errors of less than 50 ft in position and less than 10 ft/s in velocity. ETA predictions are made within 30 seconds of actual landing time for time horizons of nearly 20 minutes.

A distributed multiple-target identity management algorithm in sensor networks

In this paper, we develop a distributed identity management algorithm for multiple targets in sensor networks. Each sensor is assumed to have the capability of managing identities of multiple targets within its surveillance region and of communicating with its neighboring sensors. We use the algorithm from our companion paper to incorporate local information about the identity of a target when it is available to a local sensor and at the same time reduces the uncertainty of the targets identity as measured by entropy. Identity information fusion is crucial for distributed identity management to compute the global information of the system from information provided by local sensors. We formulate this problem as an optimization problem and present three different cost functions, namely, Shannon information, Chernoff information, and the sum of Kullback-Leibler distances, which represent different performance criteria. Using Bayesian analysis, we derive a data fusion algorithm that needs a prior probability of the given data. Finally, we demonstrate the performance of the distributed identity management algorithm using scenarios from multiple-aircraft tracking in a sensor (radar) network with different fusion criteria.

A Fully Automated Distributed Multiple-Target Tracking and Identity Management Algorithm

In this paper, we consider the problem of tracking multiple targets and managing their identities in sensor networks. Each sensor is assumed to be able to track multiple targets, manage the identities of targets within its surveillance region, and communicate with its neighboring sensors. The problem is complicated by the fact that the number of targets within the surveillance region of a sensor changes over time. We propose a scalable distributed multiple-target tracking and identity management (DMTIM) algorithm that can track multiple targets and manage their identities efficiently in a distributed sensor network environment. DMTIM finds a globally consistent solution by maintaining local consistency among neighboring sensors. DMTIM consists of data association, multiple-target tracking, identity management, and information fusion. The data association and multiple-target tracking problems are efficiently solved by Markov chain Monte Carlo data association (MCMCDA) which can track an unknown number of targets. DMTIM manages identities of targets by incorporating local information and maintains local consistency among neighboring sensors via information fusion.

Lump-Sum Markets for Air Traffic Flow Control With Competitive Airlines

Air traffic flow control during adverse weather conditions is managed by the Federal Aviation Administration in todays air traffic system, although it is the individual airlines that are in the best position to assess the costs of disruptions to scheduled operations. To improve the efficiency of resource allocation, a market mechanism is proposed that enables airlines to participate directly in the flow control decision-making process. Since airlines can be expected to behave strategically, a lump-sum market mechanism is used for which existence of a Nash equilibrium and a bound on the worst case efficiency loss have been shown for agents that anticipate the effects of their own bids on resource prices. The convergence properties of this mechanism are studied for a two-player game with linear utilities, which reveals that restricting the airline bid update step-size can result in a wider range of stable bidding processes. The mechanism is then applied to an air traffic flow control scenario for multiple airports in the northeastern United States, which demonstrates the feasibility of performing market-based resource allocation within the time horizon for reliable weather predictions.

Enroute airspace control and controller workload analysis using a novel slot-based sector model

A novel slot-based sector structure is introduced and applied to enroute airspace control. Aircraft entering a sector are assigned trajectories to safely align to an assigned slot. Aircraft move through the rest of the sector, maintaining their slot at nominal velocity. Safety is provided according to proper spacing and assignment of slots. Analysis of single-link sectors with periodic arrival flows shows the proposed alignment control strategies safely handle several aircraft at a given altitude; the vector-for-spacing strategy outperforms strategies utilizing only velocity changes. Wide variations in worst-case capacities are observed under more general arrival processes; capacities are observed to increase with reduced uncertainty and with increased outflow spacing. Finally, a controller workload metric is developed and its use illustrated; we see that complex alignment strategies and uncertainty in arrivals increases workload

Multiple-Target Tracking and Identity Management with Application to Aircraft Tracking

The problem of tracking and managing the identities of multiple targets is discussed and applied to the passive radar tracking of aircraft. The targets are assumed to be commercial aircraft switching modes during flight, and are thus well modeled by hybrid systems. We propose a computationally efficient algorithm based on joint probabilistic data association for target-measurement correlation. We use the results of this algorithm to simultaneously implement an identity management algorithm based on identity-mass flow, and a multiple-target tracking algorithm based on the residual-mean interacting multiple model algorithm. Together, they constitute the multiple-target tracking and identity management algorithm. The multiple-target tracking and identity management algorithm incorporates suitable local information about target identity, when available, in a manner that decreases the uncertainty in the system as measured by its statistical entropy. For situations in which local information is not explicitly available, a technique based on multiple hypothesis testing is proposed to infer such information. This algorithm allows us to track multiple targets, each capable of multiple modes of operation, in the presence of continuous process noise and of spurious measurements. The multiple-target tracking and identity management algorithm is demonstrated through various scenarios that are motivated by air traffic surveillance applications.

Multiple-target tracking and identity management

This paper involves the development of an algorithm which can simultaneously track and manage identities of multiple targets in a sensor network, for the purpose of air traffic control. We propose a logical integration of joint probabilistic data association (JPDA) (Bar-Shalom and Fortmann, 1988), used for associating measurements with targets, and the identity management (IM) (Shin et al., 2003) algorithm for sensor networks, which utilizes target attribute information from local sensors to maintain the targets identity correctly. For target tracking, we use a modified version of the interacting multiple model (IMM) algorithm called the residual-mean IMM (RMIMM) which we developed (Hwang et al., 2003). The proposed algorithm incorporates target state estimate information from the tracking algorithm into the evolution of a doubly-stochastic belief matrix for the target identities, and also assimilates any local information available. The algorithm has been shown not only to converge, but also to not increase the uncertainty in our belief.

Solving the aircraft routing problem using network flow algorithms

The aircraft routing problem (ARP) is formulated as a time-dependent network flow problem and proved to be NP-hard, using a reduction from the NP-complete 3-dimensional matching problem (3DM). Flow scheduling and dynamic network theory concepts are used to develop an Integer Program (IP) formulation of the ARP, which can be solved exactly through software such as CPLEX. Linear program (LP) relaxation and rounding techniques are used to solve this formulation in pseudo-polynomial time. A heuristic first-come-first-served (FCFS) is also implemented. Scenarios of routing under congestion and rerouting due to weather show that the FCFS has the fastest run-time but worst performance, while the IP formulation is optimal but has no guarantees on run-time. For a given time horizon, the LP formulation runs in polynomial time and is often optimal, with bounded suboptimality otherwise.

Probabilistic Estimation of State-Dependent Hybrid Mode Transitions for Aircraft Arrival Time Prediction

The problem of accounting for the effects of air-traffic control (ATC) decision-making in modeling arrival traffic flow at airports is approached from a probabilistic perspective. Aircraft routing decisions are incorporated into previous work in developing a Discrete-Time Stochastic Hybrid Linear System for modeling aircraft dynamics and making Estimated Time of Arrival (ETA) predictions. The determination of aircraft path is modeled as a state-dependent discrete mode transition for the hybrid model. We determine the transition probabilities based on the separation (as a result of path choice) between each arriving aircraft and the aircraft immediately in front and show that separation distance is a good predictor for aircraft path choice. We then use the path probabilities to generate probability distributions over possible ETAs. Current methods used by airlines for predicting ETA are deterministic and not robust to ATC decisions on aircraft routing: not knowing exactly which path arriving aircraft are routed through means predictions must be made using only an average, nominal path, from which the aircraft may deviate substantially. NASA’s Center/TRACON Automation System makes accurate predictions but uses knowledge of routing decisions. By predicting ATC decisions, we greatly improve ETA predictions without using ATC information. Accounting for re-routing reduces average error in aircraft path prediction from 6nm to 3.5 nm at 10 minutes from landing, with a reduction in ETA error from 2 minutes to 1 minute.

A new hybrid state estimator for systems with limited mode changes

A new algorithm for hybrid state estimation, the K-Limited Mode-Change (KLMC) algorithm, is presented. Given noisy measurements, this algorithm estimates the continuous and discrete state histories for a class of hybrid systems that exhibit limited mode changes over time. The KLMC algorithm is compared to an existing hybrid state estimator, the Interacting Multiple Model (IMM), using a newly developed performance metric based on the concept of probability of error. Monte Carlo methods are used to obtain numerical estimates of the performance metric for simple hybrid system models. Simulation results show that KLMC outperforms IMM in terms of the estimate-error metric but requires larger storage and computational resource consumption.