Distributed Sensing & Multi-modal Sensor Fusion

Multi-sensor Fusion and Fault Detection for Air Traffic Surveillance [1]

The transition from the current ATM system to a super-density air traffic structure depends on accurate and reliable aircraft surveillance and tracking technologies.
- ADS-B may lack credibility due to a wide range of factors including multipath or mask effects.
- Multilateration has coverage limitation and occasional poor accuracy

Multiple sensor fusion is to enhance the performance and reliability of the future aircraft tracking system for ATC, which can be performed by the following steps:
- Step 1: Accurate modeling of the aircraft dynamics and sensor faults
- Step 2: State estimation for an aircraft with changing flight modes
- Step 3: Sensor fault detection by checking the characteristics of the state estimates’ statistical properties
- Step 4: Development of a sensor fusion algorithm

Sensor fusion algorithm architecture

Numerical Results

Simulated aircraft trajectories

Scenario 1: Both ADS-B and multilateration working normally

Scenario 2: Multilateration has a fault (from 100 sec to 170 sec), but ADS-B has no fault

Scenario 3: ADS-B has a fault (from 100 sec to 170 sec), but multilateration has no fault

When a fault happens the estimates given by the individual estimation algorithms deviate from each other.
From the estimates the sensor fusion algorithm can identify which sensor’s data is wrong and decrease its weight in the combined estimates.

=> the error of the combined estimates does not grow much during the sensor fault, i.e., the combined estimates are robust to various sensor faults.

W. Liu, J. Wei, M. Liang, Y. Cao, and I. Hwang, “Multi-sensor Fusion and Fault Detection for Air Traffic Surveillance,” IEEE Transactions on Aerospace and Electronic Systems, Vol.49(4), pp.2323-2399, October 2013
R. Deshmukh, O. Thapliyal, C. Kwon and I. Hwang, “Distributed State Estimation for a Stochastic Linear Hybrid System over a Sensor Network”, IET Control Theory and Applications, (submitted on October 29, 2017; under review)
C. Kwon and I. Hwang, "Sensing-Based Distributed State Estimation for Cooperative Multi-Agent Systems," IEEE Transactions on Automatic Control, (submitted on November 4, 2017, under review)
O. Thapliyal, J.S. Nandiganahalli and I. Hwang, “Kalman Filtering with State-Dependent Packet Losses”, IET Control Theory and Applications, (submitted on November 12, 2017; under review)

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