Algorithms and Analysis for Optimizing the Tracking Performance of Cyber Attacked Sensor-Equipped Connected Vehicle Networks

Sensor-equipped connected vehicle networks (SECVNs) have the potential to enable substantially safer driving by improved object tracking, which is an important basic building block in SECVNs. Unfortunately, cyber-attacks on SECVNs pose a very serious threat which could lead to unacceptable outcomes, including fatalities. Recently there has been increasing focus on malicious attack detection and mitigation in SECVNs, and some of this work has considered attacks on sensor data to impact object tracking. Unfortunately, low complexity mitigation approaches which do not compromise performance are lacking. This paper describes an efficient machine-learning enhanced approach for tracking under cyber-attacks. By proper selection of some variances related to the sensor and prior probability density functions, under some assumptions the performance can be made as close as desired to a bound on the best possible performance. However, the complexity of this new approach is dramatically lower than the best existing published low complexity approach, which provides performance which is substantially inferior to that provided by the new approach. The new approach also provides much better scaling with the size of the SECVN. In particular, the complexity increases linearly in the number of sensors, while the best low complexity published approach has a complexity which grows quadratically in the number of sensors. The new approach is also applicable to other tracking applications.