Aneesh Raghavan

Bio: Aneesh Raghavan is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Observer (quantum physics) & Statistical hypothesis testing. The author has an hindex of 4, co-authored 10 publications receiving 32 citations.

Time varying control set design for UAV collision avoidance using reachable tubes

Abstract: In this paper, we propose a reachable set based collision avoidance algorithm for unmanned aerial vehicles (UAVs). UAVs have been deployed for agriculture research and management, surveillance and sensor coverage for threat detection and disaster search and rescue operations. It is essential for the aircraft to have on-board collision avoidance capability to guarantee safety. Instead of the traditional approach of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking time varying control sets for the ego aircraft given the predicted intruder reachable tube. We have applied the approach on a case study of two quadrotors collision avoidance scenario.

Binary Hypothesis Testing by Two Collaborating Observers: A Fresh Look

Abstract: We consider the binary hypothesis testing problem with two observers. There are two possible states of nature (or hypotheses). Observations are collected by two observers. The observations are statistically related to the true state of nature. Given the observations, the objective of both observers is to find out what is the true state of nature. We present three different approaches to address the problem. In the first (centralized) approach, the observations collected by both observers are sent to a central coordinator where hypothesis testing is performed. In the second approach, each observer performs hypothesis testing based on locally collected observations. At every time step decision information is exchanged until consensus is achieved. In the third approach, sequential hypothesis testing problem is formulated for each observer. The sequential hypothesis testing problem is solved for each observer using locally collected observations. Taking into account the asymmetric and random stopping times of the observers, a consensus algorithm has been designed. Numerical study has been done to assess the performance of the three approaches.

Stochastic differential linear-quadratic games with intermittent asymmetric observations

Abstract: In this paper, we consider a two-players stochastic linear quadratic game framework. The game is partially observed and each player has their own private observation. The challenge is that none of the players has access to the continuum observations, rather they can access their respective observations at discrete time instances by operating a switch unanimously. The operation of the switch is costly and hence the gathering of the observations are costly. Each player is equipped with finite memory and she can only use the latest observation to construct the control strategy. The private observations of the players lead to a source of asymmetry in this game. Moreover, the players have different costs for operating the switch, which is another source of asymmetry. We study the structural properties of the Nash equilibrium for this particular class of problems and then we finally show that the switching problem simplifies to a bi-objective optimization problem.

Large deviations and applications

Abstract: Large deviations is concerned with the study of rare events and of small probabilities. Let Xi,  ≤ i ≤ n, be independent identically distributed (i.i.d.) real random variables with expectationm, and Xn = (X + . . . +Xn)/n their empirical mean. e law of large numbers shows that, for any Borel set A ⊂ R not containing m in its closure, P(Xn ∈ A) →  as n → ∞, but does not tell us how fast the probability vanishes. Large deviations theory gives us the rate of decay, which is exponential in n. Cramer’s theorem states that,