Doppler Spectrum Estimation by Ramanujan Fourier Transforms

The Doppler spectrum estimation of a weather radar signal in a classic way can be made by two methods, temporal one based in the autocorrelation of the successful signals, whereas the other one uses the estimation of the power spectral density PSD by using Fourier transforms. We introduces a new tool of signal processing based on Ramanujan sums cq(n), adapted to the analysis of arithmetical sequences with several resonances p/q. These sums are almost periodic according to time n of resonances and aperiodic according to the order q of resonances. New results will be supplied by the use of Ramanujan Fourier Transform (RFT) for the estimation of the Doppler spectrum for the weather radar signal

Multi-Layered Optimal Navigation System For Quadrotors UAV

Purpose This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV). Design/methodology/approach The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS). Findings All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system. Practical implications The proposed controllers are easily implementable on-board and are computationally efficient. Originality/value The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly

A Fully Autonomous Search and Rescue System Using Quadrotor UAV

In order to deal with critical missions a growing interest has been shown to the UAVs design. Flying robots are now used fire protection, surveillance and search & rescue (SAR) operations. In this paper, a fully autonomous system for SAR operations using quadrotor UAV is designed. In order to scan the damaged area, speeds up the searching process and detect any possible survivals a new search strategy that combines the standard search strategies with the probability of detection is developed. Furthermore the autopilot is designed using an optimal backstepping controller and this enables the tracking of the reference path with high accuracy and maximizes the flying time. Finally a comparison between the applied strategies is made using a study case of survivals search operation. The obtained results confirmed the efficiency of the designed system

Distributed Obstacles Avoidance For UAVs Formation Using Consensus-based Switching Topology

Nowadays, most of the recent researches are focusing on the use of multi-UAVs in both civil and military applications. Multiple robots can offer many advantages compared to a single one such as reliability, time decreasing and multiple simultaneous interventions. However, solving the formation control and obstacles avoidance problems is still a big challenge. This paper proposes a distributed strategy for UAVs formation control and obstacles avoidance using a consensus-based switching topology. This novel approach allows UAVs to keep the desired topology and switch it in the event of avoiding obstacles. A double loop control structure is designed using a backstepping controller for tracking of the reference path, while a Sliding Mode Controller (SMC) is adopted for formation control. Furthermore, collaborative obstacles avoidance is assured by switching the swarm topology. Numerical simulations show the efficiency of the proposed strategy

Quadrotors trajectory tracking using a differential flatness-quaternion based approach

A quadrotors is a type of Unmanned Aerial Vehicles (UAV) systems that attract the researchers in the control field since it's a highly nonlinear, underactuated system. In this paper, a non-linear dynamic model based on quaternions is developed. Differential flatness is an approach that enables the optimization to occur within the output space and therefore simplifies the problem of the trajectory tracking. This work aim to combine both methods in order to create a differential flatness-quaternion based approach that enables the quadrotors to follow a desired optimal path and avoid any singularities that can occur. The trajectory tracking is assured by a double loop control structure based on the LQR controller

Trends summarization of times series: a multi-objective genetic algorithm-based model

Aim: The explosion of the amounts of data generated in many application domains, makes the paradigm of data summarization more essential. Furthermore, it is of a great interest to effectively handle some specific needs. In this work, we discuss an advanced model to drive linguistic summarization in the context of time series. This model relies on a multi-objective genetic algorithm mechanism to generate a set of best summaries from a large number of candidates.Methods: To achieve this objective, the current work is divided into two parts: The first part is dedicated for extracting the linguistic summaries of the dynamic characteristics of the trends of time series. It is achieved using the traditional genetic algorithm where the fitness function represents the truth degree of the linguistic quantified proposition. The second part is devoted to formalise the problem of interest as a multi-criteria optimization problem. We use different quality measures of summary as targets for improving the predicted set of summaries. To reach this goal, we use the Fast Non-Dominated Sorting Genetic Algorithm NSGA-II.Results: We evaluate the proposed approach on real data from a Smart Campus application (Neocampus project of the University of Toulouse, France). The results are promising and confirming the usability of the proposed approach.Conclusion: The proposed approach overcomes the problem of the overabundance of irrelevant linguistic summaries of the time series. It allows selecting a set of best summaries regarding some relevant criteria

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework

Quadrotors UAVs Swarming Control Under Leader-Followers Formation

Unmanned Aerial Vehicles (UAVs) swarming has took an important part of the recent researches. Multiple robots offer many advantages when comparing to a single one such as reliability, time decreasing and multiple simultaneous interventions. This paper proposes a new scheme for trajectory tracking of multiple quadrotors UAVs under a centralized leader-followers formation strategy. Attitude stability and position control are assured using a double loop control structure based on the Linear Quadratic Regulator (LQR), moreover the leader-followers formation is maintained via a Sliding Mode Controller (SMC) controller. Many scenarios are proposed within this work. Simulation results proof the energy optimization and formation controller's robustness and accuracy