arXiv subjects

Lihua Xie

Publications and source records attributed to Lihua Xie.

At least 235 records · Page 13Linked to original sources

arXiv2019

Robust Target-relative Localization with Ultra-Wideband Ranging and Communication

In this paper we propose a method to achieve relative positioning and tracking of a target by a quadcopter using Ultra-wideband (UWB) ranging sensors, which are strategically installed to help retrieve both relative position and bearing between the quadcopter and target. To achieve robust localization for autonomous flight even with uncertainty in the speed of the target, two main features are developed. First, an estimator based on Extended Kalman Filter (EKF) is developed to fuse UWB ranging measurements with data from onboard sensors including inertial measurement unit (IMU), altimeters and optical flow. Second, to properly handle the coupling of the target's orientation with the range measurements, UWB based communication capability is utilized to transfer the target's orientation to the quadcopter. Experiment results demonstrate the ability of the quadcopter to control its position relative to the target autonomously in both cases when the target is static and moving.

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arXiv2019

Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints

This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. Without assuming Slater's condition, we first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated dynamic variation of the comparator sequence, the number of agents, and the network connectivity. As a result, under some natural decreasing stepsize sequences, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated dynamic variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation. In addition, smaller bounds on the static regret are achieved when the objective function is strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

math.OC↗

arXiv2019

A Unified Strategy for Solution Seeking in Graphical N-coalition Noncooperative Games

This paper aims to reduce the communication and computation costs of the Nash equilibrium seeking strategy for the $N$-coalition noncooperative games proposed in [1]. The objective is achieved in two manners: 1. An interference graph is introduced to describe the interactions among the agents in each coalition. 2. The Nash equilibrium seeking strategy is designed with the interference graphs considered. The convergence property of the proposed Nash equilibrium seeking strategy is analytically investigated. It is shown that the agents' actions generated by the proposed method converge to a neighborhood of the Nash equilibrium of the graphical $N$-coalition noncooperative games under certain conditions. Several special cases where there is only one coalition and/or there are coalitions with only one agent are considered. The results for the special cases demonstrate that the proposed seeking strategy achieves the solution seeking for noncooperative games, social cost minimization problems and single-agent optimization problems in a unified framework. Numerical examples are presented to support the theoretical results.

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arXiv2019

Remote State Estimation with Stochastic Event-triggered Sensor Schedule in the Presence of Packet Drops

This paper studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. It is shown that the system state conditioned on the available information at the estimator side is Gaussian mixture distributed. Minimum mean square error (MMSE) estimators are subsequently derived for both open-loop and closed-loop schedules. Since the optimal estimators require exponentially increasing computation and memory, sub-optimal estimators to reduce the computational complexities are further provided. In the end, simulations are conducted to illustrate the performance of the optimal and sub-optimal estimators.

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arXiv2018

On the Sample Complexity of Multichannel Frequency Estimation via Convex Optimization

The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring, wireless communications, and more. Recently proposed atomic norm methods have attracted considerable attention due to their provable superiority in accuracy, flexibility and robustness compared with conventional approaches. In this paper, we analyze atomic norm minimization for multichannel frequency estimation from noiseless compressive data, showing that the sample size per channel that ensures exact estimation decreases with the increase of the number of channels under mild conditions. In particular, given $L$ channels, order $K\left(\log K\right) \left(1+\frac{1}{L}\log N\right)$ samples per channel, selected randomly from $N$ equispaced samples, suffice to ensure with high probability exact estimation of $K$ frequencies that are normalized and mutually separated by at least $\frac{4}{N}$. Numerical results are provided corroborating our analysis.

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arXiv2018

Distributed Consensus over Markovian Packet Loss Channels

This paper studies the consensusability problem of multi-agent systems (MASs), where agents communicate with each other through Markovian packet loss channels. We try to determine conditions under which there exists a linear distributed consensus controller such that the MAS can achieve mean square consensus. We first provide a necessary and sufficient consensus condition for MASs with single input and i.i.d.\ channel losses, which complements existing results. Then we proceed to study the case with identical Markovian packet losses. A necessary and sufficient consensus condition is firstly derived based on the stability of Markov jump linear systems. Then a numerically verifiable consensus criterion in terms of the feasibility of linear matrix inequalities (LMIs) is proposed. Furthermore, analytic sufficient conditions and necessary conditions for mean square consensusability are provided for general MASs. The case with nonidentical packet loss is studied subsequently. The necessary and sufficient consensus condition and a sufficient consensus condition in terms of LMIs are proposed. In the end, numerical simulations are conducted to verify the derived results.

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arXiv2018

An integrated localization-navigation scheme for distance-based docking of UAVs

In this paper we study the distance-based docking problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously achieve the relative localization and navigation tasks for discrete-time integrators under bounded velocity: a nonlinear adaptive estimation scheme to estimate the relative position to the landmark, and a delicate control scheme to ensure both the convergence of the estimation and the asymptotic docking at the given landmark. A rigorous proof of convergence is provided by invoking the discrete-time LaSalle's invariance principle, and we also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a GPS-less environment.

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arXiv2018

Least-square based recursive optimization for distance-based source localization

In this paper we study the problem of driving an agent to an unknown source whose location is estimated in real-time by a recursive optimization algorithm. The optimization criterion is subject to a least-square cost function constructed from the distance measurements to the target combined with the agent's self-odometry. In this work, two important issues concerning real world application are directly addressed, which is a discrete-time recursive algorithm for concurrent control and estimation, and consideration for input saturation. It is proven that with proper choices of the system's parameters, stability of all system states, including on-board estimator variables and the agent-target relative position can be achieved. The convergence of the agent's position to the target is also investigated via numerical simulation.

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arXiv2018

Consensusability of Multi-agent Systems with Delay and Packet Dropout Under Predictor-like Protocols

This paper considers the consensusability of multi-agent systems with delay and packet dropout. By proposing a kind of predictor-like protocol, sufficient and necessary conditions are given for the mean-square consensusability in terms of system matrices, time delay, communication graph and the packetdrop probability. Moreover, sufficient and necessary conditions are also obtained for the formationability of multi-agent systems.

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arXiv2018

Kernel Cross-Correlator

Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at https://github.com/wang-chen/KCC

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arXiv2018

Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.

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arXiv2018

Iterative Learning Economic Model Predictive Control

An iterative learning based economic model predictive controller (ILEMPC) is proposed for repetitive tasks in this paper. Compared with existing works, the initial feasible trajectory of the proposed ILEMPC is not restricted to be convergent to an equilibrium so it can handle various types of control objectives: stabilization, tracking a periodic trajectory and even pure economic optimization. The controller can learn from the previous closed-loop trajectory, resulting in a performance which is guaranteed to be no worse than the previous one. Under some standard assumptions in model predictive control, we show that recursive feasibility is ensured. Furthermore, for stabilization problem, the convergence of each learned trajectory and the learning process are established provided the initial trajectory is convergent. Numerical examples show that the proposed control strategy works well for different types of control tasks and systems.

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arXiv2018

Output Feedback Control Based on State and Disturbance Estimation

Recently developed control methods with strong disturbance rejection capabilities provide a useful option for control design. The key lies in a general concept of disturbance and effective ways to estimate and compensate the disturbance. This work extends the concept of disturbance as the mismatch between a system model and the true dynamics, and estimates and compensates the disturbance for multi-input multi-output linear/nonlinear systems described in a general form. The results presented do not need to assume the disturbance to be independent of the control inputs or satisfy a certain matching condition, and do not require the system to be expressible in an integral canonical form as required by algorithms previously described in literature. The estimator and controller are designed under a state tracking framework, and sufficient conditions for the closed-loop stability are presented. The performance of the resulting controller relies on a co-design of the system model, the state and disturbance observer, and the controller. Numerical experiments on a first-order system and an inverted pendulum under uncertainties are used to illustrate the control design method and demonstrate its efficacy.

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arXiv2017

Ultra-Wideband Aided Fast Localization and Mapping System

This paper proposes an ultra-wideband (UWB) aided localization and mapping system that leverages on inertial sensor and depth camera. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A general framework is developed which consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. The other mapping thread integrates visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that an optimization is performed to provide robust trajectory estimation. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments.

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arXiv2017

Frequency-Selective Vandermonde Decomposition of Toeplitz Matrices with Applications

The classical result of Vandermonde decomposition of positive semidefinite Toeplitz matrices, which dates back to the early twentieth century, forms the basis of modern subspace and recent atomic norm methods for frequency estimation. In this paper, we study the Vandermonde decomposition in which the frequencies are restricted to lie in a given interval, referred to as frequency-selective Vandermonde decomposition. The existence and uniqueness of the decomposition are studied under explicit conditions on the Toeplitz matrix. The new result is connected by duality to the positive real lemma for trigonometric polynomials nonnegative on the same frequency interval. Its applications in the theory of moments and line spectral estimation are illustrated. In particular, it provides a solution to the truncated trigonometric $K$-moment problem. It is used to derive a primal semidefinite program formulation of the frequency-selective atomic norm in which the frequencies are known {\em a priori} to lie in certain frequency bands. Numerical examples are also provided.

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arXiv2017

Non-Iterative SLAM

The goal of this paper is to create a new framework for dense SLAM that is light enough for micro-robot systems based on depth camera and inertial sensor. Feature-based and direct methods are two mainstreams in visual SLAM. Both methods minimize photometric or reprojection error by iterative solutions, which are computationally expensive. To overcome this problem, we propose a non-iterative framework to reduce computational requirement. First, the attitude and heading reference system (AHRS) and axonometric projection are utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds can be matched in independent spaces respectively. Second, based on single key-frame training, the matching process is carried out in frequency domain by Fourier transformation, which provides a closed-form non-iterative solution. In this manner, the time complexity is reduced to $\mathcal{O}(n \log{n})$, where $n$ is the number of matched points in each frame. To the best of our knowledge, this method is the first non-iterative and online trainable approach for data association in visual SLAM. Compared with the state-of-the-arts, it runs at a faster speed and obtains 3-D maps with higher resolution yet still with comparable accuracy.

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arXiv2017

Sparse Methods for Direction-of-Arrival Estimation

Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receiving antennas that form a sensor array. DOA estimation is a major problem in array signal processing and has wide applications in radar, sonar, wireless communications, etc. With the development of sparse representation and compressed sensing, the last decade has witnessed a tremendous advance in this research topic. The purpose of this article is to provide an overview of these sparse methods for DOA estimation, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm. Several future research directions are also discussed.

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arXiv2016

On Stochastic Sensor Network Scheduling for Multiple Processes

We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error (MMSE) estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing realtime information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving an Markov decision process (MDP) problem with average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.

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