Identification of Structured LTI MIMO State-Space Models

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.Comment: Accepted to IEEE Conference on Decision and Control (CDC) 201

Sparse plus low-rank identification for dynamical latent-variable graphical AR models

This paper focuses on the identification of graphical autoregressive models with dynamical latent variables. The dynamical structure of latent variables is described by a matrix polynomial transfer function. Taking account of the sparse interactions between the observed variables and the low-rank property of the latent-variable model, a new sparse plus low-rank optimization problem is formulated to identify the graphical auto-regressive part, which is then handled using the trace approximation and reweighted nuclear norm minimization. Afterwards, the dynamics of latent variables are recovered from low-rank spectral decomposition using the trace norm convex programming method. Simulation examples are used to illustrate the effectiveness of the proposed approach

TDLE: 2-D LiDAR Exploration With Hierarchical Planning Using Regional Division

Exploration systems are critical for enhancing the autonomy of robots. Due to the unpredictability of the future planning space, existing methods either adopt an inefficient greedy strategy or require a lot of resources to obtain a global solution. In this work, we address the challenge of obtaining global exploration routes with minimal computing resources. A hierarchical planning framework dynamically divides the planning space into subregions and arranges their orders to provide global guidance for exploration. Indicators that are compatible with the subregion order are used to choose specific exploration targets, thereby considering estimates of spatial structure and extending the planning space to unknown regions. Extensive simulations and field tests demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based approaches. Our code has been made public for further investigation.Comment: Accepted in IEEE International Conference on Automation Science and Engineering (CASE) 202

A fringe projection profilometry scheme based on embedded speckle patterns and robust principal component analysis

2019 SPIE. Phase unwrapping is one of the key steps for fringe projection profilometry (FPP)-based 3D shape measurements. Conventional spatial phase unwrapping schemes are sensitive to noise and discontinuities, which may suffer from low accuracies. Temporal phase unwrapping is able to improve the reliability but often requires the acquisition of additional patterns, increasing the measurement time or hardware costs. This paper introduces a novel phase unwrapping scheme that utilizes composite patterns consisting of the superposition of standard sinusoidal patterns and randomly generated speckles. The low-rankness of the deformed sinusoidal patterns is studied. This is exploited together with the sparse nature of the speckle patterns and a robust principal component analysis (RPCA) framework is then deployed to separate the deformed fringe and speckle patterns. The cleaned fringe patterns are used for generating the wrapped phase maps using the standard procedures of phase shift profilometry (PSP) or Fourier Transform profilometry (FTP). Phase unwrapping is then achieved by matching the deformed speckle patterns that encode the phase order information. In order to correct the impulsive fringe order errors, a recently proposed postprocessing step is integrated into the proposed scheme to refine the phase unwrapping results. The analysis and simulation results demonstrate that the proposed scheme can improve the accuracy of FPP-based 3D shape measurements by effectively separating the fringe and speckle patterns

Blind system identification with application to medical imaging

Blind system identification is to identify a particular system model using only the system output under some mild conditions on the system input and the system model. It has been intensively explored for a few decades and is still a hot research topic nowadays. In this thesis, we shall investigate the blind identification problem for the following system models: linear time-invariant (LTI) infinite impulse response (IIR) or autoregressive moving average (ARMA) model, Hammerstein model, output-switching model, and single-input multi-output (SIMO) finite impulse response (FIR) system model with quantized outputs. The blind identification of SIMO FIR systems is a well-studied topic; however, it is not the case for SIMO IIR systems. In this thesis, a second-order statistics based identification method is developed for SIMO IIR systems. The proposed method exploits the dynamical autoregressive information of the system contained in the autocorrelation matrices of the system outputs so that the system can be identified using only the lag-0 and lag-1 autocorrelation matrices. Further, the blind identification of single-input single-output (SISO) IIR plants is studied. Using the over-sampling technique, the SISO IIR system is equivalently transformed into an SIMO IIR system so that it can be identified using the proposed second-order statistics based identification method. We also study the blind identification for Hammerstein systems and output-switching systems. Based on the over-sampling technique, deterministic blind identification approaches and sufficient identifiability conditions are provided for the Hammerstein system and the output-switching system. The involved over-sampling rate in the blind identification of the Hammerstein system can be much smaller than the system order of its linear dynamic part as required by other existing methods. For the output-switching system, the design of over-sampling strategy so as to make the system identifiable is deliberately discussed. The difficulty of identifying the output-switching system is that multiple IIR filters are to be identified from a single system output. Next, the blind identification of the SIMO system using quantized observations is investigated. Since the system outputs are nonlinearly distorted and the system inputs are unknown, the identification problem is quite challenging. First, a two-channel SIMO system model with precise observations is transformed into an error-in-variables model, and a maximum likelihood estimator is provided which can obtain consistent estimates of the system parameters. A consistent estimate means that the associated estimation error decays to zero with probability one when the number of observation samples tends to infinity. When only quantized observations are available, an expectation-maximization (EM) like algorithm is then provided. Asymptotic properties of the proposed algorithm are analyzed and the quantization effects on the identification performance are discussed. As the applications of the blind system identification technique, blind deconvolution of ultrasound imaging and blind reconstruction of parallel magnetic resonance imaging (MRI) are studied in this thesis. For the blind deconvolution of ultrasound imaging, an SIMO channel model is introduced to describe the imaging process, in which the ultrasound pulse is the common system input and tissue reflectivity functions are the channel impulse responses. A regularized blind deconvolution model is then proposed based on the prior knowledge that the tissue reflectivity functions are sparse and the spectrum of the ultrasound pulse is smooth. The parallel MRI is an SIMO system with the hydrogen proton density function being the common system input and the multiple coil sensitivity functions being the channel impulse responses. A total variation (TV) regularized model is then provided for the reconstruction of MR images using measurements obtained from arbitrary sampling patterns.Doctor of Philosophy (EEE

Multi-Agent Task Allocation with Multiple Depots Using Graph Attention Pointer Network

The study of the multi-agent task allocation problem with multiple depots is crucial for investigating multi-agent collaboration. Although many traditional heuristic algorithms can be adopted to handle the concerned task allocation problem, they are not able to efficiently obtain optimal or suboptimal solutions. To this end, a graph attention pointer network is built in this paper to deal with the multi-agent task allocation problem. Specifically, the multi-head attention mechanism is employed for the feature extraction of nodes, and a pointer network with parallel two-way selection and parallel output is introduced to further improve the performance of multi-agent cooperation and the efficiency of task allocation. Experimental results are provided to show that the presented graph attention pointer network outperforms the traditional heuristic algorithms

A new deterministic identification approach to hammerstein systems

The deterministic identification of Hammerstein systems is investigated in this paper. Based on the over-sampling technique, a new deterministic identification approach is presented, which blindly identifies the linear dynamic part followed by the estimation of the nonlinear function. The proposed method allows us to identify the Hammerstein system using an over-sampling rate smaller than the numerator polynomial's length of the linear dynamic part as required by other existing methods. In addition, it can obtain the true values of the system parameters in the noise-free case and an asymptotically consistent estimate in the presence of noise. The richness condition of the system input and the selection of the over-sampling rate are studied for the identifiability of the Hammerstein system. Simulation examples are given to show the performance of the proposed method

Nucleic acids research

This paper presents a blind identification algorithm for single-input single-output (SISO) plants using an oversampling technique with each input symbol lasting for several sampling periods. First, a state-space equation of the multi-rate sampled plant is given and its associated single-input multi-output (SIMO) autoregressive moving average (ARMA) model is formulated. A new blind identification algorithm for the SIMO ARMA model is then presented, which exploits the dynamical autoregressive information of the model contained in the autocorrelation matrices of the system outputs but does not require the block Toeplitz structure of the channel convolution matrix used by classical subspace methods. A method for recovering the transfer function of the SISO system from its associated SIMO transfer functions is further given based on the polyphase interpretation of multi-rate systems. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results