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Traditional monocular vision localization methods are usually suitable for short-range area and indoor relative positioning tasks. This paper presents MGG, a novel monocular global geolocation method for outdoor long-range targets. This method takes a single RGB image combined with necessary navigation parameters as input and outputs targets' GPS information under the Global Navigation Satellite System (GNSS). In MGG, we first design a camera pose correction method via pixel mapping to correct the pose of the camera. Then, we use anchor-based methods to improve the detection ability for long-range targets with small image regions. Next, the local monocular vision model (LMVM) with a local structure coefficient is proposed to establish an accurate 2D-to-3D mapping relationship. Subsequently, a soft correspondence constraint (SCC) is presented to solve the local structure coefficient, which can weaken the coupling degree between detection and localization. Finally, targets can be geolocated through optimization theory-based methods and a series of coordinate transformations. Furthermore, we demonstrate the importance of focal length on solving the error explosion problem in locating long-range targets with monocular vision. Extensive experiments on the challenging KITTI dataset as well as applications in outdoor environments with targets located at a long range of up to 150 meters show the superiority of our method.

In this article, a unified identification framework called constrained subspace method for structured state-space models (COSMOS) is presented, where the structure is defined by a user-specified linear or polynomial parametrization. The new approach operates directly from the input and output data, which differs from the traditional two-step method that first obtains a state-space realization followed by the system-parameter estimation. The new identification framework relies on a subspace inspired linear regression problem which may not yield a consistent estimate in the presence of process noise. To alleviate this problem, the linear regression formulation is imposed by structured and low-rank constraints in terms of a finite set of system Markov parameters and the user specified model parameters. The nonconvex nature of the constrained optimization problem is dealt with by transforming the problem into a difference-of-convex optimization problem, which is then handled by the sequential convex programming strategy. Numerical simulation examples show that the proposed identification method is more robust than the classical prediction-error method initialized by random initial values in converging to local minima, but at the cost of heavier computational burden. Funding Agencies|National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61991414, 61873301]; National Defense Pre-Research Foundation of China [61403120304]; European Research Council under the European UnionEuropean Research Council (ERC) [339681]

This note considers the identification of large-scale one-dimensional networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and the two-layer Toeplitz structural property in the data equation, whereas the state-space realization of a single subsystem is formulated as a structured low-rank matrix-factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.

This note studies the identification of individual systems operating in a large-scale distributed network by considering the interconnection signals between neighboring systems to be unmeasurable. The unmeasurable interconnections act as unknown system inputs to the individual systems in a network, which poses a challenge for the identification problem. A subspace identification framework is proposed in this note for the consistent identification of individual systems using only local input and output information. The key step of this identification framework is the accurate estimation of the unknown system inputs of individual systems using local observations. Sufficient identifiability conditions are provided for the proposed identification framework and a simulation example is given to demonstrate its performance.

The objective of adaptive optics (AO) system control is to design an output feedback controller to reduce the adverse effect of the phase aberration caused by the atmospheric turbulence. As the size of the telescope or AO system becomes larger and larger, how to improve the efficiency of the controller execution becomes an urgent but challenging problem. To this end, this paper presents a structured and sparse controller design method for the large-scale AO systems. A Kronecker structured turbulent phase model, inspired by the frozen-flow movement of the atmospheric turbulence, is developed first, following the design of a sparse controller gain under the $H_{2}$ -norm optimal control framework. Based on the Kronecker structured system matrices and the sparse controller gain, the obtained dynamical controller has a linear execution complexity in the dimension of the turbulent phase, which is even lower than the standard matrix-vector multiplication method. Since the proposed method is a preliminary result, which cannot be directly used in a telescope today, its performance is demonstrated by numerical simulations only.

This note studies the identification of a network comprised of interconnected clusters of LTI systems. Each cluster consists of homogeneous dynamical systems, and its interconnections with the rest of the network are unmeasurable. A subspace identification method is proposed for identifying a single cluster using only local input and output data. With the topology of the concerned cluster being available, all the LTI systems within the cluster are decoupled by taking a transformation on the state, input and output data. To deal with the unmeasurable interconnections between the concerned cluster and the rest of the network, the Markov parameters of the decoupled LTI systems are identified first by solving a nuclear-norm regularized convex optimization, following the state-space realization of a single LTI system within the cluster by solving another nuclear-norm regularized optimization problem. The effectiveness of the proposed identification method is demonstrated by a simulation example.

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms.

In this paper, we study the blind channel and source estimation in sensor networks, where the channels are modeled by FIR filters and the source signal is deterministic. Distributed estimation algorithms for networked systems under noise-free and noisy measurements are developed, which blindly identify the multiple channels, followed by the source signal estimation. The key to the proposed algorithms lies in the adaptation of the blind system identification technique for the distributed channel estimation. In the presence of measurement noises, conventional blind identification methods cannot be straightforwardly realized in distributed environments. Instead, two stable distributed algorithms are introduced, which can avoid trivial solutions for the blind identification problem. Convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

In this paper, a deterministic blind identification approach is proposed for linear output-switching systems, which are modeled by multiple infinite impulse-response (IIR) dynamic functions. By adopting a new over-sampling strategy, the concerned single-input-single-output (SISO) output-switching system is equivalently transformed into a time-invariant multi- input-multi-output (MIMO) system. Further, by exploring the mutual relations among the multiple inputs, the time-invariant MIMO system model and subsequently the output-switching system model are identified uniquely up to a scalar constant using the proposed identification approach. Sufficient identifiability conditions are provided for output-switching systems and numerical simulations are carried out to validate the proposed approach.

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.

This correspondence presents a new second-order statistical approach to blind identification of single-input multiple-output (SIMO) autoregressive and moving average (ARMA) system models. The proposed approach 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. For the multi-channel model with the same autoregressive (AR) polynomial, sufficient conditions and an efficient identification algorithm are given such that the multi-channel model can be uniquely identified up to a constant scaling factor. Furthermore, an extension of the result to blind identification of multi-channel models with different AR polynomials is presented. Simulation results are given to show the effectiveness of the proposed approach.