Monitoring the current trajectories of autonomous heavy platforms moving along the quarry routes of mining enterprises

In order to obtain information about the generated current trajectories (CT) of unmanned mining dump trucks, in the software and hardware complexes of the computer-aided dispatching system (in the external control subsystem and the autonomous control subsystem) installed on-board of an (AHP), one-dimensional (scalar) continuous signals (hereinafter converted into discrete digital ones) with a time-dependent instantaneous frequency, the so-called chirp signals, are put in accordance with the current trajectories of the AHP. This approach makes it possible to continuously monitor and manage the dynamics of current AHP trajectories with a high degree of efficiency. Note that for the purpose of information-rich and semantically transparent representation of information about the current state of the AHP CT, the chirp signals of the CT are converted into multidimensional Cohen’s class time-frequency wavelet distributions. The Wigner-Ville distribution (hereinafter referred to as the Wigner distribution) is selected as a working tool for performing computational procedures in the hardware / software module. This distribution is based on the Gabor basis wavelet functions and the wavelet matching pursuit algorithm. The choice of Gabor wavelets as the main ones is explained by their sinusoidal-like shape, since they are sinusoidal signals modulated by the Gauss window. On the other hand, the analyzed 1D-signals indicating the current position of the AHP on the route are also sinusoidal-like. This makes it possible to approximate current signals with high accuracy based on their comparison with the wavelet functions selected from the redundant wavelet dictionary. This approximation is adaptive, since it is performed on separate local fragments of the signal analyzed depending on approximating wavelets. This is the essence of the wavelet matching pursuit algorithm. The resulting wavelet series is then transformed into the Wigner time-frequency distribution, which is used to form a corresponding CT. As an example, reconstructions of time-frequency distributions (TFD) are given, corresponding to the deviation of a certain CT to the left (the trajectory signal decreases exponentially) and to the right (the CT-signal increases) from the nominal axial trajectory (NAT). The calculated scalar signal and its TFD for the AHP CT deviating to the left from NAT are also presented. In addition, on the basis of theoretical explanations the calculated linear-increasing TFD is demonstrated, corresponding to the CT-deviation to the right from NAT, and the time invariant stationary TFD characterizing the movement of AHP along the NAT line. In conclusion, based on the results obtained, it is concluded that the most appropriate ways to monitor the current trajectories of AHP movement and procedures for processing the corresponding signals are the operations implemented in computer-aided subsystems of external and autonomous control and based on such concepts as the Cohen’s class wavelet distributions, Gabor redundant dictionary of wavelet functions, the wavelet matching pursuit algorithm, and the representation of technological chirp-signals, as well as frequency-stationary signals about the current AHP trajectories represented in the wavelet medium. In this connection, the authors concluded that the procedures realizing the current monitoring of AHP movement on open pit mine routes and implementing the process of analyzing a relevant dynamic change in current trajectories, described in the article and embedded in software and hardware autonomous and external control subsystems of “Smart quarry” are adequate for performing required functions. The introduction of the principles of computer-aided controlling the unmanned mining vehicles allows you to optimize labor costs for the operation of mining equipment, reduce the cost of current work, and attract highly qualified specialists for the development and operation of innovative transport equipment.

Matching Pursuit Algorithm for Decoding of Binary LDPC Codes

This paper presents a novel hard decision decoding algorithm for low-density parity-check (LDPC) codes, in which the stand matching pursuit (MP) is adapted for error pattern recovery from syndrome over GF(2). In this algorithm, the operation of inner product can be converted into XOR and accumulation, which makes the matching pursuit work with a high efficiency. In addition, the maximum iteration is theoretically explored in relation to sparsity and error probability according to the sparse theory. To evaluate the proposed algorithm, two MP-based decoding algorithms are simulated and compared over an AWGN channel, i.e., generic MP (GMP) and syndrome MP (SMP). Simulation results show that the GMP algorithm outperforms the SMP by 0.8 dB at BER = 10 − 5 .

Super-resolution for doubly-dispersive channel estimation

In this work we consider the problem of identification and reconstruction of doubly-dispersive channel operators which are given by finite linear combinations of time-frequency shifts. Such operators arise as time-varying linear systems for example in radar and wireless communications. In particular, for information transmission in highly non-stationary environments the channel needs to be estimated quickly with identification signals of short duration and for vehicular application simultaneous high-resolution radar is desired as well. We consider the time-continuous setting and prove an exact resampling reformulation of the involved channel operator when applied to a trigonometric polynomial as identifier in terms of sparse linear combinations of real-valued atoms. Motivated by recent works of Heckel et al. we present an exact approach for off-the-grid super-resolution which allows to perform the identification with realizable signals having compact support. Then we show how an alternating descent conditional gradient algorithm can be adapted to solve the reformulated problem. Numerical examples demonstrate the performance of this algorithm, in particular in comparison with a simple adaptive grid refinement strategy and an orthogonal matching pursuit algorithm.

Fast Matching Pursuit with Multi-Gabor Dictionaries

Finding the best K -sparse approximation of a signal in a redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit algorithms are generally used for this task. In this work, we present an acceleration technique and an implementation of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency dictionaries, each of which consists of translations and modulations of a possibly different window and time and frequency shift parameters. The technique is based on pre-computing and thresholding inner products between atoms and on updating the residual directly in the coefficient domain, i.e., without the round-trip to the signal domain. Since the proposed acceleration technique involves an approximate update step, we provide theoretical and experimental results illustrating the convergence of the resulting algorithm. The implementation is written in C (compatible with C99 and C++11), and we also provide Matlab and GNU Octave interfaces. For some settings, the implementation is up to 70 times faster than the standard Matching Pursuit Toolkit.

A Novel Sparse Data Reconstruction Algorithm for Dynamically Detect and Adjust Signal Sparsity

This paper proposed a novel algorithm which is called the joint step-size matching pursuit algorithm (JsTMP) to solve the issue of calculating the unknown signal sparsity. The proposed algorithm falls into the general category of greedy algorithms. In the process of iteration, this method can adjust the step size and correct the indices of the estimated support that were erroneously selected in a dynamical way. And it uses the dynamical step sizes to increase the estimated sparsity level when the energy of the residual is less than half of that of the measurement vectory. The main innovations include two aspects: 1) The high probability of exact reconstruction, comparable to other classical greedy algorithms reconstruct arbitrary spare signal. 2) The sinh() function is used to adjust the right step with the value of the objective function in the late iteration. Finally, by following this approach, the simulation results show that the proposed algorithm outperforms state of- the-art similar algorithms used for solving the same problem.

Under-Determined Blind Source Separation

The Under-determined Blind Source Separation problem aims at estimating N source signals, with only a given set of M known mixtures, where M < N. The problem is solved by a two-stage approach. The rst stage is the estimation of the unknown mixing matrix. The contributions made unravel a more precise and accurate tool which directly relates to the initialization of the clustering algorithm. Di erent schemes such as segmentation, correlation and least square curve tting are used to take advantage of the sparsity of the sources. A signi cant addition involves applying linear transforms to produce a higher sparse domain. Further, the second stage is the sparse source recovery using a Matching Pursuit algorithm. The contributions involve a Matching Pursuit algorithm with di