A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Chen, Ying; Liu, Yanjun; Chen, Jing; Ma, Junxia

doi:10.1109/access.2020.2976862

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…A large amount of literature points out that there are many methods for solving the unconstrained optimization problem in (29). In this paper, an improved method with low complexity proposed in [46] is used to solve this problem.…”

Section: Improved Parameter Estimationmentioning

confidence: 99%

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Wang,

Cao,

Yeo

et al. 2024

Remote Sensing

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Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

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Section: Improved Parameter Estimationmentioning

confidence: 99%

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Wang,

Cao,

Yeo

et al. 2024

Remote Sensing

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“…ere exist many identification algorithms, for example, the least squares (LS) algorithm [4,5], the gradient descent (GD) algorithm [6,7], and the particle swarm optimization (PSO) algorithm [8,9]. When the considered model has a high order, the LS algorithm and the PSO algorithm are inefficient for their heavy computational efforts [10][11][12]. e GD algorithm has few computational efforts, but with slow convergence rates [13,14].…”

Section: Introductionmentioning

confidence: 99%

Exhaustive Search and Power‐Based Gradient Descent Algorithms for Time‐Delayed FIR Models

Chen

2022

Complexity

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In this study, two modified gradient descent (GD) algorithms are proposed for time-delayed models. To estimate the parameters and time-delay simultaneously, a redundant rule method is introduced, which turns the time-delayed model into an augmented model. Then, two GD algorithms can be used to identify the time-delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: (1) avoid a high-order matrix eigenvalue calculation, thus, are more efficient for large-scale systems; (2) have faster convergence rates, therefore, are more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms.

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“…In engineering practices, the linear model usually cannot well catch the dynamics of the systems, while the nonlinear model can [23]- [25]. Therefore, nonlinear model identification is more important.…”

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confidence: 99%

Fractional Stochastic Gradient Algorithm for Time-Delayed Models With Piece-Wise Linear Input Using Self-Organizing Maps Method

Tang

2022

IEEE Access

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Although stochastic gradient algorithm can identify linear systems with high efficiency. It is inefficient for nonlinear systems for the difficulty in the step-size designing. To overcome this dilemma, this paper proposes a fractional stochastic gradient algorithm for systems with piece-wise linear input. First, the nonlinear system is transformed into a polynomial nonlinear model, then the parameters and time-delay are estimated iteratively based on the fractional stochastic gradient algorithm and self-organizing maps method. In addition, to increase the convergence rates of the fractional stochastic gradient algorithm, a multi-innovation fractional stochastic gradient algorithm is developed. Convergence analysis and simulation examples are introduced to show the effectiveness of the proposed algorithms. INDEX TERMSsystem identification; fractional stochastic gradient algorithm; multi-innovation; piecewise linear; nonlinear model; self-organizing maps Parameter estimation plays an important role in control theory and application [1]-[3]. A robust controller or an accurate predicted model is designed based on accurate parameters of the dynamic systems [4]-[7]. The Stochastic Gradient (SG) and Least Squares (LS) algorithms are two classical identification algorithms. The LS algorithm updates the parameters by solving a derivative function, it has faster convergence rates but with the cost of heavier computational efforts when compared with the SG algorithm [8], [9]. In addition, if the considered model has a complex structure, the derivative function may not have analytic solutions. Therefore, the LS algorithm can be inefficient for systems with complex nonlinear structures.

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A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Cited by 4 publications

References 25 publications

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Exhaustive Search and Power‐Based Gradient Descent Algorithms for Time‐Delayed FIR Models

Fractional Stochastic Gradient Algorithm for Time-Delayed Models With Piece-Wise Linear Input Using Self-Organizing Maps Method

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