We consider a joint radar parameter estimation and communication system using orthogonal time frequency space (OTFS) modulation. The scenario is motivated by vehicular applications where a vehicle ...(or the infrastructure) equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. Provided that the radar-equipped transmitter is ready to send data to its target receiver, this setting naturally assumes that the receiver has been already detected. In a point-to-point communication setting over multipath time-frequency selective channels, we study the joint radar and communication system from two perspectives, i.e., the radar parameter estimation at the transmitter as well as the data detection at the receiver. For the radar parameter estimation part, we derive an efficient approximated Maximum Likelihood algorithm and the corresponding Cramér-Rao lower bound for range and velocity estimation. Numerical examples demonstrate that multi-carrier digital formats such as OTFS can achieve as accurate radar estimation as state-of-the-art radar waveforms such as frequency-modulated continuous wave (FMCW). For the data detection part, we focus on separate detection and decoding and consider a soft-output detector that exploits efficiently the channel sparsity in the Doppler-delay domain. We quantify the detector performance in terms of its pragmatic capacity , i.e., the achievable rate of the channel induced by the signal constellation and the detector soft-output. Simulations show that the proposed scheme outperforms concurrent state-of-the-art solutions. Overall, our work shows that a suitable digitally modulated waveform enables to efficiently operate joint radar parameter estimation and communication by achieving full information rate of the modulation and near-optimal radar estimation performance. Furthermore, OTFS appears to be particularly suited to the scope.
In the present work, we investigate the dipion transitions between Υ(5S) and ΥJ(1D) with J = 1, 2, 3. Our analysis indicates that the dominant sources of the anomalously large widths of Υ(5S) → ...ΥJ(1D)π+π− should be Z(')b, i.e., the dipion transitions occur via the cascade decays Υ(5S) → Z(') ± bπ∓ → ΥJ(1D)π+π−. With the assumption that all the short ranged dynamics could be absorbed in a single cutoff with a model parameter α, the present estimations indicate that in a reasonable parameter range the measured branching ratios of Υ(5S) → ΥJ(1D)π+π− can be reproduced in magnitude, which further proves that the decays via Z(')b dominate the dipion transitions of Υ(5S) to ΥJ(1D). Moreover, we also predict the ratios of the branching fractions of Z(')b → ΥJ(1D)π, which in our calculations are largely independent of the parameter α and could be tested by further experiments in Belle II.
The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of ...PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.
According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, ...such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, a. The LF model produces sudden large jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e„ as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.
The parameter estimation problem of multivariable output-error-like systems with autoregressive moving average noises is investigated in this study, and the primary system is segregated into some ...subsystems and a subsystem generalised extended gradient-based iterative algorithm is presented according to the decomposition technique. Nevertheless, there exists the common parameter vector in each subsystem, which increases the calculation. By taking the mean value of the common parameter estimation vectors of the subsystems as the optimal estimate of the current iteration, and substituting it into the next iteration, a partially coupled subsystem generalised extended gradient-based iterative algorithm is proposed. Furthermore, in the cause of further deepening the coupled relationships between the common parameter estimation vectors of two subsystems and to reduce the computational cost and the redundant estimates, a partially coupled generalised extended gradient-based iterative algorithm is presented by making use of the coupling identification concept. Finally, the simulation results show that the coupled gradient-based iterative algorithms are effective.
Angel Ricardo Plastino and Angelo Plastino give a brief introduction to two key developments in statistics that originate with C. R. Rao's 1945 paper, “Information and accuracy attainable in the ...estimation of statistical parameters”
Angel Ricardo Plastino and Angelo Plastino give a brief introduction to two key developments in statistics that originate with C. R. Rao's 1945 paper, “Information and accuracy attainable in the estimation of statistical parameters”.
The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the ...latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.
Extracting parameters and constructing high-precision models of photovoltaic modules through actual current-voltage data is required for simulation, control, and optimization of a photovoltaic ...system. Because of the application of such problems, the identification of unknown parameters accurately and reliably remains a challenging task. In this paper, we propose an enhanced Harris Hawks Optimization (EHHO), which combines orthogonal learning (OL) and general opposition-based learning (GOBL), to estimate the parameters of solar cells and photovoltaic modules effectively and accurately. In EHHO, OL helps to improve the speed of the HHO method and the accuracy of the solution. At the same time, the GOBL mechanism can increase both diversity of the population and the HHO’s exploitation performance. In addition, these two mechanisms defend the equilibrium between the exploitation and exploration rates. The results show that accuracy, reliability, and other aspects of this method are better than most existing methods. Thus, we observed that EHHO can be used as an effective method for parameter estimation of solar cells and photovoltaic modules.
•Enhanced Harris Hawks Optimization is proposed to identify the parameters of photovoltaic cell models.•Multiple learning strategies balance the exploration and exploitation trends.•The exploitative trend is boosted with the aid of orthogonal learning strategy.•The exploratory behavior is enhanced based on general opposition-based learning.
The increasing integration of distributed energy resources calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or ...inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using nonzero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables model in a maximum-likelihood estimation framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization algorithm in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from Southern California Edison.
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