Data assimilation has been widely used for improvement of crop models’ estimates, for example to incorporate the effects of external events or compensate calibration errors in large areas. The term describes multiple approaches for those who want to take advantage of satellite imagery to reduce uncertainty or improve accuracy of model estimates. Kalman Filters are among the most used methods for achieving these goals. But their use in new contexts, i.e., from open field to protected environments, requires untangling aspects of the pipeline that are often performed in many different ways without guidelines, such as which variables to assimilate or how to ascribe uncertainty to observations or model estimates. This study is then divided in two parts. In the first, we review details on how uncertainty is ascribed on crop model estimates and in observations for applications of the Kalman Filter and three variations of the method, i.e., the Extended, Unscented and Ensemble, as well as which state variables are often updated and the frequency with which assimilation may occur, as well as how these aspects are connected to each other. In the second part, we apply different approaches from the reviewed literature in a greenhouse tomato crop model. We use artificial data with controlled noise levels as well as artificial data generated by simulation using other tomato crop model. We assess the impacts of using different methods and different approaches for ascribing uncertainty in model estimates and in observations, by assimilating artificial observations of fruit and of mature fruit biomass. We note that covariances should not be fixed values, that there are trade-offs between ascribing model uncertainty to the state itself and to other elements of the process, that observation covariance may have been considered disproportionality higher when using some ensemble generation approaches in the EnKF, and that bias in model estimates may lead to worse outcomes even when observations are high-quality ones. While we discussed aspects that should be considered in a new environment, many of them are also important for field crops, and we concluded assimilation should follow an assessment of which variables could be useful for assimilation.

A solução clássica para o problema de estimação dos ângulos de chegada (DOA) de sinais incidindo em um arranjo de sensores é a aplicação do método de máxima verossimilhança. Este método leva ao problema de otimização de uma função custo não-linear, não-quadrática, multimodal e variante com a relação sinal-ruído (SNR). Os métodos propostos para tal tarefa, presentes na literatura, falham em uma ampla gama de valores de SNR. Este trabalho apresenta os resultados de um estudo sobre a aplicação de ferramentas pertencentes à computação natural ao problema de estimação DOA. Simulações demonstram que quatro dos algoritmos analisados alcançam o ótimo global para uma ampla faixa de valores de SNR, com esforços computacionais inferiores àquele exigido por uma busca exaustiva.

The classical solution to the problem of estimating the direction of arrival (DOA) of plane waves impinging on a sensor array is based on the application of the maximum likelihood method. This approach leads to the problem of optimizing a cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-noise ratio (SNR). The methods proposed in the literature to solve this problem fail for a wide set of SNR values. This work presents the results obtained from a study on the application of natural computing algorithms to the DOA estimation problem. Computational simulations show that four of the analyzed algorithms find the global optimum for a broad range of SNR values with computational efforts lower than that associated with an exaustive search.

Neste artigo são abordados critérios usados para resolver o problema da equalização cega também conhecida como autodidata. Consideram-se os critérios clássicos do módulo constante e o do Shalvi-Weinstein. Apresentaremos os principais resultados existentes na literatura e alguns resultados mais recentes, que dizem respeito ao estudo do algoritmo do módulo constante generalizado (GCMA) e à comparação entre os critérios citados e destes com o critério de Wiener.

In this work, we study the criteria used to solve the blind equalization problem. Two approaches are considered in detail: the constant modulus and the Shalvi-Weinstein criteria. In the course of our exposition, a more recent and less studied technique, the generalized constant modulus criterion, is also discussed. Some of the most important results found in the literature are presented together with some recent contributions related to the comparison between blind criteria and between unsupervised techniques and the Wiener criterion.