scholarly journals Multiband Radar Signal Coherent Processing Algorithm for Motion Target

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Tingjing Wang ◽  
Ying Zhang ◽  
Hua Zhao ◽  
Yanxin Zhang
Keyword(s):  
Radar Signal ◽  
Quadratic Term ◽  
Minimum Entropy ◽  
Signal Model ◽  
Quadratic Phase ◽  
Entropy Principle ◽  
Geometrical Theory Of Diffraction ◽  
And Performance ◽  
Difference Term ◽  
Phase Term

In real application, most aerial targets are movable. In this paper, an effective multiple subbands coherent processing method is proposed for moving target. Firstly, an echoed signal model of motion target based on geometrical theory of diffraction is established and the influence of velocity on range profile of the target is analyzed. Secondly, a method based on minimum entropy principle is used to compensate velocity. Then, incoherent factors including a quadratic phase term, a linear phase factor, a fixed factor, and an amplitude difference term are analyzed. Subsequently, efficient methods are applied to estimate other incoherent factors, except that the quadratic term is small enough to be ignored. Finally, the feasibility and performance of the proposed method are investigated through numerical simulation.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals Evaluation of a class of NLFM radar signals

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Sebastian Alphonse ◽  
Geoffrey A. Williamson
Keyword(s):  
Search Space ◽  
Frequency Function ◽  
Radar Signal ◽  
Optimized Design ◽  
Nonlinear Frequency ◽  
Signal Design ◽  
Signal Model ◽  
Radar Systems ◽  
Comprehensive Comparison

AbstractSignal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed, and the first comprehensive comparison of a range of signals proposed in the literature is conducted. The search is restricted to a set of nonlinear, frequency-modulated signals whose frequency function is monotonically nondecreasing and antisymmetric about the temporal midpoint. This set includes many signals designed for smaller sidelobes including our proposed odd polynomial frequency signal (OPFS) model and antisymmetric time exponentiated frequency modulated (ATEFM) signal model. The signal design is optimized based on autocorrelation sidelobe levels with constraints on the autocorrelation mainlobe width and leakage of energy outside the allowed bandwidth, and we compare our optimized design with the best signal found from parameterized signal model classes in the literature. The quality of the overall best such signal is assessed through comparison to performance of a large number of randomly generated signals from within the search space. From this analysis, it is found that the OPFS model proposed in this paper outperforms all other contenders for most combinations of the objective functions and is expected to be better than nearly all signals within the entire search set.

MIMO-OTH Radar: Signal Model for Arbitrary Placement and Signals With Non-Point Targets

2015 ◽  
Vol 63 (7) ◽  
pp. 1846-1857 ◽  
Author(s):  
Qian He ◽  
Xiaodong Li ◽  
Zishu He ◽  
Rick S. Blum
Keyword(s):  
Radar Signal ◽  
Signal Model

A New Knowledge Discovery Model for Extracting Diagnosis Rules of Manufacturing Process

2006 ◽  
Vol 505-507 ◽  
pp. 889-894
Author(s):  
Ying Chieh Tsai ◽  
Ching Hsue Cheng ◽  
Jing Rong Chang
Keyword(s):  
Knowledge Discovery ◽  
Manufacturing Process ◽  
Conveyor Belt ◽  
Membership Functions ◽  
Minimum Entropy ◽  
Manufacturing Line ◽  
Proposed Model ◽  
New Knowledge ◽  
Entropy Principle ◽  
Correlation Based Feature Selection

The knowledge obtained from the experience of monitoring manufacturing process is critical to guarantee good products produced at the end of manufacturing line. Recently, many methods have been developed for the described purpose above. In this paper, a new knowledge discovery model based on soft computing is proposed. The proposed model contains a new algorithm Modified Correlation-based Feature Selection (MCFS), a new algorithm Modified Minimum Entropy Principle Algorithm (MMEPA), and Variable Precision Rough Set Model (VP-model). After conducting a real case of monitoring the process of manufacturing industrial conveyor belt, some advantages of the proposed model are that (1) MCFS can quickly identifying and screening irrelevant, redundant, and noisy features for data reduction; (2) MMEPA can objectively construct membership functions of fuzzy sets for fuzzifing the reduced dataset; (3) VP-model can extract causal relationship rules for controlling product quality; (4) Extracted rules by the proposed knowledge discovery model are easily understood and interpretable.

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