LSTM recurrent neural networks for high resolution range profile based radar target classification

Jithesh, V.; Sagayaraj, M.J.; Srinivasa, K. G.

doi:10.1109/ciact.2017.7977298

Cited by 42 publications

(21 citation statements)

References 5 publications

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“…LSTM architectures can be used effectively to classify signals or in general data on input [25,26]. For these reasons, the author has equipped the proposed LSTM based deep pipeline with a fullyconnected neuronal layer with a SoftMax layer and a classifier in cascade.…”

Section: Deep Learning Blockmentioning

confidence: 99%

“…where β represents the learning rate (defined as β = 0.15), ϕ p ξ c (k) represents a function that provides the close price quotations of the EUR/USD cross currency time-series being used as input data of the RL motor map while σ x t min , y t min represents the update function of the neighborhood of the winning neuron that in the case under examination has been implemented by the classic adaptive Gaussian function [25,29].…”

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confidence: 99%

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Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

Rundo

2019

Applied Sciences

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High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability.

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Section: Deep Learning Blockmentioning

confidence: 99%

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confidence: 99%

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

Rundo

2019

Applied Sciences

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“…Recurring neural network models were implemented using the TensorFlow library. LSTM RNN [4] For high-resolution range profile-based radar target classification, it is important to set a positive and timely target of the system in any military situation. The paper describes the analyze the utility of long-term memory recurrent neural networks (LSTM RNNs) based on the organization of radar targets based on high-resolution range profiles (HRRPs).…”

Section: Literature Reviewmentioning

confidence: 99%

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

Mulla,

Patil

2019

IJEAT

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Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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“…The range profiles of a target are aspect dependent in the sense they vary with look angle. Classification of radar targets based on their HRRP has been studied [12][13][14][15][16][17][18][19][20] . A recent study using long short-term memory -recurrent neural network (LSTM-RNN) may be found in Sagayaraj 19 , et al Application of convolutional neural network (CNN) in radar target classification problems based on SAR images and micro doppler signatures have also been studied [23][24][25] .…”

Section: Case Studies 61 Radar Target Classification Based On High-rmentioning

confidence: 99%

A Hybrid Approach to Cognition in Radars

et al. 2018

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In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.

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LSTM recurrent neural networks for high resolution range profile based radar target classification

Cited by 42 publications

References 5 publications

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

A Hybrid Approach to Cognition in Radars

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