Comparative Study Between Deep Learning Techniques and Random Forest Approach for HRRP Based Radar Target Classification

Sagayaraj, M.J.; Jithesh, V.; Roshani, Dange

doi:10.1109/icais50930.2021.9395855

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…[37] to distinguish between 3 aircraft, in ref. [38] to do a study between traditional ML algorithms and CNNs and in ref. [39] to recognise ground targets.…”

Section: High-resolution Range Profilementioning

confidence: 99%

“…High Range Resolution Profile (HRRP): the magnitude of each range cell scattered on the target creates a specific plot depending on where the target is facing relatively to the transmitter—receiver. Ideally, all angles of the target are desired to train the algorithm and consequently get better results [33–39];…”

Section: Introductionmentioning

confidence: 99%

“…A frequency plot along time-Time-Frequency Images (TFI)-can be used to classify the target [30][31][32]; � High Range Resolution Profile (HRRP): the magnitude of each range cell scattered on the target creates a specific plot depending on where the target is facing relatively to the transmitter-receiver. Ideally, all angles of the target are desired to train the algorithm and consequently get better results [33][34][35][36][37][38][39]; � Synthetic Aperture Radar (SAR) images can be used in several applications, usually for earth observations due to its capacity to provide images in any weather conditions and also at night time. This way, as in common optical images, targets can under certain conditions, be classified out of it [40].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence applications in Noise Radar Technology

Sénica,

Marques,

Figueiredo

2024

IET Radar Sonar & Navi

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Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.

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“…[37] to distinguish between 3 aircraft, in ref. [38] to do a study between traditional ML algorithms and CNNs and in ref. [39] to recognise ground targets.…”

Section: High-resolution Range Profilementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence applications in Noise Radar Technology

Sénica,

Marques,

Figueiredo

2024

IET Radar Sonar & Navi

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“…The other technique is utilizing the random forest model for classification. Random forest is an ensemble classification model [24,25] that models multiple decision trees. The basic principle of a random forest is to build multiple decision trees by randomly selecting partial features from the feature space.…”

Section: Multivariable Weather Relevance Modelling and Analysismentioning

confidence: 99%

Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets

Liu

et al. 2023

Aerospace

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Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model.

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“…The random forest algorithm was verified as a wellperforming methodology for the use in the lane-changing detection and decision-making processes [20][21][22][23][24], and it is broadly employed because of its simplicity and computational efficiency when compared to deep learning methods, such as the long short-term memory (LSTM) network and the convolutional neural network (CNN) [25]. Moreover, the random forest method has a high tolerance for the quality and quantity of the training dataset [26].…”

Section: Introductionmentioning

confidence: 99%

A novel merging strategy model considering the remaining distance in the acceleration lane

Wang

et al. 2023

IET Intelligent Trans Sys

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The driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane () during a merging decision‐making process. To investigate the impact of on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high‐resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish ‘Merge’ events and ‘Wait’ events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded influence. The proposed merging decision model can aid merging processes and give cues for human‐like merge decisions of automated vehicles.

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Comparative Study Between Deep Learning Techniques and Random Forest Approach for HRRP Based Radar Target Classification

Cited by 14 publications

References 18 publications

Artificial Intelligence applications in Noise Radar Technology

Artificial Intelligence applications in Noise Radar Technology

Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets

A novel merging strategy model considering the remaining distance in the acceleration lane

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