Simulation and analysis of polarimetric radar signatures of human gaits

Park, James; Johnson, Joel T.; Majurec, Ninoslav; Frankford, Mark T.; Stewart, Kyle B.; Smith, Gerald Birney; Westbrook, LaMar

doi:10.1109/taes.2014.120792

Cited by 19 publications

(13 citation statements)

References 16 publications

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“…These algorithms are applied to many fields of machine learning because of being simple and efficient [32,33]. At the same time, the method this paper proposed is compared with the method proposed by Wang [34] and Sato [12].…”

Section: Results Contrastmentioning

confidence: 99%

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

Wang

Gong

et al. 2017

Entropy

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Abstract:Recently, Android malicious code has increased dramatically and the technology of reinforcement is increasingly powerful. Due to the development of code obfuscation and polymorphic deformation technology, the current Android malicious code static detection method whose feature selected is the semantic of application source code can not completely extract malware's code features. The Android malware static detection methods whose features used are only obtained from the AndroidManifest.xml file are easily affected by useless permissions. Therefore, there are some limitations in current Android malware static detection methods. The current Android malware dynamic detection algorithm is mostly required to customize the system or needs system root permissions. Based on the Dendritic Cell Algorithm (DCA), this paper proposes an Android malware algorithm that has a higher detection rate, does not need to modify the system, and reduces the impact of code obfuscation to a certain degree. This algorithm is applied to an Android malware detection method based on oriented Dalvik disassembly sequence and application interface (API) calling sequence. Through the designed experiments, the effectiveness of this method is verified for the detection of Android malware.

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Section: Results Contrastmentioning

confidence: 99%

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

Wang

Gong

et al. 2017

Entropy

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“…other movers [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The main limitation of the STFT, however, is that the resolution along both the time and frequency domains are controlled by the choice of the dwell time or short time window used in the transform.…”

Section: Topics In Radar Signal Processingmentioning

confidence: 99%

Representation of Radar Micro-Dopplers Using Customized Dictionaries

Ram¹

2018

Topics in Radar Signal Processing

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Human motions give rise to frequency modulations, known as micro-Dopplers, to continuous wave radar signals. Micro-Doppler signals have been extensively researched for the classification of different types of human motions as well as to distinguish humans from other moving targets. However, there are two main scenarios where the performance of existing algorithms deteriorates significantly-one, when the channel consists of multiple moving targets resulting in distorted signatures, and two, when the systems conditions during the training stage deviate significantly from the conditions during the test stage. In this chapter, it is demonstrated that both of these limitations can be overcome by representing the radar data through customized dictionaries, fine-tuned to provide sparser representations of the data, than traditional data-independent dictionaries such as Fourier or wavelets. The performances of the algorithms are evaluated with both simulated and measured radar data gathered from moving humans in indoor line-of-sight conditions.

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“…Early techniques using kinematic and radar cross-section characteristics [3] could not reliably differentiate UAVs from birds, as the both kinds are small and slow-moving targets. Many representations of micro-Doppler signature (mDS) have been explored, e.g., spectrogram [4][5][6][7][8][9][10][11], cepstrogram [12], cadence velocity diagram [13][14][15], others [16][17][18][19], and combinations of the aforementioned [20][21][22]. Particularly in [22], a rich source of features including spectrogram, cepstrogram and CVD are utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the representations are closely related to spectrogram. mDS has been utilized in many radar-target-recognition tasks [23], e.g., airplane classification [24], ship detection [25], human detection [5], gait recognition [8,9], action classification [10,14] and vehicle classification [26].…”

Section: Introductionmentioning

confidence: 99%

A three-step classification framework to handle complex data distribution for radar UAV detection

Ren

Jiang

2021

Pattern Recognition

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Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches.

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Simulation and analysis of polarimetric radar signatures of human gaits

Cited by 19 publications

References 16 publications

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

Representation of Radar Micro-Dopplers Using Customized Dictionaries

A three-step classification framework to handle complex data distribution for radar UAV detection

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