FMCW rail-mounted SAR: Porting spotlight SAR imaging from MATLAB to FPGA

In this work, a low-cost laptop-based radar platform derived from the MIT open courseware has been implemented. It can perform ranging, Doppler measurement and SAR imaging using MATLAB as the processor. In this work, porting the signal processing algorithms onto a FPGA platform will be addressed as well as differences between results obtained using MATLAB and those obtained using the FPGA platform. The target FPGA platforms were a Virtex6 DSP kit and Spartan3A starter kit, the latter was also low-cost to further reduce the cost for students to access radar technology

Porting Spotlight Range Migration Algorithm Processor from Matlab to Virtex 6

This paper describes the implementation and optimization of a Synthetic Aperture Radar process Spotlight Range Migration Algorithm processor on FPGA Virtex 6 DSP kit that fits on the chip. The mean/max error compared to a software implementation is -54/-28.74dB for 55 elements and 882 samples

Radar High Resolution Range & Micro-Doppler Analysis of Human Motions

In radar imaging it is well known that relative motion or deformation of parts of illuminated objects induce additional features in the Doppler frequency spectra. These features are called micro-Doppler effect and appear as sidebands around the central Doppler frequency. They can provide valuable information about the structure of the moving parts and may be used for identification purposes [1]. Previous papers have mostly focused on ID micro-Doppler analysis [2-4]. In this paper, we propose to emphasize the analysis of such "non stationary targets" using a 2D imaging space, using both the micro-Doppler and a high range resolution analysis. As in 2D-ISAR imaging, range separation enables us to better discriminate the various effects caused by the time varying reflectors. We will focus our study on human motion. We will see how micro-Doppler signature can be used to extract information on pedestrians gait. We will show examples on simulated and experimental data

Gait Analysis of Horses for Lameness Detection with Radar Sensors

This paper presents the preliminary investigation of the use of radar signatures to detect and assess lameness of horses and its severity. Radar sensors in this context can provide attractive contactless sensing capabilities, as a complementary or alternative technology to the current techniques for lameness assessment using video-graphics and inertial sensors attached to the horses' body. The paper presents several examples of experimental data collected at the Weipers Centre Equine Hospital at the University of Glasgow, showing the micro- Doppler signatures of horses and preliminary results of their analysis

Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor

This student paper presents a Quadratic-kernel Support Vector Machine (SVM) based FMCW (Frequency Modulated Continuous Wave) radar system to recognize daily activities and detect fall accidents. Data collected in this work is divided into two different collection modes, namely, snapshots mode (different activities individually collected in isolation) and continuous activity mode (continuous streams of activities collected one after the other). For the continuous activity streams, a sliding window approach with 4s duration and 70% overlapping has achieved 84.7% classification accuracy and subsequent improvement of 2.6% has been proved by using Sequential Forward Selection (SFS) on six participants to identify an optimal feature set. A ‘tracking’ graph has been utilized to verify that the radar system can correctly identify falls as critical events among the other activities

A case implementation of a spotlight range migration algorithm on FPGA platform

2014 International Symposium on Antennas and Propagation, ISAP 2014, Taiwan, 2-5 December 2014This paper presents a real-time implementation of a spotlight range migration algorithm processor on FPGA platform for the MIT open courseware radar. The modified radar platform is presented. The paper also describes the use of FPGA resources on a Virtex-6 DSP kit board, and compares the results obtained with the hardware implementation and the Matlab equivalent. A good match between the real-time and the offline processing of data was found.Department of Electronic and Information Engineerin

An intelligent implementation of multi-sensing data fusion with neuromorphic computing for human activity recognition

The increasing demand for considering multi-sensor data fusion technology has drawn attention for precise human activity recognition over standalone technology due to its reliability and robustness. This paper presents a framework that fuses data from multiple sensing systems and applies Neuromorphic computing to sense and classify human activities. The data is collected by utilizing Inertial Measurement Unit (IMU) sensors, software-defined radios, and radars and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after 2 steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to probabilistic statistics on inference predictions, the proposed method that Neuromorphic computing of the three data fused framework achieved the Box-plot for highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that Neuromorphic computing is most capable for multi-sensor data fusion-based human activity recognition. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data

Feature diversity for fall detection and human indoor activities classification using radar systems

none6siINSPEC Accession Number: 17667614noneShrestha, A.; Le Kernec, J.; Fioranelli, F.; Cippitelli, E.; Gambi, E.; Spinsante, S.Shrestha, A.; Le Kernec, J.; Fioranelli, F.; Cippitelli, E.; Gambi, E.; Spinsante, S

Sequential Human Gait Classification with Distributed Radar Sensor Fusion

This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those containing multiple gait styles performed one after the other, with natural transitions in between, including fall events developing from walking gait in some cases. The proposed information fusion approaches operate at signal and decision level. For the signal level combination, a simple trilateration algorithm is implemented on the range data from the 3 UWB radar sensors, achieving good classification results with the proposed Bi-LSTM (Bidirectional LSTM neural network) as classifier, without exploiting conventional micro-Doppler information. For the decision level fusion, the classification results of individual radars using the Bi-LSTM network are combined with a robust Naive Bayes Combiner (NBC), and this showed subsequent improvement compared to the single radar case thanks to multi-perspective views of the subjects. Compared to conventional SVM and Random Forest classifiers, the proposed approach yields +20% and +17% improvement in the classification accuracy of individual gaits for the range-only trilateration method and NBC decision fusion method, respectively. When classifying sequential gaits, the overall accuracy for the two proposed methods reaches 93% and 90%, with validation via a ’leaving one participant out’ approach to test the robustness with subjects unknown to the network

Sequential Human Gait Classification with Distributed Radar Sensor Fusion

This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those containing multiple gait styles performed one after the other, with natural transitions in between, including fall events developing from walking gait in some cases. The proposed information fusion approaches operate at signal and decision level. For the signal level combination, a simple trilateration algorithm is implemented on the range data from the 3 UWB radar sensors, achieving good classification results with the proposed Bi-LSTM (Bidirectional LSTM neural network) as classifier, without exploiting conventional micro-Doppler information. For the decision level fusion, the classification results of individual radars using the Bi-LSTM network are combined with a robust Naive Bayes Combiner (NBC), and this showed subsequent improvement compared to the single radar case thanks to multi-perspective views of the subjects. Compared to conventional SVM and Random Forest classifiers, the proposed approach yields +20% and +17% improvement in the classification accuracy of individual gaits for the range-only trilateration method and NBC decision fusion method, respectively. When classifying sequential gaits, the overall accuracy for the two proposed methods reaches 93% and 90%, with validation via a ’leaving one participant out’ approach to test the robustness with subjects unknown to the network.Microwave Sensing, Signals & System