Chot Hun Lim

A new unmanned aerial vehicle synthetic aperture radar for environmental monitoring

A new Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) has been developed at Multimedia University, in collaboration with Agency of Remote Sensing Malaysia. The SAR operates at C-band, single V V -polarization, with 5m £ 5m spatial resolution. Its unique features include compact in size, light weight, low power and capable of performing real-time imaging. A series of fleld measurements and ∞ight tests has been conducted and good quality SAR images have been obtained. The system will be used for monitoring and management of earth resources such as paddy flelds, oil palm plantation and soil surface. This paper reports the system design and development, as well as some preliminary results of the UAVSAR.

Practical approach in estimating inertial navigation unit's errors

Inertial navigation unit (INU), which is commonly composed of three orthogonally aligned accelerometers and gyros, is well known for its short term measurement accuracy in position, velocity and attitude. However, such measurement accuracy degrades with time due to various types of errors. In this paper, a practical approach is proposed to estimate both the deterministic and random errors of an INU. The deterministic errors, which include bias and scaling errors, can be estimated through a simple experimental setup; while the random noise is modeled using Allan Variance (AV) analysis method. The empirical values of the errors are then fed into the INUs system model for error correction using Kalman filtering. Finally, the calibrated INU shows promising results in preserving long term accuracy of the motion sensor.

A new data acquisition and processing system for UAVSAR

Synthetic Aperture Radar (SAR) is widely known as a high resolution imaging system in microwave remote sensing. Large number of frequency-modulated received echoes must be acquired in real time. A typical SAR data acquisition unit (DAQ) involves high speed analog-to-digital conversion, front-end pre-processing, and data recording. Subsequent processes consist of computationally intensive digital signal processing for image formation. In this paper, an efficient data acquisition and SAR processing method is proposed. It is based on a modified discrete Fourier Transform algorithm, which requires lesser systems computational load as compared to conventional Fast Fourier Transform. The proposed system has been implemented on an UAVSAR (Unmanned Aerial Vehicle SAR) and the flight tests have shown promising results for real-time imaging.

Stochastic Error Modeling of MEMS Inertial Sensor with Implementation to GPS-Aided INU System for UAV Motion Sensing

The resided stochastic error in Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (INU) had caused the instrument not being able to operate as a standalone device for navigation applications. The conventional Global Positioning System (GPS)-aided strapdown INU system is commonly adopted to tackle such issue. Note that the estimation accuracy of such system depends on how precise the modeling of the stochastic error. In this paper, a comprehensive stochastic error modeling through three distinct approaches, namely the Gauss-Markov (GM) modeling, the Allan Variance (AV) analysis, and the Autoregressive (AR) modeling, are presented. The analysis shows that AR model achieved better modeling accuracy than the other two approaches. Next, the modeled stochastic errors were implemented on a GPS-aided strapdown INU system for UAV airplanes motion sensing, and the results shown that AR model achieved lower RMSE than the GM model, indicating that AR model is more suitable than GM model in representing the stochastic error model of MEMS strapdown INU.

A MINIATURE REAL-TIME RE-CONFIGURABLE RADAR WAVEFORM SYNTHESIZER FOR UAV BASED RADAR

Radar waveform synthesizer is a key component in radar system as it determines the best achievable resolution. In this paper, a miniature and low cost radar waveform synthesizer is proposed. The synthesizer is targeted for Unmanned Aerial Vehicle (UAV) based radar system applications that require miniaturized equipment due to limited space in aircrafts fuselage. The waveform synthesizer has been developed using Altera DE3 development board (Stratix III FPGA) and a custom made dual-channel 420MSPS HS-DAC board. The proposed system is capable of generating various types of radar waveforms: a) Linear Frequency Modulated (LFM) or chirp pulse, b) Frequency Modulated Continuous Wave (FMCW), and c) Calibration Tone (Cal-Tone), for use in difierent types of radar applications. The distinguishing feature of the proposed synthesizer is its capability in re-conflguring the signal properties in real-time. The performance of the synthesizer has been benchmarked with commercially available radar waveform synthesizer and comparable performance has been observed.

A MEMS based, low cost GPS-aided INS for UAV motion sensing

This paper presents a new design and development of a low cost GPS (Global Positioning System)-aided INS (Inertial Navigation System) using Micro-ElectroMechanical-System (MEMS) inertial sensor. A typical MEMS type inertial sensor consists of three orthogonally aligned accelerometers and three orthogonally aligned gyroscopes confined in a very small chip. In this paper, the intensive preprocessing and modeling techniques of MEMS inertial sensors primitive, noisy motion data are outlined. These techniques transform the erroneous motion data into usable motion indicators illustrated in three-dimensional position, three-dimensional velocity, and three-dimensional orientation. GPS serves as an aiding device to tune the MEMS inertial sensors measurements through Kalman filter, while extra sensory feedbacks from magnetometers are employed to improve the system performance. Experiment was conducted to evaluate the performance of the GPS-aided INS by installing it into an Unmanned Aerial Vehicle (UAV) for motion sensing. Lastly, the experiment results are evaluated and verified using the motion data generated from a commercial navigation system.

A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation

This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robots indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robots positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.

New GPS-aided SINU System Modeling using an Autoregressive Model

Stochastic error in the Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (SINU) is the primary issue causing sensors to be unable to operate as a standalone device. Conventional implementation of MEMS SINU fuses measurement with a global positioning system (GPS) through a Kalman filter in order to achieve long-term accuracy. Such integration is known as a GPS-aided SINU system, and its estimation accuracy relies on how precise the stochastic error prediction is in Kalman filtering operation. In this paper, a comprehensive study on stochastic error modeling and analysis through a Gauss-Markov (GM) model and autoregressive (AR) model are presented. A wavelet denoising technique is introduced prior to error modeling to remove the MEMS SINUs high frequency noise. Without a wavelet denoising technique, neither the GM model nor AR model can be utilized to represent the stochastic error of SINU. Next, details of the Kalman filter implementation to accommodate the AR model are presented. The modeling outcomes are implemented on an unmanned aerial vehicle (UAV) for on-board motion sensing. The experimental results show that AR model implementation, compared to a conventional GM model, significantly reduced the estimated errors while preserving the position, velocity and orientation measurements.

IMPLEMENTATION OF ANFIS FOR GPS-AIDED INS UAV MOTION SENSING AT SHORT TERM GPS OUTAGE

The recent improvement in Micro-Electro-Mechanical System (MEMS) technology has enabled the evolvement of Inertial Navigation Unit (INU) to be built on top of a low cost, small size Integrated C ircuit (IC) chip. Due to the nature of the MEMS INU, its o utputs are normally corrupted by the resided stocha stic noise. A common practice to regulate its measurements into usable motion data is by fusing the Global Positioning System (GPS) measurement data with the MEMS INU measurement data through Kalman filter for position, velocity and orientation estimations. Such integrated system is known as GPS-aided Inert ial Navigation System (INS). Note that the robustness o f the GPS-aided INS relies heavily on the availabil ity of the GPS signals. In the event of no GPS signals, the overall system will solely depend on the INU t o predict the position, velocity and orientation. The prediction results will eventually drift from its true value due to the INU’s resided stochastic noise. In this study, a remedy system using Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to improve the performance of the GPS-aided INS during GPS outage condition. UAV motion sensing experiment was carried out and GPS outage conditions were imposed at several locations during the UAV navigat ion. The motion prediction dataduring GPS outages, with and without ANFIS implementation, were compared and the results clearly show that the GPS-aided INS with ANFIS implementation achieved better performance than the GPS-aided INS without ANFIS.

A VISION-BASED TRAVELLED DISTANCE ESTIMATION ALGORITHM IN AN INDOOR ENVIRONMENT USING A MOBILE ROBOT

Autonomous navigation for a mobile robot still remains as a challenging area to be explored. In an indoor environment, while GPS is unavailable and wheel encoder suffer from error accumulation due to wheel slips, vision-based travelled distance estimation can be considered as an alternative approach for more accurate measurements. This study presents a new algorithm to estimate travelled distance of the mobile robot in an indoor environment. Using a downward looking camera, features points are detected from the floor texture and tracked with the Lucas-Kanade optical flow technique. The measurement accuracy of this algorithm will be put through several experiments on real scenarios which involve comparing the proposed techniques, running the algorithm on different type of indoor surface at different speed and different trajectories.