Chirp spread spectrum toward the Nyquist signaling rate - orthogonality condition and applications

With the proliferation of Internet-of-Things (IoT), the chirp spread spectrum (CSS) technique is re-emerging for communications. Although CSS can offer high processing gain, its poor spectral efficiency and the lack of orthogonality among different chirps tend to limit its potential. In this paper, we derive the condition to orthogonally multiplex an arbitrary number of linear chirps. For the first time in the literature, we show that the maximum modulation rate of the linear continuous-time chirps satisfying the orthogonality condition can approach the Nyquist signaling rate, the same as single-carrier waveforms with Nyquist signaling or orthogonal frequency-division multiplexing signals. The performance of the proposed orthogonal CSS is analyzed in comparison to the emerging LoRa systems for IoT applications with power constraint, and its capability for high-speed communications is also demonstrated in the sense of Nyquist signaling

Target Localization Using Approximate Maximum Likelihood for MIMO Radar Systems

This thesis deals with target localization using multiple-input multiple-output (MIMO) radars. In the field of communications, navigation, radar, and sensing networks, one of the common and most sophisticated problems is target localization. We develop a target localization scheme in distributed MIMO radar systems using bistatic range measurements. The localization approach consists of two phases. First, measurements are divided into multiple groups based on the various transmitter and receiver elements. For each group, an approximate maximum likelihood (AML) estimator is proposed to estimate the location of a target. Then, the estimation results from these different groups are combined to form the final estimate. The performance of the proposed algorithm is validated by simulation and is shown to reach the Cram\'{e}r-Rao lower bound (CRLB) in a range of measurement noise levels. The main advantage of the proposed algorithm is that it achieves a higher accuracy than existing schemes for locating a target position in high-noise conditions

Movement Pattern Detection Through IMU and Barometer

Movement pattern detection can be applied in a variety of applications such as assisting independent living of seniors at home, behaviour understanding in surveillance systems, sports analytics, and robotics. This project develops a scheme that fuses information from different sensors to detect movement patterns. This report contains three main parts: information collection and processing, pattern detection using the information collected, and algorithm implementation and results. The information needed for movement pattern detection comes from an inertial measurement unit (IMU) and a barometer. The information from the accelerometer and the gyroscope is first combined by using a complementary filter. The measurements in the body coordinates of the IMU are then transformed into data in the earth coordinates via quaternions. We then develop a scheme that exploits the advantages of the vupport vector machine and the k-nearest neighbor algorithm for motion detection. These schemes are finally implemented to detect four different movement patterns: walking, running, standing up and falling down, which are classified into static and dynamic motions. For dynamic motion, the difference of tilt angle and height could be used to distinguish the standing-up and falling-down patterns; for static motion, the difference of velocity in the horizontal plane could be used to distinguish the walking and running patterns

Hybrid AOA and TDOA Solution for Transmitter Positioning

Accurate positioning has become an active research area in recent years. It has a wide range of applications in many fields such as navigation, asset tracking, health care, proximity marketing/location-based advertising, and sport analytics. Transmitter positioning via radio frequency (RF) signals is the most widely encountered scenario, and it uses a two-step process: First, parameters that depend on the location of the transmitter are extracted from the received signal. Second, the transmitter’s location is estimated by using these parameters. Many parameters can be used; for instance, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS). Localization can use one or multiple of such parameters. In this thesis, a hybrid AOA and TDOA method is studied. Specifically, an array of N collinear receiving antennas are employed to estimate the transmitter position. In order to use AOA, existing assumes that the transmitter is far away from the receiving antennas and that the spacing between the receiving antennas is very small (typically a fraction of one wavelength). This ensures that the directions of the incident waves to all receivers are parallel, so that there is a single AOA for all receivers. Such condition cannot be maintained for some scenarios (e.g., when wavelength is very large). Also, in order to use valid TDOAs, the receiving antennas cannot be placed very close to one another, which will result a unique AOA for each of the receiving antennas. This research develop solutions for the cases where the above constraints cannot be maintained. A maximum likelihood (ML) estimator is developed to obtain the AOA of each receiving antenna assuming there is no limitation on the antenna spacing; it can be sufficiently large orsmall . A cross correlation algorithm is used to determine the TDOA between the received signals. Finally, an algorithm that jointly processes the AOAs and the TDOAs to estimate the position of the transmitter is developed

M-ary Chirp Modulation for Data Transmission

M-ary chirp modulations, both discontinuous- and continuous-phase, for M-ary data transmission are proposed and examined for their error rate performances in additive, white, Gaussian noise (AWGN) channel. These chirp modulated signals are described and illustrated as a function of time and modulation parameters. M-ary chirp modula­ tion with discontinuous phase is first proposed and then the M-ary Continuous Phase Chirp Modulation (MCPCM) is considered. General descriptions of these modula­ tion systems are given and properties of signals representing these modulations are given and illustrated. Optimum algorithms for detection of these signals in AWGN are derived and structures of optimum receivers are identified. Using the minimum Euclidean distance criterion in signal-space; upper bounds on Signal-to-Noise Ratio (SNR) gain relative to Multiple Phase Shift Keying (MPSK) are established for 2-. *4-, and 8-ary MCPCM systems. It is observed that the maximum likelihood coherent and non-coherent receivers for MCPCM are non-linear and require multiple-symbol observations. Since symbol error probability performance analyses of these receivers are too complex to perform, union upper bounds on their performances are derived and illustrated as a function of SNR, number of observation symbols, and modulation parameters for MCPCM. Optimum 2-, 4-, and 8-ary modulation schemes that mini­ mize union upper bound on symbol error rates have been determined and illustrated. Our results show that 2-, 4-, and 8-ary optimum coherent MCPCM systems, with 5-symbol observation length, offer 1.6 dB, 3.6 dB, and 8 dB improvements relative to 2-ary, 4-ary, and 8-ary PSK systems, respectively. Also, it is shown that opti­ mum 2-ary and 4-ary non-coherent MCPCM systems can outperform 2-ary and 4-ary coherent PSK systems, respectively

Exploring IMU Attitude and Position Estimation for Improved Location in Indoor Environments

Wearable sensors with an inertial measurement unit (IMU) are popular for indoor positioning and activity pattern detection. The IMUs can be connected to a wireless transmission module, allowing users to monitor and process motion-related parameters remotely. Because of the complexity and uncertainty of signals in indoor environments, a radio frequency (RF) positioning system alone is often insufficient to provide the position accuracy and stability required for many applications, for example, position-guided indoor mobile robots. Our idea is to fuse two or more sources of data to generate highly accurate positioning information. Specifically, we have developed an IMU-aided RF positioning system, aiming to improve the accuracy of the system in indoor environments for mobile robotics. This approach combines the measurements of the accelerometers, gyroscopes, and magnetometer from an IMU via a complementary filter. The work includes a development of a calibration algorithm, which reduces the IMU drift and error. With the calibrated data, the trapezoidal integration method can now better use the accelerometer data to estimate the velocity and displacement of the mobile robot. In order to transform the data in the coordinate system of the IMU mounted on the mobile robot body to the ground positioning coordinate that RF positioning uses, we implement a quaternion rotation algorithm. This enables the fusion of the IMU and RF positioning estimates to accurately determine the moving trajectory of the mobile robot and guide its moving directions

Indoor Localization Using Direction of Arrival Approach

In this paper, a Direction of Arrival (DOA) based system is proposed. This method searches the direction relative to the array to find where the signal source is located. The proposed system can achieve sub-meter level accuracy with a near real-time update rate. Also, we introduced several refinement methods including a compact tracking system that is compatible with small items, a DOA accuracy prediction function, a method based on linear prediction to expand antenna array to create a virtual antenna matrix, and a novel method for multipath effect cancellation. Overall, the proposed system achieved sub-meter level accuracy, and the functionality of the refinement methods has been approved in the simulation