Mikel Miller

Post-correlation semi-coherent integration for high-dynamic and weak GPS signal acquisition

Acquisition and ultimate tracking of a weak GPS signal faces several technical challenges, notably, possible data bit sign reversal every 20 ms and tolerable frequency error inversely proportional to the integration interval. Brute force search over all possible combinations of the unknown values is computationally prohibitive. Assisted GPS relying on external infrastructure for timing, data bit, and frequency error information is costly. Coherent techniques such as the block accumulating coherent integration over extended interval (BACIX) have recently been proposed to increase coherent integration. Although efficient, such coherent methods may still be too expensive except for high-end receivers and may not maintain the SNR performance when there are large frequency changes over the extended integration interval. In this paper, we set forth a novel method that utilizes the semi-coherent scheme for post-correlation integration. It is named as block accumulating semi-coherent integration of correlations (BASIC) and can be viewed as an extension of the BACIX algorithm. Although less sensitive than coherent integration, semi-coherent integration based on inter-block conjugate products is computationally more efficient. In addition, it can handle large frequency changes. The BASIC algorithm is first formulated in the paper. Computer simulation results are then presented to illustrate the operation and performance of the BASIC algorithm for joint estimation of the initial frequency, chirping rate (rate of change in frequency), bit sync, and bit sign pattern.

Symmetric Phase-Only Matched Filter (SPOMF) for Frequency-Domain Software GNSS Receivers

Abstract : As a powerful tool, the symmetric phase-only matched filter (SPOMF) has been shown to yield superior performance over the conventional correlator and is widely used in image registration and recognition. In this paper, we investigate the use of this SPOMF for processing GNSS signals. This extension is compatible with our frequency-domain software GNSS receiver architecture in which both the incoming signal and replica spectra are available for the SPOMF implementation versus the conventional correlator. The use of phase-only information is equivalent to equalizing the magnitude spectrum in contrast to the original spectrum that tapers off according to a sinc-function. This tends to accentuate the high frequency components corresponding to edges or transitions in the signals. As such, the SPOMF produces a much sharper peak (ideally a Dirac delta function) that is more accurate in timing and less sensitive to multipath. In addition, the same operation is applicable to both a binary phase shift keying (BPSK) modulated signal such as the GPS C/A-code and P-code and a binary offset carrier (BOC) modulation such as the GPS M-code and Galileo codes. More importantly, it only has a single matching peak regardless of which modulation code is being used. In this paper, the SPOMF is introduced within the framework of a generalized frequency-domain correlator (GFDC) for GNSS signals. The salient features of SPOMF as well as its application to BPSK and BOC signals are illustrated with simulation examples.

Egomotion estimation with optic flow and air velocity sensors

We develop a method that allows a flyer to estimate its own motion (egomotion), the wind velocity, ground slope, and flight height using only inputs from onboard optic flow and air velocity sensors. Our artificial algorithm demonstrates how it could be possible for flying insects to determine their absolute egomotion using their available sensors, namely their eyes and wind sensitive hairs and antennae. Although many behaviors can be performed by only knowing the direction of travel, behavioral experiments indicate that odor tracking insects are able to estimate the wind direction and control their absolute egomotion (i.e., groundspeed). The egomotion estimation method that we have developed, which we call the opto-aeronautic algorithm, is tested in a variety of wind and ground slope conditions using a video recorded flight of a moth tracking a pheromone plume. Over all test cases that we examined, the algorithm achieved a mean absolute error in height of 7% or less. Furthermore, our algorithm is suitable for the navigation of aerial vehicles in environments where signals from the Global Positioning System are unavailable.

Novel electrocardiogram segmentation algorithm using a multiple model adaptive estimator

Presents an electrocardiogram (ECG) processing algorithm design based on a multiple model adaptive estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. The MMAE robustly locates these key temporal landmarks in the ECG signal, extracting crucial patient treatment information from the often distorted or unstable ECG waveform. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal.

Navigation in Difficult Environments: Multi-Sensor Fusion Techniques

This chapter focuses on multi-sensor fusion for navigation in difficult environments where none of the existing navigation technologies can satisfy requirements for accurate and reliable navigation if used in a stand-alone mode. A generic multi-sensor fusion approach is presented. This approach builds the navigation mechanization around a self-contained inertial navigator, which is used as a core sensor. Other sensors generally derive navigation-related measurements from external signals, such as Global Navigation Satellite System (GNSS) signals and signals of opportunity (SoOP), or external observations, for example, features extracted from images of laser scanners and video cameras. Depending on a specific navigation mission, these measurements may or may not be available. Therefore, externally-dependent sources of navigation information (including GNSS, SoOP, laser scanners, video cameras, pseudolites, Doppler radars, etc.) are treated as secondary sensors. When available, measurements of a secondary sensor or sensors are utilized to reduce drift in inertial navigation outputs. Inertial data are applied to improve the robustness of secondary sensors’ signal processing. Applications of the multi-sensor fusion approach are illustrated in detail for two case studies: (1) integration of Global Positioning System (GPS), laser scanner, and inertial navigation; and, (2) fusion of laser scanner, video camera, and inertial measurements. Experimental and simulation results are presented to illustrate performance of multi-sensor fusion algorithms.

Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the Worlds population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.