Dongfang Zheng

Indoor localization based on LEDs

LEDs have many advantages compared with the conventional lamps. Therefore LEDs are expected to be the next generation of lamps that will be widely installed to replace conventional lamps. Due to their rapid response times, LEDs can be modulated at very high-speeds, which allows the possibility of simultaneously providing communication while illuminating. Considering these facts, a system can be designed to receive (using a single photodiode or a camera) and analyze LED signals. Furthermore, such a system should be able to facilitate position estimation tasks either for people or vehicles, especially in indoor navigation tasks where the LED positions are known. Herein two methods — single photo-detector and array photo-detector (camera) — are introduced. The single photo-detector approach is used to detect the existence and the specific identity of an LED. The array photo-detector approach measures the angle-of-arrival of the LED signal. Navigation methods based on this measured information is considered.

Underwater Inertial Navigation With Long Baseline Transceivers: A Near-Real-Time Approach

Due to the short range over which electromagnetic fields decay in sea water, inertial navigation systems for underwater vehicles are often aided by acoustic time-of-flight positioning scheme. One widely implemented long-baseline (LBL) approach uses a ping-response protocol resulting in asynchronous measurements that depend on the state of the vehicle at two time instants. Due to these issues, the standard assumptions necessary for Extended Kalman Filter (EKF) solutions are not satisfied. This paper proposes a Near-Real-Time (NRT) Bayesian smoothing framework for the LBL aided INS application. Within this NRT framework, the navigation process is divided into the ping-response cycles of LBL transceiving. Before the end of a LBL cycle, a traditional realtime EKF is implemented using the IMU and standard aiding measurements. At the end of each LBL cycle, an optimal Bayesian trajectory estimator executes. This Maximum-A-Posteriori (MAP) estimation includes all the measurement information collected during the current LBL cycle. Furthermore, right after this smoothing process, the current EKF estimate is corrected by the corresponding MAP estimate. This article presents the theoretical solution, discusses the implementation, and presents simulation results to illustrate the accuracy and reliability of this Near-Real-Time approach.

Underwater vehicle near real time state estimation

Acoustic time-of-flight positioning schemes are widely implemented for aiding underwater inertial navigation systems. The ping-response protocol and asynchronous nature of the returns of long-baseline (LBL) systems do not satisfy the standard assumptions necessary for Extended Kalman Filter (EKF) solutions. This paper presents a Near-Real-Time (NRT) framework for LBL aided inertial navigation. The solution proposed herein implements an optimal Bayesian state estimator over the time-frame of each LBL transponding cycle. This Maximum-A-Posteriori (MAP) solution considers all navigation sensor information collected during each LBL cycle and is computed at the conclusion of the LBL cycle. The solution between LBL cycles is computed by standard extended Kalman filter (EKF) methods for all other measurements (e.g., Doppler velocity log (DVL), pressure or compass) that satisfy the EKF assumptions. The article includes simulation results to illustrate the performance of this Near-Real-Time approach.

Navigation using linear photo detector arrays

The growth of light emitting diodes (LEDs) for illumination enables new approaches both for communication and navigation. One of the challenges when LEDs are used in the navigation systems is how to extract and capitalize effectively on the encoded LED ID. A single photo detector (PD) provides the simplest hardware approach and could communicate with LEDs at a very high data rate, but only offers the most basic level of positioning. Cameras provides much more informative position measurements; however, there are challenges to achieving high rate LED-to-camera data communications due to the current hardware architectures of cameras. Alternatively, linear PD arrays allow high sample rates, high accuracy and low cost. This article investigates the issues (observability, extrinsic parameter calibration, and vehicle state initialization) related to implementation of a positioning and communications system built on a linear optical sensor array.

Near-real-time GPS integer ambiguity resolution

Real-time decimeter accuracy GPS positioning can be achieved using carrier phase measurements. This requires fast and reliable on-the-fly integer ambiguity resolution. However, in GPS challenged areas (e.g. Urban canyons, tunnels, thick canopy etc.) the GPS receiver may not be able to track a sufficient number of satellites to resolve the integer ambiguities within one epoch. In this paper, we would like to find the optimal solution by combining the measurements from several epochs. In this paper, we present the theoretical derivation for a fast and efficient method for GPS integer ambiguity resolution with multiple GPS epoch measurements. Simulation results show the effectiveness of the proposed approach.

Underwater inertial navigation with long base line transceivers: A Near-Real-Time approach

Inertial navigation systems for underwater vehicles are often aided by acoustic time-of-flight positioning schemes. One widely implemented long baseline (LBL) approach uses a ping-response protocol resulting in asynchronous measurements that depend on the state of the vehicle at two time instants. Such aiding measurements that depend on the state at multiple time instants do not fit the model format of the standard extended Kalman filter (EKF) framework. This paper proposes a near-real-time (NRT) Bayesian smoothing framework for the LBL-aided inertial navigation system application. Within this NRT framework, the time interval between LBL cycles is divided into two intervals defined by the ping-response cycles of LBL. The time instant when all the LBL responses are received will be referred to as the NRT point. Between the LBL ping and the NRT point, a traditional real-time EKF is implemented using the inertial measurement unit and all aiding measurements. After the NRT point, when all the LBL responses have been received, an optimal Bayesian trajectory estimator executes. This maximum a posteriori (MAP) estimation includes all the measurement information already collected up to the NRT point of the current LBL cycle. The MAP output is a smoothed trajectory estimate for the period of time between the LBL ping and the NRT point. At the conclusion of this smoothing process, the current EKF estimate is corrected to the optimal MAP solution and propagated to the current time, and then the EKF estimation process continues to the NRT point of the next LBL cycle. At all times, the approach maintains a real-time state estimate suitable for use by the control system. This paper presents the theoretical solution, discusses the implementation, and presents implementation results to illustrate the accuracy and reliability of this NRT approach.

High reliability integer ambiguity resolution of 6DOF RTK GPS/INS

This article presents a Contemplative Realtime (CRT) framework to resolve a vector of carrier phase integer ambiguities existing in Real-Time Kinematic GPS aided inertial navigation systems (RTK GPS/INS). Within this CRT framework, a Maximum-a-Posteriori (MAP) estimation method is derived to represent the RTK GPS/INS problem, and solved by Nonlinear Mixed Integer Least Square (NMILS) approach. This approach allows the utilization of multiple epochs of GPS data with INS defined motion constraints over a time interval. The NMILS approach over time intervals allows hypothesis testing on each interval to contemplate alternative fault detection hypotheses. The objectives are to enhance reliability and accuracy. Implementation results are included that demonstrate the performance of the proposed method achieving centimeter position accuracy.

INS aided GPS integer ambiguity resolution

Real-time high precision GPS positioning is based on carrier phase measurements, which requires fast and precise on-the-fly integer ambiguity resolution. In some navigation applications, external sensors are available that provide auxiliary measurements. For example, in GPS/INS navigation systems, the inertial sensors allow computation of prior position estimates when GPS signals become available. This information can be used to aid GPS integer ambiguity resolution, offering a higher probability of obtaining correct integers, especially in challenging GPS conditions (e.g., few satellites, high measurement noise). This paper describes a fast and efficient technique for integer ambiguity resolution when auxiliary information from INS is available. The theoretical derivation will be presented and simulation result will show the effectiveness of the proposed method.

Joint Measurement and Trajectory Recovery in Visible Light Communication

The growing preponderance of light emitting diodes (LEDs) for lighting has motivated research into their dual use for visible light communication (VLC) and navigation. VLC extracts a bit sequence from a series of photodetector scans. Among this data is an LED ID that ensures the reliable data association in navigation and data communication. Recovering the LED data and ID requires the accurate prediction of each LEDs projected position on the photodetector array to extract efficiently and reliably the LED ON-OFF status in each photodetector scan. Estimating the LED projected position is challenging because: 1) clutter and noise corrupt the measurements; 2) the LED status will be OFF in some scans; and 3) the predicted projection location sequence depends on the estimated rover state trajectory, which is uncertain and time varying. This paper presents a method to determine the q-most probable data and the LED position sequences simultaneously for a time window of data, using Bayesian multiple hypothesis tracking techniques by maximizing the posterior probabilities. This paper focuses on the VLC data and the LED position sequence extraction, which includes rover state estimation. Implementation of the multiple hypothesis tracking algorithm is illustrated by postprocessed experimental results.

An algorithm to recover an LED path

In Visible Light Communication (VLC) each LEDs on-off status can be recovered only after its projected position in each measurement frame is determined. Finding the LED projection is not straightforward since: 1) Clutter and noise corrupt the measurements; 2) When the LED is turned off, its position cannot be extracted by evaluating the intensity value of each pixel. Herein, a new method based on the Viterbi algorithm is developed. This method recovers the LED trajectory through a sequence of scans by maximizing its posterior probability. This method is developed under the assumption that the frame rate of the array sensor is high relative to the rover motion bandwidth. The Viterbi based algorithm is compared with alternative approaches and demonstrated in an application.