Nandakumaran Nadarajah

BeiDou Inter-Satellite-Type Bias Evaluation and Calibration for Mixed Receiver Attitude Determination

The Chinese BeiDou system (BDS), having different types of satellites, is an important addition to the ever growing system of Global Navigation Satellite Systems (GNSS). It consists of Geostationary Earth Orbit (GEO) satellites, Inclined Geosynchronous Satellite Orbit (IGSO) satellites and Medium Earth Orbit (MEO) satellites. This paper investigates the receiver-dependent bias between these satellite types, for which we coined the name “inter-satellite-type bias” (ISTB), and its impact on mixed receiver attitude determination. Assuming different receiver types may have different delays/biases for different satellite types, we model the differential ISTBs among three BeiDou satellite types and investigate their existence and their impact on mixed receiver attitude determination. Our analyses using the real data sets from Curtins GNSS array consisting of different types of BeiDou enabled receivers and series of zero-baseline experiments with BeiDou-enabled receivers reveal the existence of non-zero ISTBs between different BeiDou satellite types. We then analyse the impact of these biases on BeiDou-only attitude determination using the constrained (C-)LAMBDA method, which exploits the knowledge of baseline length. Results demonstrate that these biases could seriously affect the integer ambiguity resolution for attitude determination using mixed receiver types and that a priori correction of these biases will dramatically improve the success rate.

Assessing the IRNSS L5-signal in combination with GPS, Galileo, and QZSS L5/E5a-signals for positioning and navigation

Abstract The Indian Regional Navigation Satellite System (IRNSS), which is being developed for positioning services in and around India, is the latest addition to the global family of satellite-based navigation systems. As IRNSS only shares the L5-frequency with GPS, the European Galileo, and the Japanese Quasi-Zenith Satellite System (QZSS), it has at least at present a limited interoperability with the existing systems. Noting that the L5-frequency capability is under development even for GPS, this contribution assesses the interoperability of the IRNSS L5-signal with the GPS, Galileo, and QZSS L5/E5a-signals for positioning and navigation using real data collected in Perth, Australia. First, the noise characteristic of the IRNSS L5-signal and its comparison with that of the GPS, Galileo, and QZSS L1/E1- and L5/E5a-signals is presented. Then, the L5-observables of combined systems (formed from IRNSS, GPS, Galileo, and QZSS) are assessed for real-time kinematic positioning using the standard LAMBDA method and for instantaneous attitude determination using the constrained LAMBDA method. The results show that the IRNSS L5-signal has comparable noise characteristics as that of the other L5/E5a-signals. For single-frequency carrier phase-based positioning and navigation, the results show better ambiguity resolution performance of L5/E5a-only processing than that of L1/E1-only processing.

The mixed-receiver BeiDou inter-satellite-type bias and its impact on RTK positioning

The inter-satellite-type bias (ISTB) is a receiver-dependent hardware delay/bias between different satellite types. Our recent research revealed the existence of nonzero mixed-receiver phase ISTBs for the Chinese BeiDou system. Triggered by this finding, global navigation satellite system receiver manufactures, who are in the early stage of BeiDou-enabled receiver developments, are working toward a mutually consistent measurement extraction procedure. We analyze the long-term stability and current status of the mixed-receiver ISTBs, as well as study their impact on BeiDou stand-alone real-time kinematic (RTK) positioning. Our results confirm that a recent update in one of the receiver types has aligned it with one of the other receiver types. However, since not all receiver types are aligned yet, nonzero mixed-receiver ISTBs are shown to be still present. Analyses of BeiDou stand-alone RTK positioning using mixed-receiver types demonstrate that ISTBs could seriously affect the integer ambiguity resolution performance and that a priori correction for these biases will dramatically improve the success rate. Our analyses using real data from three different receiver types also demonstrate the long-term stability of the ISTBs, thus showing that such a priori calibration is indeed possible.

GPS, Galileo, QZSS and IRNSS differential ISBs: estimation and application

Knowledge of inter-system biases (ISBs) is essential to combine observations of multiple global and regional navigation satellite systems (GNSS/RNSS) in an optimal way. Earlier studies based on GPS, Galileo, BDS and QZSS have demonstrated that the performance of multi-GNSS real-time kinematic positioning is improved when the differential ISBs (DISBs) corresponding to signals of different constellations but transmitted at identical frequencies can be calibrated, such that only one common pivot satellite is sufficient for inter-system ambiguity resolution at that particular frequency. Recently, many new GNSS satellites have been launched. At the beginning of 2016, there were 12 Galileo IOV/FOC satellites and 12 GPS Block IIF satellites in orbit, while the Indian Regional Navigation Satellite System (IRNSS) had five satellites launched of which four are operational. More launches are scheduled for the coming years. As a continuation of the earlier studies, we analyze the magnitude and stability of the DISBs corresponding to these new satellites. For IRNSS this article presents for the first time DISBs with respect to the L5/E5a signals of GPS, Galileo and QZSS for a mixed-receiver baseline. It is furthermore demonstrated that single-frequency (L5/E5a) ambiguity resolution is tremendously improved when the multi-GNSS observations are all differenced with respect to a common pivot satellite, compared to classical differencing for which a pivot satellite is selected for each constellation.

Multitarget Tracking using Probability Hypothesis Density Smoothing

In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. It is observed that the particle implementation of the PHD filter is dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper a PHD smoothing algorithm is proposed to improve the capability of PHD-based tracking system. It involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Smoothing, which produces delayed estimates, results in better estimates for target states and a better estimate for the number of targets. Multiple model PHD (MMPHD) smoothing, which is an extension of the proposed technique to maneuvering targets, is also provided. Simulations are performed with the proposed method on a multitarget scenario. Simulation results confirm improved performance of the proposed algorithm.

Instantaneous GPS–Galileo Attitude Determination: Single-Frequency Performance in Satellite-Deprived Environments

New and modernized global navigation satellite systems (GNSSs) are paving the way for an increasing number of applications in positioning, navigation, and timing (PNT). A combined GNSS constellation will significantly increase the number of visible satellites and, thus, will improve the geometry of observed satellites, enabling improvements in navigation solution availability, reliability, and accuracy. In this paper, a global positioning system (GPS) +Galileo robustness analysis is carried out for instantaneous single-frequency GNSS attitude determination. Precise attitude determination using multiple GNSS antennas mounted on a platform relies on successful resolution of the integer carrier-phase ambiguities. The multivariate-constrained least squares ambiguity decorrelation adjustment (MC-LAMBDA) method has been developed to resolve the integer ambiguities of the nonlinearly constrained GNSS attitude model that incorporates the known antenna geometry. In this paper, the method is used to analyze the attitude determination performance of a combined GPS +Galileo system. Special attention is thereby given to the GPS and Galileo intersystem biases (ISBs). The attitude determination performance is evaluated using GPS/Galileo data sets from a hardware-in-the-loop experiment and two real-data campaigns. In the hardware-in-the-loop experiment, a full GPS/Galileo constellation is simulated, and performance analyses are carried out under various satellite-deprived environments, such as urban canyons, open pits, and other satellite outages. In the first real-data experiment, single-frequency GPS data, combined with the data of Galileo in-orbit validation element (GIOVE) satellites GIOVE-A/GIOVE-B (the two experimental Galileo satellites), are used to analyze the two constellation attitude solutions. In the second real-data experiment, we present the results based on single-frequency data from one of the Galileo IOV satellites, combined with the data of GIOVE-A and GPS. We demonstrate and quantify the improved availability, reliability, and accuracy of attitude determination using the combined constellation.

IMM Forward Filtering and Backward Smoothing for Maneuvering Target Tracking

The interacting multiple model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model (MM) smoothing in the literature. A new smoothing method is presented here which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode-conditioned smoother uses standard Kalman smoothing recursion. The resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. Results of the new method are compared with existing methods, namely, the augmented state IMM filter and the generalized pseudo-Bayesian estimator of order 2 smoothing. Specifically, the proposed IMM smoother operates just like the IMM estimator, which approximates N2 state transitions using N filters, where N is the number of motion models. In contrast, previous approaches require N2 smoothers or an augmented state.

Multitarget Passive Coherent Location with Transmitter-Origin and Target-Altitude Uncertainties

Passive coherent location (PCL) systems, which use existing commercial signals (e.g., FM broadcast, digital TV) as the illuminators of opportunity, is an emerging technology in air defence systems. PCL systems have many advantages such as low cost, covert operation, and low vulnerability to electronic countermeasures, over conventional radar systems. However, the limitations of PCL include lack of control over illuminators, limited observability, and poor detection due to low signal-to-noise ratio (SNR). This leads to high clutter with low probability of detection of target. Also, it is possible to transmit through multiple transmitters the same signal/frequency inside the coverage region of the receiver. Even though using multiple transmitters will facilitate better estimates of the target states due to spatial diversity, one cannot use these measurements without resolving transmitter- and measurement-origin uncertainties. Another limitation is that the elevation measurement, which is required to estimate the target altitude, is not available in most currently available PCL system receivers. In this paper, multiple target tracking algorithms for PCL systems are derived to handle low probability of detection and high nonlinearity in the measurement model due to high measurement error. Also, tracking algorithms are proposed to track multiple targets by resolving transmitter-origin uncertainty. Finally, algorithms are proposed for fusing multiple PCL system estimates by incorporating the altitude estimate uncertainty in order to improve the tracking accuracy and to increase the area coverage. The major contributions of this paper are the new algorithms for tracking using PCL systems with transmitter-origin and target-altitude uncertainties. The feasibility of using transmitters of opportunity for tracking airborne targets is shown on simulated and real data sets.

Assessing drivers’ visual-motor coordination using eye tracking, GNSS and GIS: a spatial turn in driving psychology

Abstract Vehicle-driving in real traffic can be considered as a human-machine system involving not only the attribute of the vehicle movement but also the human visual perception, cognition and motion of the driver. The study of driving behaviours, therefore, would integrate information related to driver psychology, vehicle dynamics and road information in order to tackle research questions concerning driving safety. This paper describes a conceptual framework and an integrated GIS data model of a visual-motor coordination model (VMCM) to investigate drivers’ driving behaviour via the combination of vision tracking and vehicle positioning. The eye tracker recorded eye fixations and duration on video images to exhibit the driver’s visual search pattern and the traffic scenes. Real-time kinematic (RTK) post-processing of multi-GNSS (global navigation satellite system) tracking generated the vehicle movement trajectory at centimeter-level accuracy, which encompasses precise lateral positioning and speed control parameters of driving behaviours. The eye fixation data were then geocoded and linked to the vehicle movement trajectory to represent the VMCM on the GIS platform. An implementation prototype of the framework and the VMCM for a study of older drivers is presented in this paper. The spatial-temporal visualisation and statistical analysis based on the VMCM data-set allow for a greater insight into the inherent variability of older drivers’ visual search and motor behaviours. The research framework has demonstrated a discriminant and ecologically valid approach in driving behaviour assessment, which can also be used in studies for other cohort populations with modified driving scenarios or experiment designs.

GLONASS CDMA L3 ambiguity resolution and positioning

A first assessment of GLONASS CDMA L3 ambiguity resolution and positioning performance is provided. Our analyses are based on GLONASS L3 data from the satellite pair SVNs 755-801, received by two JAVAD receivers at Curtin University, Perth, Australia. In our analyses, four different versions of the two-satellite model are applied: the geometry-free model, the geometry-based model , the height-constrained geometry-based model, and the geometry-fixed model. We study the noise characteristics (carrier-to-noise density, measurement precision), the integer ambiguity resolution performance (success rates and distribution of the ambiguity residuals), and the positioning performance (ambiguity float and ambiguity fixed). The results show that our empirical outcomes are consistent with their formal counterparts and that the GLONASS data have a lower noise level than that of GPS, particularly in case of the code data. This difference is not only seen in the noise levels but also in their onward propagation to the ambiguity time series and ambiguity residuals distribution.