Improved decentralized multi-sensor navigation system for airborne applications

The integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) technologies is a very useful navigation option for high-accuracy positioning in many applications. However, its performance is still limited by GNSS satellite availability and satellite geometry. To address such limitations, a non-GNSS-based positioning technology known as “Locata” is used to augment a standard GNSS/INS system. The conventional methods for multi-sensor integration can be classified as being either in the form of centralized Kalman filtering (CKF), or decentralized Kalman filtering. However, these two filtering architectures are not always ideal for real-world applications. To satisfy both accuracy and reliability requirements, these three integration algorithms—CKF, federated Kalman filtering (FKF) and an improved decentralized filtering, known as global optimal filtering (GOF)—are investigated. In principle, the GOF is derived from more information resources than the CKF and FKF algorithms. These three algorithms are implemented in a GPS/Locata/INS integrated navigation system and evaluated using data obtained from a flight test. The experimental results show that the position, velocity and attitude solution derived from the GOF-based system indicate improvements of 30, 18.4 and 20.8% over the CKF- and FKF-based systems, respectively.     

Estimating satellite phase fractional cycle biases based on Kalman filter

Phase fractional cycle biases (FCBs) originating from satellites and receivers destroy the integer nature of PPP carrier phase ambiguities. To achieve integer ambiguity resolution of PPP, FCBs of satellites are required. In former work, least squares methods are commonly adopted to isolate FCBs from a network of reference stations. However, it can be extremely time consuming concerning the large number of observations from hundreds of stations and thousands of epochs. In addition, iterations are required to deal with the one-cycle inconsistency among FCB measurements. We propose to estimate the FCB based on a Kalman filter. The large number of observations are handled epoch by epoch, which significantly reduces the dimension of the involved matrix and accelerates the computation. In addition, it is also suitable for real-time applications. As for the one-cycle inconsistency, a pre-elimination method is developed to avoid iterations and posterior adjustments. A globally distributed network consisting of about 200 IGS stations is selected to determine the GPS satellite FCBs. Observations recorded from DoY 52 to 61 in 2016 are processed to verify the proposed approach. The RMS of wide lane (WL) posterior residuals is 0.09 cycles while that of the narrow lane (NL) is about 0.05 cycles, which indicates a good internal accuracy. The estimated WL FCBs also have a good consistency with existing WL FCB products (e.g., CNES-GRG, WHU-SGG). The RMS of differences with respect to GRG and SGG products are 0.03 and 0.05 cycles. For satellite NL FCB estimates, 97.9% of the differences with respect to SGG products are within ±?0.1 cycles. The RMS of the difference is 0.05 cycles. These results prove the efficiency of the proposed approach.     

Testing block subdivision algorithms on block designs

Integrated land use–transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. While the authors who developed each of the algorithms have evaluated them, they have used their own metrics and block types to evaluate their own algorithms. This makes it difficult to compare their strengths and weaknesses. The contribution of this paper is in resolving this difficulty with the aim of finding a better algorithm suited to subdividing each block type. The proposed hypothesis is that given the different approaches that block subdivision algorithms take, it’s likely that different algorithms are better adapted to subdividing different block types. To test this, a standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the oriented bounding box algorithm performs better for warped non-uniform sites, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks as well as fragmented non-uniform and warped uniform sites. It also produces more similar parcel shapes and patterns.     

What governs the presence of residual vegetation in boreal wildfires?

Wildfires are frequent boreal forest disturbances in Canada, and emulating their patterns with forest harvesting has emerged as a common forest management goal. Wildfires contain many patches of residual vegetation of various size, shape, and composition; understanding their characteristics provides insights for improved emulation criteria. We studied the occurrence of residual vegetation within eleven boreal wildfire events in a natural setting; fires ignited by lightning, no suppression efforts, and no prior anthropogenic disturbances. Relative importance of the measurable geo-environmental factors and their marginal effects on residual presence are studied using Random Forests. These factors included distance from natural firebreaks (wetland, bedrock and non-vegetated areas, and water), land cover, and topographic variables (elevation, slope, and ruggedness index). We present results at spatial resolutions ranging from four to 64 m while emphasizing four and 32 m since they mimic IKONOS- and Landsat-type images. Natural firebreak features, especially the proximity to wetlands, are among the most important variables that explain the likelihood residual occurrence. The majority of residual vegetation areas are concentrated within 100 m of wetlands. Topographic variables, typically important in rugged terrain, are less important in explaining the presence of residuals within our study fires.     

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach

Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.     

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.     

Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

The social interaction potential (SIP) metric measures urban structural constraints on social interaction opportunities of a metropolitan region based on the time geographic concept of joint accessibility. Previous implementations of the metric used an interaction surface based on census tracts and the locations of their centroids. This has been shown to be a shortcoming, as the metric strongly depends on the scale of the zoning system in the region, making it difficult to compare the SIP metric between metropolitan regions. This research explores the role of spatial representation in the SIP metric and identifies a suitable grid-based representation that allows for comparison between regions while retaining cost-effectiveness with respect to computational burden. We also report on findings from an extensive sensitivity analysis investigating the SIP metric’s input parameters such as a travel flow congestion factor and the length of the allowable time budget for social activities. The results provide new insights on the role of the modifiable areal unit problem in the computation of time geographic measures of accessibility.     

Weak GPS signal tracking using FFT discriminator in open loop receiver

An open loop tracking architecture, which tracks GPS signals under weak and challenging conditions, is analyzed. The in-phase and quadrature-phase integration pair is regarded as a single tone complex signal. An FFT-based method is used as a frequency discriminator to estimate the Doppler frequency residual of the single tone signal. Another FFT-based method applies complex squaring to eliminate the effect of the navigation data bits polarities. The performance of the FFT-based discriminators is assessed in three criteria. Those criteria are the signal strength and dynamic range that can be tracked and the accuracy of the estimated Doppler frequency. In addition, the performance of the discriminators is analyzed to provide the theoretical and simulated peak detection probability. The results indicate that the FFT discriminator can track signals about 5 dB weaker than the signals that can be tracked by the complex squared FFT discriminator. In a quasi-static environment, the Doppler frequency residual can be assumed to be around zero, which can enable the FFT-based discriminators to track signals with approximately 2 dB less power. Moreover, the performance of the FFT-based discriminators is compared with the performance of two other frequency discriminators, namely the fast–slow and power-based. The comparison results indicate that these two frequency discriminators give higher frequency estimation accuracy, but they have a narrower dynamic range.     

Precise orbit determination of BeiDou constellation: method comparison

Chinese BeiDou navigation satellite system is in official service as a regional constellation with five geostationary earth orbit (GEO) satellites, five inclined geosynchronous satellite orbit (IGSO) satellites and four medium earth orbit (MEO) satellites. There are mainly two methods for precise orbit determination of the BeiDou constellation found in the current literatures. One is the independent single-system method, where only BeiDou observations are used without help from other GNSS systems. The other is the two-step GPS-assisted method where in the first step, GPS data are used to resolve some common parameters, such as station coordinates, receiver clocks and zenith tropospheric delay parameters, which are then introduced as known quantities in BeiDou processing in the second step. We conduct a thorough performance comparison between the two methods. Observations from the BeiDou experimental tracking stations and the IGS Multi-GNSS Experiment network from January 1 to March 31, 2013, are processed with the Positioning and Navigation Data Analyst (PANDA) software. The results show that for BeiDou IGSO and MEO satellites, the two-step GPS-assisted method outperforms the independent single-system method in both internal orbit overlap precision and external satellite laser ranging validation. For BeiDou GEO satellites, the two methods show close performances. Zenith tropospheric delays estimated from the first method are very close to those estimated from GPS precise point positioning in the second method, with differences of several millimeters. Satellite clock estimates from the two methods show similar performances when assessing the stability of the BeiDou on board clocks.     

Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability?

The global positioning system (GPS) differential code biases (DCB) provided by the International GNSS Service (IGS) show solar-cycle-like variation during 2002–2013. This study is to examine whether this variation of the GPS DCBs is associated with ionospheric variability. The GPS observations from low earth orbit (LEO) satellites including CHAMP, GRACE and Jason-1 are used to address this issue. The GPS DCBs estimated from the LEO-based observations at different orbit altitudes show a similar tendency as the IGS DCBs. However, this solar-cycle-like dependency is eliminated when the DCBs of 13 continuously operating GPS satellites are constrained to zero-mean. Our results thus revealed that ionospheric variation is not responsible for the long-term variation of the GPS DCBs. Instead, it is attributed to the GPS satellite replacement with different satellite types and the zero-mean condition imposed on all satellite DCBs.