“An Investigation into the Temporal Correlation at the ASF Monitor Sites” by Prof. Peter Swaszek, URI/USCGA Dr. Gregory Johnson, Alion Capt. Richard Hartnett, USCGA Dr. Sherman Lo, Stanford

or “A Partial Answer to David Last’s Question on Monitor Spacing Requirements”

Background ILA-35 (2006) – “Warping Time and Space: Spatial Correlation of Temporal Variations” –Seasonal Monitor Network Sites, equipment, software –Spatial Correlation Several anecdotal examples –ASF Filtering Reduce receiver noise effects

Prior Conclusions There is an obvious correlation in the ASFs of nearby sites –Depends on local topography Land-path stations experience more variation –Most extreme variations occur in winter Placement of monitors for dLoran will be dependent upon worst-case “correlation” –Winter in the NorthEast is the long pole

ILA-36 (today) Look at some of the available data –2 new sites –Some sites collecting over almost 2 years More on temporal correlation including error effects –Statistical measures –Error performance

The Seasonal Monitors circa Oct. 2007

Sites Monitored at CGA USCGA, New London CT URI, Kingston RI Volpe, Cambridge MA FAATC, Atlantic City NJ OU, Athens OH Staten Island, NY Goodspeed (CT) New Haven (CT) NEW !!!

Seasonal Monitor Sites 12/22/ MANY MILES

Shorter Baselines Distances 9

Purposes of Monitor Network Analysis of ASF variation for aviation –Center of range studies –Bounds on error dLoran system component for HEA –ASF updates to LSU –Broadcast out on LDC Sherman’s presentation next Greg’s presentation tomorrow

What’s New Today We have lots more data, some on shorter baselines –Includes pre/post-TOT transition –2 summers/winters for the early sites Examine statistics versus distance Examine position error performance of dLoran versus distance

Some ASF Data

Typical ASF Data

ASF Data from Monitor Sites Have long assumed that the ASF can be decomposed into 3 independent, additive terms: –Spatial term –Temporal term –Directional term for a moving antenna For further visuals, we remove (zero out) the spatial term –Temporal term forced to mean of zero –Directional term assumed to be zero

Typical Temporal Term

Some Comparisons Seasonal differences –Summer (June1 – August 31) –Winter (January 1 – March 31) Two year repeatability “Correlation” site-to-site –High –Low ASF differences

Our Winter/Summer Definition

Repeatability of ASFs – 2 Years at One Site

Repeatability – Zoom of Summer

Repeatability – Zoom into Winter

Site-to-Site, Strong Correlation

Site-to-Site, Weaker Correlation

Differences of the ASFs

and

Statistics What’s relevant to compute? Correlation coefficient is one option –ρ = 1 just means a “linear” relationship Ignores scaling and offset Not relevant for error analysis Will look at average differences in ASF

Measure “spread” of differences in ASF by standard deviation of differences –Tabulate average standard deviation of differences –Focus on pairwise characteristics of close sites – short baselines only

Table of Results (nanosec) Monitor site pair Distance km Yearly average Summer average Winter average CGA/GSPD HVN/GSPD CGA/URI CGA/HVN URI/GSPD URI/TSC ACY/OU

Position Error Performance How far away from a monitor site are the ASFs good enough for dLoran? –Measure above is unclear –Anecdotal evidence from harbor testing Approach – identify position error due to mismatch –Consider one monitor site as a “mobile” receiver –Use ASFs from second site in position solution

Example –URI & TSC ASFs SWAP ASFs

Example SUMMER WINTER

Performance Results Average over time –All year, winter, summer Tabulate 95% error radii Focus on pairwise characteristics of close sites – short baselines – only

Best Site-to-Site Performance SUMMER WINTER

95% Error Radius vs Distance

Conclusions/Future While ASFs are clearly correlated at nearby sites, position performance is sensitive to mismatch –Close spacing seems necessary for HEA –dLoran for aviation could accept wider spacing –Error budget needs to also include receiver noise and spatial ASF components Will continue collecting and testing data – Get shorter baseline data (along coastline) from PIG/LSU sites Point Allerton (MA) Sandy Hook (NJ)