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`MULTIPLE LOCATION ESTIMATORS FOR WIRELESS LOCATION
`RELATED APPLICATIONS
`The present application is a continuation of U.S. Application No. 09/194,367 filed November 24, 1998
`
`which is the National Stage oflntemational Application No. PCT/US97/15892, filed Sept. 8, 1997 which claims
`
`5
`
`the benefit of the following three provisionals: U.S. Provisional Application No. 60/056,590 filed August 20,
`
`1997; U.S. Provisional Application No. 60/044,821 filed April 25, 1997; and U.S. Provisional Application No.
`
`60/025,855 filed September 9, 1996.
`FIELD OF THE INVENTION
`
`The present invention is directed generally to a system and method for locating people and/or objects, and in
`
`10
`
`particular, to a network gateway for receiving a plurality of requests for locating mobile stations for, e.g., E9 l l
`
`requests, and vehicle location or tracking, and requesting locations of the mobile stations from one or more ofa
`
`plurality of mobile station location estimators, wherein such estimators may be based on, e.g., coverage area, GPS,
`
`TDOA, and AOA location techniques, and/or signal pattern recognition/matching.
`BACKGROUND
`Wireless communications systems are becoming increasingly important worldwide. Wireless cellular
`
`15
`
`telecommunications systems are rapidly replacing conventional wire-based telecommunications systems in many
`
`applications. Cellular radio telephone networks ("CRT"), and specialized mobile radio and mobile data radio
`
`networks are examples. The general principles of wireless cellular telephony have been described variously, for
`
`example in U. S. Patent 5,295,180 to Vendetti, et al, which is incorporated herein by reference.
`
`20
`
`There is great interest in using existing infrastructures for wireless communication systems for locating
`
`people and/or objects in a cost effective manner. Such a capability would be invaluable in a variety of situations,
`
`especially in emergency or crime situations. Due to the substantial benefits of such a location system, several
`
`attempts have been made to design and implement such a system.
`
`Systems have been proposed that rely upon signal strength and trilateralization techniques to permit location
`
`25
`
`include those disclosed in U.S. Patents 4,818,998 and 4,908,629 to Apsell et al. ("the Apsell patents") and
`
`4,891,650 to Sheffer ("the Sheffer patent"). However, these systems have drawbacks that include high expense in
`
`that special purpose electronics are required. Furthermore, the systems are generally only effective in line-of(cid:173)
`
`sight conditions, such as rural settings. Radio wave surface reflections, refractions and ground clutter cause
`
`significant distortion, in determining the location of a signal source in most geographical areas that are more than
`
`30
`
`sparsely populated. Moreover, these drawbacks are particularly exacerbated in dense urban canyon (city) areas,
`
`where errors and/or conflicts in location measurements can result in substantial inaccuracies.
`
`Another example of a location system using time of arrival and triangulation for location are satellite-based
`
`systems, such as the military and commercial versions of the Global Positioning Satellite system ("GPS"). GPS
`
`can provide accurate position determination (i.e., about 100 meters error for the commercial version ofGPS) from
`
`35
`
`a time-based signal received simultaneously from at least three satellites. A ground-based GPS receiver at or near
`
`the object to be located determines the difference betvveen the time at which each satellite transmits a time signal
`
`and the time at which the signal is received and, based on the time differentials, determines the object's location.
`
`However, the GPS is impractical in many applications. The signal power levels from the satellites are low and the
`
`GPS receiver requires a clear, line-of-sight path to at least three satellites above a horizon of about 60 degrees for
`
`40
`
`effective operation. Accordingly, inclement weather conditions, such as clouds, terrain features, such as hills and
`
`trees, and buildings restrict the ability of the GPS receiver to determine its position. Furthermore, the initial GPS
`
`signal detection process for a GPS receiver is relatively long (i.e., several minutes) for determining the receiver's
`
`position. Such delays are unacceptable in many applications such as, for example, emergency response and
`
`vehicle tracking.
`
`1
`
`Apple, Inc. Exhibit 1030 Page 5
`
`

`
`Differential GPS, or DGPS systems offer con-ection schemes to account for time syncln·onization drift. Such
`
`con-ection schemes include the transmission of con-ection signals over a two-way radio link or broadcast via FM
`
`radio station subcan-iers. These systems have been found to be awkward and have met with limited success.
`
`Additionally, GPS-based location systems have been attempted in which the received GPS signals are
`
`5
`
`transmitted to a central data center for performing location calculations. Such systems have also met with limited
`
`success. In brief, each of the various GPS embodiments have the same fundamental problems of limited reception
`
`of the satellite signals and added expense and complexity of the electronics required for an inexpensive location
`
`mobile station or handset for detecting and receiving the GPS signals from the satellites.
`
`Radio Propagation Background
`
`10
`
`The behavior of a mobile radio signal in the general environment is unique and complicated. Efforts to
`
`perform con-elations between radio signals and distance between a base station and a mobile station are similarly
`
`complex. Repeated attempts to solve this problem in the past have been met with only marginal success. Factors
`
`include ten-ain undulations, fixed and variable clutter, atmospheric conditions, internal radio characteristics of
`
`cellular and PCS systems, such as frequencies, antenna configurations, modulation schemes, diversity methods,
`
`15
`
`and the physical geometries of direct, refracted and reflected waves between the base stations and the mobile.
`
`Noise, such as man-made externally sources (e.g., auto ignitions) and radio system co-channel and adjacent
`
`channel interference also affect radio reception and related performance measlu-ements, such as the analog can-ier(cid:173)
`
`to-interference ratio (C/I), or digital energy-per-bit'Noise density ratio (Eb!No) and are particular to various points
`
`20
`
`in time and space domains.
`RF Propagation in Free Space
`Before discussing real world con-elations between signals and distance, it is useful to review the theoretical
`
`premise, that of radio energy path loss across a pure isotropic vacuum propagation channel, and its dependencies
`
`within and among various communications channel types. Fig. 1 illustrates a definition of channel types arising in
`
`communications:
`
`25
`
`Over the last forty years various mathematical expressions have been developed to assist the radio mobile cell
`
`designer in establishing the proper balance between base station capital investment and the quality of the radio
`
`link, typically using radio energy field-strength, usually measured in microvolts/meter, or decibels.
`
`First consider Hata's single ray model. A simplified radio channel can be described as:
`
`Gi =LP+ F +Lr+ Lm +Lb - G 1 +Gr
`where Gi = system gain in decibels
`
`30
`
`(Equation 1)
`
`LP= free space path loss in dB,
`
`F = fade margin in dB,
`
`Lr = transmission line loss from coaxials used to connect radio to antenna, in dB,
`
`Lm= miscellaneous losses such as minor antenna misalignment, coaxial con-osion, increase in the receiver
`
`35
`
`noise figure due to aging, in dB,
`
`Lb= branching loss due to filter and circulator used to combine or split transmitter and receiver signals in a
`
`single antenna
`
`G 1= gain of transmitting antenna
`Gr= gain of receiving antenna
`
`40
`
`Free space path loss LP as discussed in Mobile Communications Design Fundamentals, William C. Y. Lee,
`
`2nd, Ed across the propagation channel is a function of distance d, frequency
`f (for fvalues < 1 GHz, such as the 890-950 mHz cellular band):
`
`1
`( 4ndfe) 2
`
`(equation 2)
`
`2
`
`Apple, Inc. Exhibit 1030 Page 6
`
`

`
`where P or = received power in free space
`
`Pt =transmitting power
`
`c =speed of light,
`The difference between two received signal powers in free space,
`
`5
`
`11 P = (10) log(Por 2
`porl
`
`) = (20) log(~)( dB)
`d2
`
`(equation 3)
`
`indicates that the free propagation path loss is 20 dB per decade. Frequencies between 1 GHz and 2GHz
`experience increased values in the exponent, ranging from 2 to 4, or 20 to 40 dB/decade, which would be
`
`predicted for the new PCS 1.8 - 1.9 GHz band.
`This suggests that the free propagation path loss is 20 dB per decade. However, frequencies between 1 GHz
`
`10
`
`and 2 GHz experience increased values in the exponent, ranging from 2 to 4, or 20 to 40 dB/decade, which would
`be predicted for the new PCS 1.8 - 1.9 GHz band. One consequence from a location perspective is that the
`
`effective range of values for higher exponents is an increased at higher frequencies, thus providing improved
`granularity of ranging conelation.
`Environmental Clutter and RF Propagation Effects
`Actual data collected in real-world environments uncovered huge variations with respect to the free space
`path loss equation, giving iise to the creation of many empirical formulas for radio signal coverage prediction.
`
`15
`
`Clutter, either fixed or stationary in geometric relation to the propagation of the radio signals, causes a shadow
`effect of blocking that perturbs the free space loss effect. Perhaps the best known model set that characterizes the
`
`average path loss is Hata's, "Empirical Formula for Propagation Loss in Land Mobile Radio", M. Hata, IEEE
`Transactions VT-29, pp. 317-325, August 1980, three pathloss models, based on Okumura's measurements in and
`
`20
`
`25
`
`30
`
`35
`
`around Tokyo, "Field Strength and its Variability in VHF and UHF Land Mobile Service", Y. Okumura, et al,
`Review of the Electrical Communications laboratory, Vol 16, pp 825-873, Sept. - Oct. 1968.
`
`The typical urban Hata model for LP was defined as LP = Lhu:
`LHu = 69.55 + 26.16 log(f)-13.82 log(hBs )- a(hMs) + (( 44.9- 6.55 log(HBs) log(d)[dB])
`(Equation 4)
`
`where LHu =path loss, Hata urban
`
`hBs = base station antenna height
`
`hMS= mobile station antenna height
`
`d =distance BS-MS in km
`
`a(hMS) is a correction factor for small and medium sized cities, found to be:
`1 log(f - 0. 7)h MS - 1.56 log(f - 0.8) = a ( h MS )
`
`For large cities the correction factor was found to be:
`
`2
`a (hMs) = 3.2[log11.75hMs] - 4.97
`
`assuming f is equal to or greater than 400 mHz.
`
`The typical suburban model correction was found to be:
`
`(Equation 5)
`
`(Equation 6)
`
`LH
`
`suburban
`
`= LHu - 2[1og(LJ
`28
`
`2
`
`]-s.4[dB]
`
`(Equation 7)
`
`The typical rural model modified the urban formula differently, as seen below:
`
`3
`
`Apple, Inc. Exhibit 1030 Page 7
`
`

`
`LHrural = LHu-4.78 (logf)- + 18.33logf-40.94 [dB]
`
`')
`
`(Equation 8)
`Although the Hata model was found to be useful for generalized RF wave prediction in frequencies under 1
`
`5
`
`GHz in certain suburban and rural settings, as either the frequency and/or clutter increased, predictability
`decreased. In current practice, however, field technicians often have to make a guess for dense urban an suburban
`
`10
`
`areas (applying whatever model seems best), then installing a base stations and begin taking manual
`measurements. Coverage problems can take up to a year to resolve.
`Relating Received Signal Strength to Location
`Having previously established a relationship between d and Pon reference equation 2 above: d represents the
`distance between the mobile station (MS) and the base station (BS); P 0 r represents the received power in free
`space) for a given set of unchanging environmental conditions, it may be possible to dynamically measure P 0 r and
`then determine d.
`
`In 1991, U.S. Patent 5,055,851 to Sheffer taught that ifthree or more relationships have been established in
`a triangular space of three or more base stations (BSs) with a location database constrncted having data related to
`
`15
`
`possible mobile station (MS) locations, then arculation calculations may be performed, which use three distinct
`P or measurements to determine an X,Y, two dimensional location, which can then be projected onto an area map.
`
`The triangulation calculation is based on the fact that the approximate distance of the mobile station (MS) from
`any base station (BS) cell can be calculated based on the received signal strength. Sheffer acknowledges that
`
`20
`
`terrain variations affect accuracy, although as noted above, Sheffer's disclosure does not account for a sufficient
`number of variables, such as fixed and variable location shadow fading, which are typical in dense urban areas
`
`with moving traffic.
`
`Most field research before about 1988 has focused on characterizing (with the objective of RF coverage
`
`prediction) the RF propagation channel (i.e., electromagnetic radio waves) using a single-ray model, although
`standard fit errors in regressions proved dismal (e.g., 40-80 dB). Later, multi-ray models were proposed, and
`
`25
`
`much later, certain behaviors were studied with radio and digital channels. In 1981, Vogler proposed that radio
`waves at higher frequencies could be modeled using optics principles. In 1988 Walfisch and Bertoni applied
`
`optical methods to develop a two-ray model, which when compared to certain highly specific, controlled field
`data, provided extremely good regression fit standard errors of within 1.2 dB.
`
`30
`
`In the Bertoni two ray model it was assumed that most cities would consist of a core of high-rise buildings
`surrounded by a much larger area having buildings of uniform height spread over regions comprising many square
`
`blocks, with street grids organizing buildings into rows that are nearly parallel. Rays penetrating buildings then
`emanating outside a building were neglected. Fig. 2 provides a basis for the variables.
`
`After a lengthy analysis it was concluded that path loss was a function of three factors: (1) the path loss
`between antennas in free space; (2) the reduction of rooftop wave fields due to settling; and (3) the effect of
`
`35
`
`diffraction of the rooftop fields down to ground level. The last two factors were summarily termed Lex, given by:
`
`Lex = 5 7 .1 + A + log ( f) + R -
`
`2
`R
`( ( 1 8 log ( H)) - 1 8 log 1 - - - (Equation 9)
`]
`[
`l 7H
`
`The influence of building geometry is contained in A:
`
`[( <l) 2]
`A= Slog 2
`
`-91ogd+201og{tan[2(h-HMs)]-}
`
`1
`
`(Equation 10)
`
`4
`
`Apple, Inc. Exhibit 1030 Page 8
`
`

`
`However, a substantial difficulty with the two-ray model in practice is that it requires a substantial amount of
`data regarding building dimensions, geometries, street widths, antenna gain characteristics for every possible ray
`
`path, etc. Additionally, it requires an inordinate amount of computational resources and such a model is not easily
`updated or maintained.
`
`5
`
`Unfortmmtely, in practice clutter geometries and building heights are random. Moreover, data of sufficient
`detail has been extremely difficult to acquire, and regression standard fit enors are poor; i.e., in the general case,
`
`these enors were found to be 40-60 dB. Thus the two-ray model approach, although sometimes providing an
`improvement over single ray techniques, still did not predict RF signal characteristics in the general case to level
`
`of accuracy desired (<lOdB).
`Work by Greenstein has since developed from the perspective of measurement-based regression models, as
`
`10
`
`opposed to the previous approach of predicting-first, then performing measurement comparisons. Apparently
`yielding to the fact that low-power, low antenna (e.g., 12-25 feet above ground) height PCS microcell coverage
`
`was insufficient in urban buildings, Greenstein, et al, authored "Performance Evaluations for Urban Line-of-sight
`Microcells Using a Multi-ray Propagation Model", in IEEE Globecom Proceedings, 12/91. This paper proposed
`
`15
`
`the idea of formulating regressions based on field measurements using small PCS microcells in a lineal microcell
`geometry (i.e., geometries in which there is always a line-of-sight (LOS) path between a subscriber's mobile and
`
`its cunent microsite).
`Additionally, Greenstein studied the communication channels variable Bit-Enor-Rate (BER) in a spatial
`
`domain, which was a departure from previous research that limited field measurements to the RF propagation
`channel signal strength alone. However, Greenstein based his finding on two suspicious assumptions: 1) he
`
`20
`
`assumed that distance conelation estimates were identical for uplink and downlink transmission paths; and 2)
`modulation techniques would be transparent in terms of improved distance conelation conclusions. Although
`
`some data held very conelations, other data and environments produced poor results. Accordingly, his results
`appear unreliable for use in general location context.
`
`25
`
`In 1993 Greenstein, et al, authored "A Measurement-Based Model for Predicting Coverage Areas of Urban
`Microcells", in the IEEE Journal On Selected Areas in Communications, Vol. 11, No. 7, 9/93. Greenstein
`
`reported a generic measurement-based model of RF attenuation in terms of constant-value contours sUITounding a
`given low-power, low antenna microcell environment in a dense, rectilinear neighborhood, such as New York
`
`City. However, these contours were for the cellular frequency band. In this case, LOS and non-LOS clutter were
`considered for a given microcell site. A result of this analysis was that RF propagation losses (or attenuations),
`
`30
`
`when cell antenna heights were relatively low, provided attenuation contours resembling a spline plane curve
`depicted as an asteroid, aligned with major street grid patterns. Further, Greenstein found that convex diamond(cid:173)
`
`shaped RF propagation loss contours were a common occunence in field measurements in a rectilinear urban
`area. The special plane curve asteroid is represented by the formula x213 + y213
`= r2/3. However, these results
`alone have not been sufficiently robust and general to accurately locate an MS, due to the variable nature of urban
`clutter spatial anangements ..
`
`35
`
`At Telesis Technology in 1994 Howard Xia, et al, authored "Microcellular Propagation Characteristics for
`Personal Communications in Urban and Suburban Environments", in IEEE Transactions of Vehicular
`
`Technology, Vol. 43, No. 3, 8/94, which performed measurements specifically in the PCS 1.8 to 1.9 GHz
`frequency band. Xia found conesponding but more variable outcome results in San Francisco, Oakland (urban)
`
`40
`
`and the Sunset and Mission Districts (suburban).
`
`5
`
`Apple, Inc. Exhibit 1030 Page 9
`
`

`
`Summary of Factors Affecting RF Propagation
`
`The physical radio propagation channel perturbs signal strength, frequency (causing rate changes, phase
`
`delay, signal to noise ratios (e.g., C/I for the analog case, or Eb/No , RF energy per bit, over average noise density
`
`ratio for the digital case) and Doppler-shift. Signal strength is usually characterized by:
`
`5
`
`· Free Space Path Loss (Lp)
`
`· Slow fading loss or margin (Ls1ow)
`
`· Fast fading loss or margin (Lfast)
`
`Loss due to slow fading includes shadowing due to clutter blockage (sometimes included in Lp). Fast fading
`
`is composed of multipath reflections which cause: 1) delay spread; 2) random phase shift or Rayleigh fading; and
`
`10
`
`3) random frequency modulation due to different Doppler shifts on different paths.
`
`Summing the path loss and the two fading margin loss components from the above yields a total path loss of:
`Lrotal = Lp + Lslow + Lfast
`Referring to Fig. 3, the figure illustrates key components of a typical cellular and PCS power budget design
`
`process. The cell designer increases the transmitted power PTX by the shadow fading margin Lsiow which is
`
`15
`
`usually chosen to be within the 1-2 percentile of the slow fading probability density function (PDF) to minimize
`
`the probability of unsatisfactorily low received power level PRX at the receiver. The PRX level must have enough
`
`signal to noise energy level (e.g., 10 dB) to overcome the receiver's internal noise level (e.g., -l 18dBm in the case
`
`of cellular 0.9 GHz), for a minimum voice quality standard. Thus in the example PRX must never be below -108
`
`dBm, in order to maintain the quality standaTd.
`
`20
`
`Additionally the short term fast signal fading due to multipath propagation is taken into account by
`
`deploying fast fading margin Lfast. which is typically also chosen to be a few percentiles of the fast fading
`
`distribution. The 1 to 2 percentiles compliment other network blockage guidelines. For example the cell base
`
`station traffic loading capacity and network transport facilities are usually designed for a 1-2 percentile blockage
`
`factor as well. However, in the worst-case scenario both fading margins are simultaneously exceeded, thus
`
`25
`
`causing a fading margin overload.
`
`In Roy, Steele's, text, Mobile Radio Communications, IEEE Press, 1992, estimates for a GSM system
`
`operating in the 1.8 GHz band with a transmitter antenna height of 6.4m and an MS receiver antenna height of
`
`2m, and assumptions regarding total path loss, transmitter power would be calculated as follows:
`
`Table 1: GSM Power Budget Example
`
`30
`
`35
`
`Parameter
`
`Lslow
`
`Lfast
`
`Llpath
`
`Min. RX pwr required
`
`dBm value
`
`Will require
`
`14
`
`7
`
`110
`
`-104
`
`Steele's sample size in a specific urban London area of 80,000 LOS measurements and data reduction found a
`
`TXpwr = 2 7 dBm
`
`slow fading variance of
`a= 7dB
`
`assuming lognormal slow fading PDF and allowing for a 1.4% slow fading margin overload, thus
`L
`= 2a = 14dB
`slow
`
`The fast fading margin was determined to be:
`
`6
`
`Apple, Inc. Exhibit 1030 Page 10
`
`

`
`Lfast = 7dB
`
`In contrast, Xia's measurements in urban and suburban California at 1.8 GHz uncovered flat-land shadow
`
`fades on the order of25-30 dB when the mobile station (MS) receiver was traveling from LOS to non-LOS
`
`geometries. In hilly terrain fades of +5 to -50 dB were experienced. Thus it is evident that attempts to correlate
`
`5
`
`signal strength with MS ranging distance suggest that error ranges could not be expected to improve below 14 dB,
`
`with a high side of 25 to 50 dB. Based on 20 to 40 dB per decade, Corresponding error ranges for the distance
`
`variable would then be on the order of900 feet to several thousand feet, depending upon the particular
`
`environmental topology and the transmitter and receiver geometries.
`
`Description Of Terms
`The following definitions are provided for convenience. In general, the de