Patent Application
20120042192
The invention relates to improving frequency stability in frequency reference devices used for example in contemporary communication and navigation systems.
Thermal hysteresis has been a significant contributing factor to frequency instability of RF oscillators and in particular, quartz crystal oscillators (XO) which are currently the most widely used frequency reference technology for high-performance consumer applications, such as mobile phones and GPS receivers, due to the corresponding high performance-to-cost ratio.
Current technology used to achieve maximum frequency stability in XO uses interpolation of a lookup table of frequency and temperature measurements to minimise temperature induced frequency error. However this approach cannot remove the effects of hysteresis and is typically limited to about ±0.25 ppm stability over a temperature range of −30° C. to +80° C.
U.S. Pat. Nos. 7,259,637 and 7,466,209 disclose systems which attempt to model thermal hysteresis in XO by offering two possible frequency curves for each of the two directions of temperature. U.S. Pat. No. 7,259,637 discloses a system in which a separate lookup-table of temperature versus frequency values is used for when the temperature is increasing to when the temperature decreasing. U.S. Pat. No. 7,466,209 discloses a system which aims to account for temperature versus frequency directional dependence. This is achieved by providing a single function that models a frequency curve for when the temperature is increasing and another frequency curve for when the temperature is decreasing.
As consumer expectations become increasingly, demanding, the frequency stability requirements from manufacturers become progressively more stringent. For example, as GPS technology has become more and more widespread—so has the consumer expectation of using a GPS receiver indoors. This typically requires operating the receiver in extremely weak signal conditions which can be in the order of −165 dBm, pushing the limits of the current state of the art.
One method of improving receiver performance is increasing the integration periods in the code correlation. However this has the consequence that the tolerance of the receiver to frequency reference instabilities is dramatically reduced through a square law relationship. Thus if the contribution of hysteresis to these instabilities can be mitigated then it will allow GPS receivers to take advantage of longer integration times, therefore offering improved sensitivity without significantly increasing core processing power.
In broad terms in one aspect the invention comprises a method of improving the effective frequency stability of a frequency reference device, wherein an algorithm utilizing a set of mathematical parameters determined from frequency and temperature sensing measurements of the device over a number of temperature excursions of different magnitudes is used in conjunction with temperature measurement history to account for effects of hysteresis in the said device's frequency-temperature characteristic.
In broad terms in another aspect the invention comprises a method of improving the frequency stability of a frequency reference, or improving the effective frequency stability of a frequency reference in a host application system, comprising:
In broad terms in another aspect the invention comprises a method of improving the frequency stability of a frequency reference, or improving the effective frequency stability of a frequency reference in a host application system, comprising:
In broad terms in a further aspect the invention comprises a frequency reference source comprising:
In broad terms in a further aspect the invention comprises a frequency reference source or host application system comprising:
The invention provides for frequency references with improved frequency stability by providing a frequency reference source or method arranged to calculate an estimate of the thermal hysteresis induced frequency error. This frequency error can then be fed-forward to a frequency synthesizer in the host application (for example a spread-spectrum radio receiver such as GPS) or, fed-back to a tuning input in the frequency reference.
In broad terms in a further aspect the invention comprises a method of manufacturing a frequency reference device which includes subjecting the device to frequency and temperature sensing measurements over a number of temperature excursions of different magnitudes to determine a set of mathematical parameters for use in conjunction with temperature measurement history to account for effects of hysteresis in the said device's frequency-temperature characteristic.
In broad terms in a further aspect the invention comprises a manufacturing system for manufacturing a frequency reference device arranged to subject the device to frequency and temperature sensing measurements over a number of temperature excursions of different magnitudes, to build up a set of mathematical parameters for use in conjunction with a temperature measurement history to correct for effects of hysteresis in the said device's frequency-temperature characteristic.
In broad terms in a further aspect the invention comprises frequency reference device together with hysteresis characterization values or model parameters derived from frequency and temperature sensing data, said data relating to multiple temperature excursion dependent hysteresis branches of either the reference frequency with respect to temperature, or the frequency of a reference device having sufficiently similar frequency vs. temperature hysteresis behavior.
In some embodiments the invention comprise a frequency reference in close thermal contact with a temperature sensor, a block of memory which stores hysteresis characterization values or parameters and history data, an algorithm which adaptively defines the historical temperature reversal points of interest and writes them to memory, and a frequency prediction algorithm which takes the characterization values or parameters and history data and calculates the effect of each of the temperature history reversal points on the frequency error and sums them up to provide an estimate of the total frequency error.
In this specification and claims, unless the context otherwise indicates, the term ‘frequency reference device’ means an oscillator device providing an electrical signal with a stable frequency; the term ‘frequency reference’ means the stable signal provided by such a device, however it can sometimes be used to mean the frequency reference device, the meaning being clear from the context; and the term ‘reference frequency’ means the frequency of the reference signal.
The term “comprising” as used in this specification means “consisting at least in part of”. When interpreting each statement in this specification that includes the term “comprising”, features other than that or those prefaced by the term may also be present. Related terms such as “comprise” and “comprises” are to be interpreted in the same manner.
Preferred embodiments of the invention will be described by way of example only and with reference to the drawings, in which:
The error estimate can be used to directly improve the stability of the reference frequency by applying a correction signal 13 to the oscillator. Alternatively, the error signal can be sent to the host application system as in 14, where it may be possible to mitigate the effect of the frequency error, for example by tuning an onboard frequency synthesizer according to the error estimate. The double arrow between 7 and 8 indicates that the algorithm uses the state data for the error calculation, and also updates the state data as the hysteretic state of the reference frequency changes.
Important features of the invention are the use of hysteresis characterization data built from frequency and temperature measurements covering multiple branches of the hysteresis behaviour, and the use of hysteretic state parameters to essentially help evaluate which branch the frequency is moving along. These can then be used by an algorithm implementing a hysteresis model to calculate a true estimate of the hysteresis induced frequency error.
The two main embodiments described below are both based on the above structure, however the algorithms implement different model structures with the consequence that the characterization data, and hysteretic state parameters take on different forms. This will be explained in more detail in the sections below which describe the embodiments.
In the functional diagram, reference to the physical location of the blocks is avoided in order to more clearly illustrate the concept of the invention. In one embodiment elements 1-4 may be integrated into a single oscillator package and elements 5-12 may reside in the host application. Other configurations are possible and are discussed in more detail subsequently.
In this current embodiment, the oscillator is provided with hysteresis characterization values or parameters 9 from the oscillator manufacturer; typically unique for every oscillator sample. The characterization values or parameters may be generated from frequency and temperature measurements of the oscillator sample while it is subjected to a specific temperature profile using a temperature control system as will be further described. This may be done prior to installation of the frequency reference device in the frequency reference source or host application system.
In a preferred embodiment the temperature profile used is referred to as the set of “first order reversal curves” (FORC) in accordance with the scientific literature on ferromagnetic hysteresis. The FORC profile (temperature voltages for exemplary profile shown in
When the FORC measurements are completed, the data is partitioned into the individual reversal curves which are defined as the increasing segments between the reversal points Tαn and T0. For each of the n reversal curves are defined the temperature sensor voltages and frequencies at the temperature reversal points Tαn as αn and fn(αn) respectively. The temperature sensor voltages as the temperature increases from Tαn to T0 are then denoted as βni and the corresponding frequencies are denoted fn(βni).
Starting from the reversal curve associated with the first reversal point α1, build a three column table with a repeated value of α1, the temperature sensor voltages β1i and the output increment Δ between the measured frequency at β1i and the interpolated value of the descending section of the major loop(i.e. Δ1i=f−(β1i)−f1(β1i)).
Repeat step two for each of the n reversal curves and concatenate each of the tables into a single table of α, β, and Δ values.
Fit a 2 dimensional function to the table to get the output increment versus α and β. In the present embodiment we use a polynomial function of the form
This can be solved using standard least-squares techniques such as QR decomposition. Alternative embodiments may use different methods to represent the function, for example the values of Δ (α, β) could be stored in memory and interpolated, alternatively universal approximators based on fuzzy logic and/or neural networks could also be used.
The history data block 8 in
The criterion for choosing which table entries to keep or discard is based on the “wiping out” property of the Preisach Model. The wiping out property means that only an alternating series of dominant maxima and minima are stored in the history. Whenever the temperature sensor voltage increases above a stored maxima or decreases below a stored minima then that maxima/minima is no longer dominant and is deleted from the history table.
Pseudo-code for the history update algorithm 7 is shown in
The frequency prediction algorithm 11 of
The pseudo-code describes the evaluation of the following function for the “Moving Preisach” model [1], after loading all the appropriate parameters/characterization data from memory.
Where f+ and Δ come from equations 1 & 2 respectively and u(t) is the temperature sensor voltage.
Equation 3 essentially sums up the individual contribution of each pair of maxima and minima in the updated history table to the output increment and adds it to the ascending section of the major loop.
The output of the frequency prediction algorithm gives an estimate of the error in the frequency reference. This information can be used in a number of ways to correct for the device's frequency error and improve the performance of the host application system.
The preferred method of correcting for the frequency error is to use the error estimate to digitally tune a frequency synthesizer in the host application system such as a Numerically Controlled Oscillator (NCO) or a Phase Locked Loop (PLL). This is particularly pertinent in spread-spectrum radio receivers which perform the correction through software after the main mixing process is complete, without having a significant impact on the performance. A GPS receiver is an example of a system which suits such an approach.
Another method of correcting for the frequency error is to physically tune the frequency reference. This is possible in tunable devices (for example a quartz resonator which can be tuned with a variable capacitance via a control voltage) as well as devices which already have an operating onboard microprocessor (e.g. a Microcontroller Compensated Crystal Oscillator MCXO), in which case the hysteresis compensation method can be autonomously implemented in the frequency reference device itself, provided there is enough memory, processor power, sufficiently high resolution ADC and DACs as well as extremely good isolation of the oscillator circuitry from any electromagnetic interference induced by the microcontroller.
In a second embodiment, the hysteretic state can be maintained by keeping track of the most recent estimate of the reference frequency and the corresponding temperature sensor reading. This embodiment evaluates a model which gives an estimate of any frequency error using the last known hysteretic state and the current temperature. The behavior of the frequency can be represented by a differential equation such as the following:
where F1(u, f) and F2 (u, f) are functions of the temperature sensor and frequency (u, and f). In this case, the frequency vs. temperature behavior is described by a separate set of curves for each of the two directions of temperature sensor change, such that each point (u, f) is unique for a given direction of u.
The functions F1(u, f) and F2 (u, f) are directly related to the frequency vs. temperature slope for the two possible directions of temperature change. These functions can be represented by 2D lookup tables, polynomials, splines, fuzzy logic models, neural networks, etc as in the characterization function in the previous embodiment. The parameters or values used to characterize these functions can then be solved for from frequency vs. temperature sensor measurements over different values of (u, f)—i.e. from temperature excursions of different magnitudes, as in the previous embodiment.
As an example, the temperature profile used for the previous embodiment given in
Once the characterization parameters bij have been found F2(u, f) is then given by simply taking the derivative of f2 (u, f) with respect to the temperature sensor giving:
Following the same method as above, F1(u, f) can be found from the set of first order reversal curves giving the branches for increasing temperatures, such as shown in
An exact or approximate integral of the differential equations can then be evaluated to provide an estimate of the frequency error. One method to calculate the frequency error at a given temperature sensor value is using numerical integration of equation 4 starting from some initial value f(u0)=f0. If the sampling rate of the temperature sensor is sufficiently high, then each sample can be treated as a single step n in the integration, so that for example in a first order approximation, the output at un+1 is given by
Alternatively the gap between each sample can be subdivided into smaller pieces, and/or higher order methods such as Runge-Kutta [2] could be employed—both of which may improve the solution accuracy.
In general, the first embodiment is preferred over the second embodiment because (in the forms presented) it requires less characterization data. An additional reason is that the assumption that each point of the hysteresis curve has a unique branch going through it for each direction of temperature is not 100% valid as can be seen in
In applications where there is access to some information about the actual frequency reference error, the hysteresis characterization values or parameters can be adaptively refined. For example a GPS receiver can effectively measure the frequency error against an atomic clock when it is tracking satellites. This information can potentially be used in conjunction with the temperature sensor information to adaptively improve the accuracy of the hysteresis characterization data, to correct for differences between the manufacturer's test system and the host application, or to correct for relatively slow time dependent effects such as aging.
The training algorithm essentially adaptively implements the characterization algorithms previously discussed—for example in the first embodiment this can be done by finding the current values of α and β from the last turning point and the current temperature sensor voltage, and then calculating the output increment of the measured frequency from the descending section of the major loop at the current temperature and storing it in a table. The polynomial described in equation 2 is then periodically refitted with a combination of the old data and the new data which incrementally improves the accuracy and relevance of the characterization data.
It is also feasible for the hysteresis characterization values or parameters to be built-up entirely by the host application in such circumstances. This would typically require the host application system to build the characterization data in real-time, in response to natural or ambient temperature fluctuations, or for the host application system to be subjected to a temperature profile such as the one given in
One method of incorporating adaptive refinement of the characterization parameters for the first embodiment is shown in
In some applications the time required to complete a measurement of the temperature, update the history table, make a frequency prediction, and finally apply the correction to a frequency synthesizer may be unacceptably long compared with the speed the temperature is changing at. In these cases extrapolation of the temperature may be necessary. This can be done by calculating the rate of change of temperature and using a first order linear extrapolation or by additionally calculating the acceleration and doing a second order approximation.
The time lag Δt can be adaptively controlled if needed by comparing the predicted temperature with the actual temperature as new data becomes available.
There may be insufficient memory space to store the frequency reference device's (FRD) hysteresis characterization data, particularly in miniature integrated FRDs intended for size- and cost-sensitive consumer applications such as mobile phones and GPS receivers. In accordance with the invention, the FRD may have a relatively small block of non-volatile memory wherein a unique (to each FRD sample) identification (ID) code is electronically stored. The full set of characterisation parameters can be stored elsewhere outside the FRD, with the ID code being uniquely linked to characterisation data pertaining to the specific FRD sample.
In such an arrangement, the ID code can be read out of the FRD, via a standard or a proprietary communications bus, by either the host application system or the manufacturing or maintenance plant equipment, followed by obtaining the full set of characterisation parameters from a location outside the FRD, using the ID code as a unique identifier of characterisation data pertaining the FDR. Such an arrangement reduces the requirements relating to the FRD's memory size considerably.
For example, storing the full set of FRD's characterization data may require sufficient memory for storing 31 parameters and (assuming 32-bit precision), consequently requiring 992 bits (124 bytes) of non-volatile memory space. In accordance with this aspect of the invention, only a unique ID code occupying for example 32-bits of memory is stored and is used as a link to the full set of FRD's hysteresis model parameters.
In a case when the FRD is a TCXO (Temperature Compensated Crystal Oscillator), the amount of memory required for storing the ID code can be further reduced, by taking advantage of the fact that in integrated TCXOs, the contents of programmable registers defining temperature compensation parameters can be made unique for any TCXO within a certain production batch.
For example, in a contemporary miniature TCXO utilizing a 5th order compensation function generator, there are some 12 registers, ranging from 4 to 7 bits each, that are programmed during the TCXO manufacture to optimise the compensation function. Within a limited batch of TCXOs, for example in a batch of TCXOs produced within a single day, each TCXO either contains or can be made to contain a unique combination of binary values programmed in the registers that define temperature compensation parameters. An ID code uniquely identifying each of the TCXO devices can be made up by combining the contents of temperature compensation registers with registers storing a current date code. The latter can be feasibly stored in only as many as 13 bits of binary memory space.
As stated, utilizing the unique ID code allows the retrieval of the FRD's characterization data from locations outside of the FRD itself.
One possible implementation of such an arrangement is to provide the characterization data on a digital media (such as an optical disk, or a portable memory block, etc.) which is supplied by the FRD manufacturer with the FRD itself. The host application system manufacturing process may be arranged to read the characterization data from the said media (utilizing the FRD's ID code as a link to the pertinent data) and store the data in host application's memory space.
In another implementation, the characterization data is stored on the FRD manufacturer's server, or the host application manufacturer's server, or in both of these locations. In this case, the host application system itself, or the host application system's manufacturing or maintenance plant will access the server and retrieve the FRD characterization parameters data. Such server access can be done by advantageously utilizing network channels already available to devices such as mobile phones (e.g., CDMA or GSM cellular networks).
One application of the invention described herein is in improving time stability. This can be useful in many applications requiring precise time—one such application being GPS receivers.
The figure shows that in addition to going into the GPS frontend for down-conversion as per usual, the RF output of the oscillator IC also goes into a binary counter which counts the number of pulses over time. Periodic temperature measurements are made by the ADC which are fed into a frequency model (in this case the hysteresis model), and the frequency readings are integrated against the binary counter readings to get the precise time, which is fed (along with the precise frequency) into the baseband to control the acquisition process by steering the NCO and constraining the search-space.
The frequency reference device may be a quartz crystal oscillator, a temperature compensated quartz crystal oscillator or a MEMS resonator. The host application may be a spread-spectrum radio receiver or a GPS receiver. The memory storing hysteresis characterization values or parameters may extend to the frequency reference source in a host application system (e.g. the memory may be in the host application system). Alternatively the values or parameters may be retrievable from memory accessible by either a host application system or a host application system manufacturing or maintenance plant.
Alternative embodiments are possible which differ to those described above yet achieve similar results, for example several alternative hysteresis models exist in literature, and it is also to formulate the models in different ways. Additionally, some of the parts used in the embodiments may be replaced with other parts performing a similar functionality—for example universal approximators such as fuzzy logic and neural networks. It should therefore be understood that the embodiments presented herein are merely illustrative and do not limit the scope of the invention which is defined in the accompanying claims.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/NZ2009/000266 | 11/30/2009 | WO | 00 | 10/18/2011 |
| Number | Date | Country | |
|---|---|---|---|
| 61118559 | Nov 2008 | US |
