1. Field
Subject matter disclosed herein relates to processing signals for use in positioning operations.
2. Information
Movement of objects transmitting a recognizable signal may be tracked by obtaining measurements of the transmitted signal at receivers maintained at known locations. In an implementation, a small tag comprising a transmitter may be fixed to a moving object to permit tracking the movement or location of the object over an area. For example, the small tag may broadcast a probe signal that includes a timing reference to enable an accurate determination of times of arrival at receivers.
Non-limiting and non-exhaustive examples will be described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various figures.
Briefly, particular implementations are directed to a method of determining a frequency offset between a carrier frequency and a receiver frequency on a device for coherent integration of an ultra-wideband (UWB) probe signal, comprising: receiving, at a receiver operating at the receiver frequency, a modulated UWB probe signal, comprising a message containing a known sequence of bits, said modulated wireless signals comprising the carrier frequency; demodulating the modulated UWB probe signal using the known sequence of bits; determining a corrected receiver frequency by iteratively computing a frequency offset between the corrected receiver frequency and the carrier frequency based on an autocorrelation of said demodulated signal; updating the receiver frequency based, at least in part, on the corrected receiver frequency.
Another particular implementation is directed to a device for coherent integration of an ultra-wideband (UWB) probe signal comprising: a radio frequency (RF) receiver to downconvert a modulated UWB probe signal comprising a carrier frequency according to a receiver frequency, the modulated UWB probe signal comprising a message containing a known sequence of bits; and a baseband processor to: demodulate the modulated UWB probe signal using the known sequence of bits; determine a corrected receiver frequency by iteratively computing a frequency offset between the corrected receiver frequency and the carrier frequency based on an autocorrelation of said demodulated signal; and update the receiver frequency based, at least in part, on the corrected receiver frequency.
A device for coherent integration of an ultra-wideband (UWB) probe signal comprising: means for receiving, at a receiver operating at the receiver frequency, a modulated UWB probe signal, comprising a message containing a known sequence of bits, said modulated UWB probe signal comprising a carrier frequency; means for demodulating the modulated UWB probe signal using the known sequence of bits; means for determining a corrected receiver frequency by iteratively computing a frequency offset between the corrected receiver frequency based on an autocorrelation of said demodulated signal; and means for updating the receiver frequency based, at least in part, on the corrected receiver frequency.
It should be understood that the aforementioned implementations are merely example implementations, and that claimed subject matter is not necessarily limited to any particular aspect of these example implementations.
As pointed out above, a low power tag placed on an object to be tracked may transmit a detectable probe signal that enables positioning and/or tracking movement of the object in an area or venue. In one particular implementation, receivers positioned at fixed locations may acquire a low power signal transmitted by a tag to, for example, detect times of arrival of the low power signal at the receivers. If the receivers are synchronized to a common clock, differences in times of arrival of the signal at the receivers may be used to compute an estimated location of the object. In one example implementation, a tag may transmit a low power ultra wideband (UWB) probe signal that is detectable by receivers positioned at known locations. To accurately detect a time of arrival of a UWB probe signal, a receiver may coherently integrate the received probe signal to detect the timing of a preamble comprising a known sequence of chips or symbols.
A UWB probe signal may be transmitted at a particular radio frequency (RF) carrier frequency. To downconvert a received probe signal, a receiver may comprise a complex mixer to mix the received probe signal with a sinusoid oscillating at a frequency that approximates the particular RF carrier frequency. The RF carrier frequency and the frequency of the sinusoid mixed with the received probe signal, however, may differ by an unknown frequency offset. According to an embodiment, precise estimate of this frequency offset, may be used to enhance performance of a coherent integrator used for detecting timing of a known bit sequence embedded in a received probe signal.
To acquire a known bit sequence embedded within a payload of a probe signal, a receiver may capture time-synchronized chip rate in-phase and quadrature samples of the known bit sequence in order to estimate the frequency offset. A typical Zigbee receiver, however, may not allow access to in-phase and quadrature samples. A baseband processing part of a Zigbee receiver may be expected to capture in-phase and quadrature samples from an analog-to digital converter (ADC) at twice the chip rate (e.g., 4.0 Msps), detect the arrival of a probe signal packet by detecting the preamble, acquire the time and the frame synchronization and capture the appropriate in-phase and quadrature samples and finally estimate a frequency offset from the captured in-phase and quadrature samples.
According to an embodiment, transmitter 202 may generate a PHY Protocol Data Unit (PPDU) packet as specified in the Zigbee specification. PPDU bits may then be grouped as quad-bits and mapped to symbols.
A particular example mapping of chip sequences of chip values to O-QPSK constellation points in illustrated in
It may be shown that a O-QPSK pulse-shaped according to expression (1) may approach a minimum shift keying (MSK) signal.
The RF signal received at antennae 302 may be transmitted at a particular carrier frequency fC. In particular implementations, it may be desirable to closely match fR with fC to enable sufficiently accurate decoding of a predetermined sequence of bits and accurate measurement of times of arrival of the signal received (e.g., predetermined sequence of bits in a PHY payload field of a received PPDU).
Block 804 may demodulate the modulated wireless signal using the known sequence. As pointed out above, a O-QPSK signal that has been pulse-shaped according to expression (1) may approach that of an MSK signal. As such, a baseband representation of such an MSK signal with a carrier offset may be expressed according to expression (2) as follows:
s(t)=Aej[2πΔft+θ(t)]+n(t) (2)
where:
An expression of s(t) may be re-written as follows:
s(t)=Aej[2πΔf+θ(0)+θ′(t)]+n(t)
where
It may be observed that at t=kTb, θ′(t) can take four different values: 0; +/−π/2; π; and ejθ′(t)=+/−1 or +/−j. Accordingly, given the transmitted sequence bk, sequence eθ′(kTb) is known and can be used to remove modulation from s(t). Removal of modulation from expression (1) may provide the following expression (3):
x
n
=Ae
j[2πnΔf/f
+θ]+νnn=1, 2, . . . N (3)
where:
fC=1/Tb (which is the carrier frequency of the received signal);
vn is discrete time complex Gaussian noise with a variance of No;
θ is a carrier phase; and
N is a length of the known sequence of bits (e.g., provided in a PHY payload of a PPDU packet).
According to an embodiment, block 806 may determine a corrected receiver frequency fR based, at least in part, on an autocorrelation of the demodulated signal obtained at block 804 and the known sequence. Such an autocorrelation may take the form of expression (4) as follows:
where:
m is a parameter defining a depth of a correlator.
An estimated normalized frequency offset may then be given according to expression (5) as follows:
While expression (5) contemplates correlators of different depths m for computation of a normalized frequency offset, correlators of all depths from one to mJ are not necessarily used. For example, m=m1, . . mJ may comprise less than the entire set of integers from one to mJ. As such, computation of a normalized frequency offset according to expression (5) may be adapted and/or optimized for performance and efficiency by varying different depths of correlators m (including non-consecutive integers mi).
According to an embodiment, computation of an estimated normalized frequency offset may be implemented as shown in the schematic diagram of
Regarding expression (5), a maximum value of mJ may be constrained by a requirement that |mJΔf/fC|<<½. It may be observed that a large value for mJ may provide performance approaching a Cramer-Rao bound given in expression (6) as follows:
According to an embodiment, a particular Zigbee receiver may be designed to operate with up to a 40 parts per million frequency offset that corresponds to a maximum frequency error Δf=96.0 kHz, which may limit a maximum value of mJ to about 5. On the other hand, for a value of N=4096, an estimate performance may begin to approach the aforementioned Cramer-Rao bound at about mJ=600 or larger. Accordingly, as discussed below, a multi-stage approach to estimating Δf may be employed.
In a particular implementation, a frequency offset of a received probe signal (e.g., in a PPDU message) may be estimated in multiple stages. Initial stages may use a relatively small value of mJ to provide larger acquisition range but lower accuracy. Input samples may be corrected with an estimated frequency offset computed from a previous stage. In a particular implementation, three stages may be sufficient to reach estimator performance approaching the above referenced CR bound with 40 PPM initial frequency offset. In a first stage, mJ=5 may be used to obtain=1 KHz. This allows mJ<=50 in the second stage which results in of about 100 Hz after second stage so that mJ=600 can be used in a final stage.
In an implementation, an estimated frequency offset Δfest may be computed in multiple stages using an iterative process. In a particular example, Δfest may be computed in multiple stages summarized as follows:
A final value for may be computed according to expression (7) as follows:
Δfest=Δfstage1+Δfstage2+Δfstage3. (7)
In another particular implementation, in computing Δfest, components Δfstagei may be weighted according to a filtering weight αi according to expression (8) as follows:
Δfest=α1Δfstage1+α2Δfstage2+α3Δfstage3. (8)
In the example above, components of Δfest are computed in three iterative stages. In other implementations, Δfest may be computed using more or less than three iterative stages. Furthermore, it may be observed that a range of indices m used in computing components of Δfest (e.g., Δfstage1, Δfstage2 and Δfstage3). As pointed out above, using different ranges of m enables tailoring computing resources to compute associated components of Δfest with sufficient precision and without unnecessary use of the computational resources. In the above example, a range of m=3 to 5 is employed for computing Δfstage1 at a first stage, a range of m=46 to 50 is employed for computing Δfstage2 at a second stage and a range of m=900 to 1000 is employed for computing Δfstage3 at a third stage.
In particular implementations, any one of several values of a threshold may be applied at diamond 1006. For example, one threshold may be set at 40.0 ppm. Another threshold may be set at 10.0 Hz, 1.0 Hz, 0.1 Hz, or any other frequency value without deviating from claimed subject matter. Another threshold may be set at or approximately at three times a computed Cramer-Rao bound (e.g., according to expression (6)). In a particular implementation as shown in the plot of signal-to-noise ratio (SNR) and Cramer-Rao bound at
As pointed out above in connection with expression (8) in a particular implementation, values for an iteration of Δfstage may weighted by a coefficient a at block 1008 for rotating samples of xn. Likewise, at block 1010 iterations of Δfstagei may be weighted by coefficients αi according to a filter model.
In particular implementations of block 1004, ranges for values of mε{m1, . . . , jJ} applied to expressions (4) and (5) for computing iterations of Δfstage may be varied as described in the particular example described above. For example, smaller ranges of m indices may be applied in earlier iterations for obtaining coarser components of Δfest while larger ranges of m indices may be applied in later iterations for obtaining finer components of Δfest.
As used herein, the term “mobile device” refers to a device that may from time to time have a position location that changes. The changes in position location may comprise changes to direction, distance, orientation, etc., as a few examples. In particular examples, a mobile device may comprise a cellular telephone, wireless communication device, user equipment, laptop computer, other personal communication system (PCS) device, personal digital assistant (PDA), personal audio device (PAD), portable navigational device, and/or other portable communication devices. A mobile device may also comprise a processor and/or computing platform adapted to perform functions controlled by machine-readable instructions.
The methodologies described herein may be implemented by various means depending upon applications according to particular examples. For example, such methodologies may be implemented in hardware, firmware, software, or combinations thereof. In a hardware implementation, for example, a processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other devices units designed to perform the functions described herein, or combinations thereof.
“Instructions” as referred to herein relate to expressions which represent one or more logical operations. For example, instructions may be “machine-readable” by being interpretable by a machine for executing one or more operations on one or more data objects. However, this is merely an example of instructions and claimed subject matter is not limited in this respect. In another example, instructions as referred to herein may relate to encoded commands which are executable by a processing circuit having a command set which includes the encoded commands. Such an instruction may be encoded in the form of a machine language understood by the processing circuit. Again, these are merely examples of an instruction and claimed subject matter is not limited in this respect.
“Storage medium” as referred to herein relates to media capable of maintaining expressions which are perceivable by one or more machines. For example, a storage medium may comprise one or more storage devices for storing machine-readable instructions or information. Such storage devices may comprise any one of several media types including, for example, magnetic, optical or semiconductor storage media. Such storage devices may also comprise any type of long term, short term, volatile or non-volatile memory devices. However, these are merely examples of a storage medium, and claimed subject matter is not limited in these respects.
Some portions of the detailed description included herein are presented in terms of algorithms or symbolic representations of operations on binary digital signals stored within a memory of a specific apparatus or special purpose computing device or platform. In the context of this particular specification, the term specific apparatus or the like includes a general purpose computer once it is programmed to perform particular operations pursuant to instructions from program software. Algorithmic descriptions or symbolic representations are examples of techniques used by those of ordinary skill in the signal processing or related arts to convey the substance of their work to others skilled in the art. An algorithm is here, and generally, is considered to be a self-consistent sequence of operations or similar signal processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals, or the like. It should be understood, however, that all of these or similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the discussion herein, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic computing device. In the context of this specification, therefore, a special purpose computer or a similar special purpose electronic computing device is capable of manipulating or transforming signals, typically represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic computing device.
Wireless communication techniques described herein may be in connection with various wireless communications networks such as a wireless wide area network (WWAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), and so on. The term “network” and “system” may be used interchangeably herein. A WWAN may be a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an Orthogonal Frequency Division Multiple Access (OFDMA) network, a Single-Carrier Frequency Division Multiple Access (SC-FDMA) network, or any combination of the above networks, and so on. A CDMA network may implement one or more radio access technologies (RATs) such as cdma2000, Wideband-CDMA (W-CDMA), to name just a few radio technologies. Here, cdma2000 may include technologies implemented according to IS-95, IS-2000, and IS-856 standards. A TDMA network may implement Global System for Mobile Communications (GSM), Digital Advanced Mobile Phone System (D-AMPS), or some other RAT. GSM and W-CDMA are described in documents from a consortium named “3rd Generation Partnership Project” (3GPP). Cdma2000 is described in documents from a consortium named “3rd Generation Partnership Project 2” (3GPP2). 3GPP and 3GPP2 documents are publicly available. 4G Long Term Evolution (LTE) communications networks may also be implemented in accordance with claimed subject matter, in an aspect. A WLAN may comprise an IEEE 802.11x network, and a WPAN may comprise a Bluetooth or Zigbee network, an IEEE 802.15x network, for example. Wireless communication implementations described herein may also be used in connection with any combination of WWAN, WLAN or WPAN.
The terms, “and,” and “or” as used herein may include a variety of meanings that will depend at least in part upon the context in which it is used. Typically, “or” if used to associate a list, such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. Reference throughout this specification to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of claimed subject matter. Thus, the appearances of the phrase “in one example” or “an example” in various places throughout this specification are not necessarily all referring to the same example. Furthermore, the particular features, structures, or characteristics may be combined in one or more examples. Examples described herein may include machines, devices, engines, or apparatuses that operate using digital signals. Such signals may comprise electronic signals, optical signals, electromagnetic signals, or any form of energy that provides information between locations.
While there has been illustrated and described what are presently considered to be example features, it will be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from claimed subject matter. Additionally, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the central concept described herein. Therefore, it is intended that claimed subject matter not be limited to the particular examples disclosed, but that such claimed subject matter may also include all aspects falling within the scope of the appended claims, and equivalents thereof.