WEAKLY-COUPLED QUASISTATIC MAGNETIC FIELDS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to US Provisional Patent Application No. 62/597,102, filed on December 11, 2017, the disclosure of which is incorporated herein by reference in its entirety.

STATEMENT OF INTEREST

[0002] The invention described herein was made in the performance of work under a NASA contract NNN12AA01C, and is subject to the provisions of Public Law 96-517 (35 USC 202) in which the Contractor has elected to retain title.

TECHNICAL FIELD

[0003] The present disclosure relates to kinematic sensing. More particularly, it relates to wireless vector kinematic sensing via weakly-coupled magnetic fields.

BRIEF DESCRIPTION OF DRAWINGS

[0004] The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate one or more embodiments of the present disclosure and, together with the description of example embodiments, serve to explain the principles and implementations of the disclosure.

[0005] Fig. 1 illustrates an exemplary transmitter and receiver.

[0006] Fig. 2 illustrates a block diagram of the MQS inversion algorithm.

[0007] Fig. 3 illustrates measurements for a static and moving sensor. [0008] Fig. 4 illustrates measurements of F _r inverted to obtain range, velocity, and acceleration along the y-axis.

[0009] Fig. 5 illustrates inverted results for 2D position and orientation.

[0010] Fig. 6 illustrates inverted results for linear and angular velocity and acceleration.

SUMMARY

[0011] In a first aspect of the disclosure, a method is described, the method comprising: providing a transmitter configured to transmit quasistatic magnetic fields in one or more transmitter axes; providing a receiver configured to receive quasistatic magnetic fields in one or more receiver axes; transmitting, by the transmitter, the quasistatic magnetic fields in one or more frequency bands; detecting, by the receiver, one or more quasistatic magnetic field couplings between each axis of the one or more transmitter axes and each axis of the one or more receiver axes; calculating a sum function by adding power measurements of all of the one or more quasistatic magnetic field couplings; calculating a ratio function by taking a ratio between all of the power measurements of the one or more quasistatic magnetic field couplings; inverting the sum function and the ratio function by a direct linear inversion calculation; calculating, from the inverted sum function, a drift-free orientation-invariant range between the transmitter and the receiver; calculating, from the inverted ratio function, a drift-free range-invariant orientation between the transmitter and the receiver; calculating a drift-free position of the transmitter or of the receiver, based on the drift-free orientation-invariant range and on the drift-free range- invariant orientation; applying a filter to the drift-free orientation-invariant range, to the drift-free range-invariant orientation, and to the drift-free position, to reduce white noise, thereby obtaining a filtered drift-free range, a filtered drift-free orientation, and a filtered drift-free position; calculating a linear velocity by taking a derivative of the filtered drift-free position; calculating a linear acceleration by taking a derivative of the linear velocity; calculating an angular velocity by taking a derivative of the filtered drift-free orientation; and calculating an angular acceleration by taking a derivative of the angular velocity, wherein: a center frequency of each of the one or more frequency bands corresponds to a wavelength L, and a range between the transmitter and the receiver r is: r < l/2tt. DETAILED DESCRIPTION

[0012] The present disclosure describes drift-free wireless kinematic sensing of position, orientation, linear and angular velocity and acceleration via measurements of quasi- static magnetic fields.

[0013] The development of modern mobile electronic devices (e.g., cellphones, game devices, tablet computers) has placed ongoing demands on wireless kinematic and navigation sensors. These sensors can be used for several applications (e.g., search and rescue, kinesiology, virtual reality, surgery), which require different types of connectivity (e.g., local navigation, motion sensing). Modern kinematic sensors such as accelerometers, gyroscopes, magnetic tracking, infrared and optical sensors, acoustic sensors, and camera-based imaging still do not provide complete drift-free kinematics of a body or object in non-line-of-sight (NLoS) environments.

[0014] Accelerometers, gyroscopes, and magnetic tracking sensors do not require line-of-sight and have use in many applications. Modern accelerometers and gyroscopes based on microelectromechanical systems (MEMS) can be advantageously fused to enable gravity- removed acceleration and orientation. However, these sensors cannot provide drift-free measurements of vector velocity, position, or orientation. Magnetic tracking devices use very low frequencies to avoid ground effects, which limit their use to: a distance of only a few meters, low position and orientation update rates, and no measurements of vector linear or angular velocity and acceleration due to their low signal-to-noise (SNR) rate and update rate. Magnetoquasistatic (MQS) tracking techniques use frequencies of up to a few hundred kiloHertz, which still places them within the magnetoquasistatic regime. The MQS regime implies a distance between the sensor and the object to be sensed much smaller than the wavelength of the magnetoquasistatic field: r « l. MQS tracking techniques enable sensing at up to hundreds of meters away, with high update rates, by correcting for Eddy currents in nearby conductors or the ground, as described in Refs. [6, 7] MQS tracking has been demonstrated to have a long range in through-the-wall indoor environments (Ref. [8]), highlighting its applicability in heavy multi- path NLoS environments. [0015] By measuring position and orientation of an object with high SNR and update rates, MQS tracking systems can permit low noise derivates via an optimal filter, such as the Kalman filter (see Ref. [9]), additionally providing the drift-free vectors of linear and angular velocity and acceleration. As known to the person of ordinary skill in the art, Kalman filtering, also known as linear quadratic estimation, is an algorithm based on a series of measurements taken over time, containing statistical noise and other inaccuracies. A Kalman filter produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution for the variables for each timeframe. The MQS field generation carried out by a digitally synthesized signal provides high a signal to noise ratio, so that the noise in the channel is limited to the receiver noise. MQS systems described in the present disclosure are operated in an interference-free narrow band, and at a frequency sufficiently high to neglect l/f noise. The high frequency requirement is also derived by Faraday’s law which imposes a signal-to-noise requirement, due to V = 2 nj x / ₀ x B, where V is the voltage sensed, B is the magnetic field, and / ₀ is the frequency. For useful ranges greater than 3-5 m, given regulatory requirements (by the FCC) for transmitted power, the effective MQS lower frequency ranges are in the 5-10 kHz range.

[0016] The MQS system is derived by inductive coupling, with the two-sided bandwidth, B , defined as B = f ₀ /Q , where the / ₀ is the center frequency, and Q is the quality factor of the inductive antenna element of the MQS system. MQS systems require quasistatic operation such as that range to device is r < l ₀ /2p, where r is the range, and l ₀ is the center wavelength. Therefore, l ₀ > 2 nr. Using the speed of light conversion of wavelength to frequency, this expression relates to / ₀ < c/2nr , where c is the speed of light in free space. For ranges of about 100 m, this corresponds to center frequencies less than 500 kHz. The quality factor of the inductive antenna system is given by Q = 2nf ₀ L/R , where L is the inductance and R is the resistance. For most systems with strong inductances in the >1 mH ranges, and low resistances in the <2W range, this corresponds to a quality factor Q>l500. Therefore, the bandwidthis B<400 Hz, and can be as low as 100 Hz. This is a very narrow bandwidth, and thus a center frequency /o can be carefully selected such that this small bandwidth is not being interfered by external or other sources, i.e., giving an interference-free channel. [0017] MQS systems are also operated with high gain receiver amplification— therefore Gaussian thermal noise dominates. Filters such as Kalman filters can be used to estimate the noise contributions in MQS systems since the noise and signal variances are Gaussian in nature. Filters can be used to remove noise contributions to obtain signaling void of noise. This process is key when computation of higher order derivate of position and orientation are needed to obtain error-free linear and angular, velocity and acceleration.

[0018] Multiple types of filters can be used for this purpose, which includes electronic filters (having passive or active components), mechanical filters (transducers or mechanical components), distributed element filters (discreet lumped elements), or digital filters (digital filters implemented using mathematical algorithms). The use of digital filters provides the most flexibility as these can tuned to the noise distribution of the data. Most digital filters are based on Fourier transforms, including low-pass filters, high-pass filters, and band-pass filters. These are useful when precise knowledge of the noise distribution is not known. Another form of a digital filter uses a state-space model. An established state-space filter is the Kalman filter. The Kalman filter is an optimal filter when: a) the model matches the real system, b) the entering noise is white (Gaussian, for example) and c) the co-variances of the noise are exactly known.

[0019] Fig. 1 illustrates, in panel a, an exemplary two-dimensional (2D) system to sense the vector kinematics of a mobile MQS transmitter (TX, 105) device relative to a fixed receiver (RX, 110) device. In panel b, Fig. 1 illustrates a block diagram of a single axis of the TX and RX system of panel a. In Fig. 1, the vector components of the magnetoquasistatic fields are illustrated for both transmitter and receiver, along with the angles between the vector components. Panel b of Fig. 1 illustrates several electronic components that will be known to the person of ordinary skill in the art, such as a digitally synthesized signal (DDS) source, amplifiers, matching module, coils, and analog-to-digital converters (ADC). The system utilizes Fast Fourier transforms (FFT).

[0020] The present disclosure describes the kinematic sensing of a mobile transmitting MQS device relative to a fixed receiving MQS device in a lossy dielectric NLoS medium. The voltage sensed by a coil due to a MQS dipole field up to a few skin-depths away is given by V = - ]w[B ]a' (see Ref. [8]):

[0021] where a' is the coil’s surface area, n is the normal unit vector, B the magnetic field, w the radial frequency, f = rr the dipole range vector, m the magnetic moment with |m| = 1 Am ² , K = 2/( _< 5(l— _/)), 5 the bulk depth of penetration, a the depth constant (see Ref. [8]), and S denotes the signal with no noise.

[0022] Fig. 1 panel a focuses on the two-dimensional (2D), x, y, ø, 3 degrees of freedom (DOF) problem, which requires two orthogonal dipoles at the transmitter (TX) and receiver (RX) (2 x 2 = 4 field couplings). To permit linear sensitivity to position and orientation, the field couplings need to be rewritten in transmit-centric and receive-centric forms (see Ref. [6]):

[0023] where ^S F _mi and ^s F _m2 are the transmit-centric equations for transmitter TX 1 and 2 (ml, m2), which describe the total field power measured at all receivers RX(s) due to each TX, and ^S F _{n i} and ^s F _n2 are the receive-centric equations for receivers RX 1 and 2 (nl,n2), which describe the total power of all transmitters TX(s) measured at each receiver RX (see Ref. [6]). The method to obtain Eqs. (2) is summarized in Fig. 2, where P _{m n} (205) is the power of each of the 2x2 field couplings for the transmitters and receivers as described above. Fig. 2 illustrates a block diagram of the MQS inversion algorithm to obtain vector kinematics of a mobile device. [0024] The system for each channel is illustrated in Fig. 1 panel b, for the transmitter side (115) and the receiver side (120). The TX tone generation carried out by a digitally synthesized signal (DDS,l25) provides high SNR, so that the noise in the channel is limited to the RX noise. MQS systems are operated in an interference-free narrow band, and at a frequency sufficiently high to neglect l/f noise. MQS system are also operated with high gain receiver amplification— therefore Gaussian thermal noise, ^N V , dominates. The received voltage can therefore be considered as V V+ ^N V. The F _{m n} terms in Eqs. (2) are power-related by \V\ ² = VV ^* . Due to the quasistatic operation ( kr « 1), the phase is negligible so that:

[0025] From Eqs. (2), assuming equal thermal noise in each channel to simplify the derivation, it is possible to obtain: ,

(4)

[0026] where ^N P is the thermal noise power. To obtain ranging that is orientation invariant, the contributions from both transmit centric equations are summed, as can been seen from Ref. [8] and (210) in Fig. 2:

[0027] where c =—]wm ₀ a'/(4p). To obtain orientation sensing that is invariant of range, a ratio is applied instead, as shown (215) in Fig. 2. However, here the orientations are either due to orientations of the mobile device (0 _m ), or direction angles which are relative to the fixed device orientations (<p _n ) (see Fig. 1 panel a). The Taylor series of a ratio A/B with added ^N P to A and B about ^N P~ 0 is: [0028] which gives the m orientation ratio due to _m , F^ _m as: Similarly, it is possible to rewrite the noise-added

[0030] where C _n = (¾-¾)/¾ _· C _m is an orientation-modulated constant, while C _n is direction-modulated. Equations (5), (7), and (8) show that the noise model is maintained as zero- mean additive Gaussian.

[0031] The MQS technique relies on the ability to invert Eqs. (5), (7), and (8) through a direct linear inversion method (see Ref. [6]). The noise-free has the form and inversion given by

Ref. [6] as:

[0032] The Taylor series of the inverse orientation functions in Eq. (9), with an arbitrary forward signal, F, which has the form of Eqs. (7) and (8) with added noise, ^N P , taken about ^W P~ 0 is given by:

[0033] where is an ⁵ F-modulated constant. Based on Eq. (10), it is possible to derive: ion angle to the device (see Ref. [7]) is given by:

[0036] The range function, F _r , given in Eq. (5) has not been directly inverted previously, but can be found using a Lambert W function (see Ref. [11]):

[0037] where W is the Lambert W function, the 2 x ^w P factor and d = 5c ² are obtained from Eq. (5), and ^N r is the noise-influenced inverted range. As known to the person of ordinary skill in the art, the Lambert W function is a set of functions comprising the branches of the inverse relation of the function f(z) = ze ^z , where e ^z is the exponential function, and z is any complex number. Taking the Taylor series expansion of ^N r in Eq. (13) about ^W P~ 0 gives:

[0038] C _r = ⁵ r/[2F _r x (3+ ⁵ r x ak )] is a range-modulated constant, and ^s r is the noise-free inverted range given by:

[0039] Equations (11), (12), and (14), noted in Fig. 2 as (220) with (x, y) = T _xy [r, f _h \ and f = Tf [f _h> F _th \ (T denotes transformations), provide the necessary framework to obtain the complete drift free position and orientation of the mobile device in the presence of noise: X = rcos <p _n = [ ^s rcos ^s p _n ]+ ^N P x C _x + 0( ^N P ² )

y = rsin0 _n = [ ^s rsin ^s f _h \+ ^N P x C _y + 0( ^N P ² ) (16)

F = 0m - Fh = [ ⁵ F ^~5 F \+ ^N R X ¾ + 0{ ^N P ²

[0040] where (x, y) gives the position of the device, f gives the orientation of the device, the terms in square brackets denote the noise-free signaling, C _x = C _r cos ⁵ 0 _n — Cp _n ⁵ rsin ⁵ 0 _n , C _y =

[0041] The technique described above is simplified to two dimensions (2D). The simplification is to show a proof-of-concept, and is enabled by use of 2-axis orthogonal transmitters and measurements by a 2-axis orthogonal receiver system. The position information noted as x, y locations in Eq. (16) are derived through the range vector (r) and direction angle (0 _n ). The direction angle, f _h, is derived through the ratio function given by Ef _h in Eq. (8), which is the ratio between all signal powers of the 2-axis orthogonal receivers. <p _n is therefore the direction angle along the x-y plane or about the z-axis, as defined through the spherical coordinate system typically as the azimuthal angle. To determine three-dimensional (3D) direction, an additional polar or zenith angle is needed which is the angle between the z-axis (or vertical axis) and the direction to the device. This angle can be found by taking the power ratio between the all components summed of the z-axis of the receiver to the combined sum of both horizontal components so that:

^G P3

^F 0n ^~ (17)

F _{7 t} i + F„ where _n3 is the z-axis counterpart of the receiver-centric component in Eq. (2) for the 3D problem when the transmitter and receiver are both 3 -axis devices. The inversion for q _h follows the same form given in Eq. (12), which results in the polar or zenith direction angle. For range in 3D, the F _r formulation in Eq. (5) must also be updated to include the z-axis counterpart of Eq. (2) for the 3D problem when the transmitter and receiver are both 3-axis devices:

^F r - Pml + ^F m2 + ^F m3 (l ⁸ ) where F _m3 is the z-axis counterpart of transmitter-centric component in Eq. (2) for the 3D problem when the transmitter and receiver are both 3 -axis devices. The inversion for F _r follows the same form given in Eq. (15), which results in the 3D range. The 3D range to the device along with both azimuthal and polar or zenith direction angles can be used to find the 3D location using the normal Spherical coordinate conversions. 3D orientation of the device follows a similar approach to that given above for direction angles, however here ratios of F _m functions are used instead. The inversions for orientation of the device are identical to that in Eq. (11).

[0042] Accelerometers cannot provide drift-free velocity or position sensing due to single and double integration operations. This problem is eliminated if derivatives are used to obtain linear and angular velocity and acceleration from the position and orientation via MQS coupling. However, the signaling given by Eq. (16) is noisy. Differentiation of (16) will amplify noise, and this amplification would grow with the order of the derivatives (see Ref. [12]). Nevertheless, the inversions in Eq. (16) present Gaussian additive noise, thus an optimal Kalman filter (see Ref.

[9]) can be used to reduce or remove noise, and estimate the drift-free and noise-free linear and angular velocity and acceleration.

[0043] The Kalman mechanics is defined by the predicted time-update and measurement-update equations at discreet step k. The state transition for the kinematic problem considered, usually denoted by F, is well known (see Ref. [13]). The Kalman observation model, H , to indicate measurement of position and orientation only (no measurements of derivates), as well as measurement noise covariance, R , is derived from the MQS coupling in Eq. (16) for use in the Kalman mechanics:

[0044] where FI reflects position/orientation. The measurement noise covariance, R _k is determined by measurement of the radio frequency (RF) systems noise variance, and updated based on C _x /C _y /Cp for x/y/f at (k— 1). The initial position and orientation are known, therefore the initial estimated covariance in the Kalman mechanics is a 3x3 array of zeros, while the process noise co-variance, usually denoted by Q , is found by numerical tuning (see Ref.

[14]) _·

[0045] The x/y/f measurements in Eq. (16) have orthogonal basis, so the Kalman filter (KF) mechanics is applied to x, y, and f at each discreet time step k , as illustrated in Fig. 2 (225). For x, y, and f from Eq. (16), the KF mechanics removes the Gaussian noise optimally (see Ref.

[9]) to give x, y, and , as shown in Fig. 2 (230), which are noise free ( S denotes signaling that is noise free) and includes the first and second derivatives:

[0046] The system used to measure the couplings consisted of a mobile transmitter (TX) and a wide-band receiver (RX) system with 3 orthogonal axes. Each channel of the TX and RX is configured as shown in the block diagram in Fig. 1 panel b. The resonant frequencies of the inductances of the TX coils were tuned, using capacitors, at about 100 kHz. The resonant frequencies for each TX axis were kept at a unique frequency (with about 5 kHz separation), whereas the RX coils were left wideband through a high impedance match. A digitally synthesized signal (DDS,l25) source was used to generate the TX tones which are amplified via a high voltage FET (field effect transistor) amplifier and fed to the TX resonant coils (130). The tones received in each RX coil (135) were collected using a 2 MS/s analog-to-digital converter (ADC). Fast Fourier transform (FFT) and peak-detect algorithms were applied to a 10 ms window at each time step, k, to obtain the 3x3 coupled tones. The z axis contributions (from both TX and RX) were discarded in this exemplary experiment to limit the study to the 2D 2x2 problem. Additional details of the system can be found in Ref. [8]

[0047] For the first experiment, the RX was fixed and the mobile TX was moved along the positive y axis (as in Fig. 1 panel a) from 2-10 m over a duration of about 240 s in steps, with stops also at approximately 6.5 m and 8 m, then moved closer to a distance of about 9 m after an additional pause of 30 s. The measured total field power summed from all axes (summation of both TX tones measured in both RX coils), F _r , is described by Eq. (5), and shown in Fig. 3. Fig. 3 illustrates measurements of F _r for a range of motion of transmitter sensor (TX) between 2 and 10 m from the receiver (RX). In Fig. 3, the down arrows (305) show increasing range, while the up arrow (310) show decreasing range. The initial part of the graph is for the static sensor (315), while the remaining part of the graph is for the sensor in motion (320).

[0048] Fig. 4 illustrates measurements of F _r inverted to obtain range, velocity, and acceleration along the y axis. In Fig. 4, the data is compared to a co-located tactical-grade MEMS inertial measurement unit (IMU). Fig. 4 illustrates MEMS data (405,415,425) and MQS data (410,420,430).

[0049] The flat lines in Fig. 4, e.g. (435), show periods where the mobile sensor remains stationary. To obtain the range, the measured F _r is inverted to solve for range using Eq. (15). For this proof-of-concept experiments, the data was collected in free space, where ak ® 0, and Eq. (15) simplifies to ^s r = (d/F _j .) ¹ / ⁶ , as described in Ref. [11] For further simplification, and to study ranging and its derivatives, <p _n = 90° was assumed in this experiment, so that y = ⁵ r+ ^N P X C _r. The noise influence is Gaussian, thus the Kalman mechanics in Eqs. (17) and (18) can be used to find the y-directed, noise-free range (as illustrated in Fig. 5 panel a), and its higher order derivatives, velocity (as illustrated in Fig. 5 panel b) and acceleration (as illustrated in Fig. 5 panel c).

[0050] A tactical grade, 6 DOF, inertial measurement unit (IMU), as described in Ref. [15], was also used in the mobile system as a comparison. The acceleration, velocity, and range shown in Fig. 5 use state-of-the-art IMU algorithms, as described in Ref. [4] It can be seen from Fig. 5 that the acceleration data from the IMU is heavily influenced by noise, which causes its velocity and position estimation to drift (see Ref. [1]). It is well known to the person of ordinary skill in the art that position errors from an IMU, as shown in Fig. 4, grow quadratically with time. The increase in the errors is due to sensitive bias instabilities, noise, temperature effects, and calibration effects (see Ref. [16]) that cannot be fully corrected, as described in Ref. [1] By comparison, the MQS technique described in the present disclosure senses position and orientation with Gaussian noise. The Gaussian noise is removed with a Kalman filter to provide derivatives and higher order derivatives, thus enabling linear and angular velocity and acceleration sensing that is also noise-free and drift-free.

[0051] Fig. 5 illustrates inverted results for the 2D position and orientation for a translation on the y axis, in panel a. Fig. 5 also illustrates inverted results for the 2D position and orientation for a translation on the x axis, in panel b; and for a rotation and counter-rotation along the z axis, in panel c. In Fig. 5, data is shown for both x position (505) and y position (510).

[0052] Fig. 6 illustrates inverted results for linear and angular velocity (601) and acceleration (602) for translation measurements on the y axis (panel a), x axis (panel b), as well as rotation and counter-rotation along the z axis (panel c).

[0053] To study the full 2D kinematics, the mobile sensor was translated and rotated in three separate experiments. The first experiment was a translation in the positive y axis between 3-5 m for x = 0 m. In the second experiment, the device was moved along the x direction from 0-3 m for y = 5 m. In the third experiment, the device was placed back on the positive y axis at (x,y) = (0,3) m, then rotated to f _pi = 90°, and then back to f _pi = 0°. To solve for the full 2D kinematics, the range solutions were determined using Eq. (15), and f _pi and f _h were determined using Eqs. (11) and (12), and Fp _n are found by taking the ratios of the TX-centric summed power measurements and RX-centric summed power measurements, respectively, as noted in Eqs. (7) and (8) (see Fig. 2). Fig. 5 shows the inverted results for each experiment using Eq. (16). Applying the KF mechanics in Eqs. (17)-(18), the linear and angular velocity and acceleration are found as shown in Fig. 6 (in addition to the position and orientation in Fig. 5).

[0054] As described in the present disclosure, drift-free wireless kinematic sensing can be achieved using quasi-static magnetic fields. Since quasi-static magnetic fields are undisturbed by nearby dielectrics, the technique is effective in various non-line-of-sight applications. In some embodiments, the frequency of the quasistatic magnetic fields is less than 500 kHz.

[0055] The calculation of combined position, orientation, and linear (spatial) and angular (orientation) velocity and acceleration is described in the present disclosure. The Kalman filter optimally removes the noise so that higher order derivatives of the linear and angular components can be calculated. However, in some embodiments other filters can be used as well. Since MQS noise is largely a Gaussian process, and because the systems model and noise covariance are known, using a Kalman filter is optimal.

[0056] In some embodiments, the following steps are taken to calculate position, orientation, and linear (spatial) and angular (orientation) velocity and acceleration of an object. Magneto- quasistatic (MQS) fields are generated using a magnetic antenna (for example a coil antenna). The antenna or exciter is capable of generating tri-axial orthogonal MQS fields. Each orthogonal field can be uniquely identified by an observer (receiver) and does not interfere with other fields. Observation ranges are within the quasistatic range (distance « wavelength). All unique MQS fields are detected using a magnetic antenna (for example, a coil antenna). A tri-axis detector can be used so that each component senses all three fields uniquely. In other words, the receiver detects 3x3 = 9 unique quantities.

[0057] It is then possible to solve for position and orientation, as described in the present disclosure with regard to the field couplings and inversions. The position and orientation will have additive Gaussian noise in MQS systems, due to interference-free operation that can be accomplished via very narrow-band MQS signaling. Narrow-band signaling is required by MQS signaling due to the size of structures relative to long wavelengths and the technique being used to measure MQS fields. A filter can be used to remove additive Gaussian noise. Different filters can be used, however the Kalman filter is the optimal filter. This is due to the noise being Gaussian additive, the system model is shown to be correct and matches measurements, and the noise covariances are known as shown in the present disclosure.

[0058] It is then possible to calculate derivatives and higher order derivatives either using Kalman mechanics directly (Kalman mechanics can be used to estimate higher order derivates) or by taking derivatives of the measured quantities after the filters are applied to remove noise. The estimated quantities are position and orientation in 3D space with noise removed, and linear (spatial , e.g. x,y,z) velocity and acceleration, and angular (orientation, e.g., theta and phi) velocity and acceleration. In some embodiments, each transmitter axis is composed of an arbitrarily shaped (circular, square, oval, etc.) single or multi-turn coil of wire, where the open ends of the wire are driven by a harmonic source where the excitation of the source varies sinusoidally in time with a single frequency, generating a quasistatic magnetic field for ranges, r, of G<l/2p, from the transmitter. For two and three axes systems, each axis is configured orthogonally to each other so that the coil surface normal unit vector of each coil is perpendicular to the others. In some embodiments, a power measurement is taken for each magnetoquasistatic field coupling. The power measurement is calibrated to remove any unknown gain or losses in the measurement. Due to the fact that the coil or antenna is not perfect or ideal, there can be gains or losses in the measurement. These gains or losses are removed though calibration. As known to the person of ordinary skill in the art, taking derivatives to obtain the velocity and acceleration normally is not possible in this type of sensors because of the noise in the range, orientation and position. By the methods of the present disclosure, it is possible to obtain noise- free quantities, or at the very least with a greatly reduced noise. Taking the derivatives to calculate velocity and acceleration now becomes possible.

[0059] In some embodiments, taking a Cartesian frame of reference with the origin on one device, and the z axis parallel to the orientation of this device. A vector (with 3 Cartesian components) points towards the second device. The length of this vector is the range. The projected angles of this vector with respect to the Cartesian axes give the direction angles. Depending on whether one, two, or three quasistatic fields are used, the direction angles may be one, two or three angles. The second device has its own coil, two coils, or three orthogonal coils. The axis of the one coil for the 1D implementation may be at an angle with respect to the z axis of the Cartesian frame of reference. Similarly, for the 3 orthogonal coils, they also may be at an angle with respect to the frame of reference and to the first device. This orientation angle or angles give the orientation or attitude of the second device.

[0060] The examples set forth above are provided to those of ordinary skill in the art as a complete disclosure and description of how to make and use the embodiments of the disclosure, and are not intended to limit the scope of what the inventor/inventors regard as their disclosure.

[0061] Modifications of the above-described modes for carrying out the methods and systems herein disclosed that are obvious to persons of skill in the art are intended to be within the scope of the following claims. All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the disclosure pertains. All references cited in this disclosure are incorporated by reference to the same extent as if each reference had been incorporated by reference in its entirety individually.

[0062] It is to be understood that the disclosure is not limited to particular methods or systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. The term“plurality” includes two or more referents unless the content clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure pertains.

[0063] The references in the present application, shown in the reference list below, are incorporated herein by reference in their entirety.

References

[1] G. Welch and E. Foxlin. Motion tracking: no silver bullet, but a respectable arsenal. IEEE Computer Graphics and Applications, 22(6): 24-38, Nov 2002.

[2] A. Savvides T. Teixeira, G. Dublon. A survey of human- sensing: Methods for detecting presence, count, location, track, and identity. ENALAB, Yale University, Tech. Rep. 09-2010, Sep. 2010.

[3] J. L. Marins, Xiaoping Yun, E. R. Bachmann, R. B. McGhee, and M. J. Zyda. An extended kalman filter for quaternion-based orientation estimation using marg sensors. In Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180), pages 2003-2011 vol.4, 2001. [4] S. O. H. Madgwick, A. J. L. Harrison, and R. Vaidyanathan. Estimation of imu and marg orientation using a gradient descent algorithm. In 2011 IEEE International Conference on Rehabilitation Robotics, pages 1-7, June 2011.

[5] F. Raab, E. Blood, Steiner T., and Jones H. Magnetic Position and Orientation Tracking System. IEEE Trans on Aerospace and Electrical Systems , AES-l5(5):709-7l8, 1979.

[6] D.D. Arumugam. Decoupled range and orientation sensing in long-range magnetoquasistatic positioning. Antennas and Wireless Propagation Letters, IEEE , 14:654-657, 2015.

[7] D. D. Arumugam. Single-anchor 2-d magnetoquasistatic position sensing for short to long ranges above ground. IEEE Antennas and Wireless Propagation Letters , 15 : 1325-1328, 2016.

[8] D. D. Arumugam. Through-the-wall magnetoquasistatic ranging. IEEE Antennas and Wireless Propagation Letters, PP(99): l-l, 2016.

[9] R. E. Kalman. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(l):35-45, 03 1960.

[10] C. E. Calosso, Y. Gruson, and E. Rubiola. Phase noise and amplitude noise in DDS. In 2012 IEEE International Frequency Control Symposium Proceedings, pages 1-6, May 2012.

[11] L. Euler. De serie lambertina plurimisque eius insignibus proprietatibus. Acta Acad. Scient. Petropol, 2:29-51, 1783.

[12] R. S. Anderssen and P. Bloomfield. Numerical differentiation procedures for non-exact data. Numerische Mathematik, 22(3) : 157-182, 1974.

[13] Robert Grover Brown and Patrick Y. C. Hwang. Introduction to random signals and applied Kalman filtering. 3. Wiley, 1997.

[14] M. Nilsson. Kalman filtering with unknown noise covariances. Proc. Reglemente, KTH Stockholm, Sweden, pages 1-4, May 2006. [15] Analog Devices. Adis 16497. http://www.analog.com/en/products/mems/inertial- measurement-units/ adis 16497. html.

[16] Oliver Woodman. An introduction to inertial navigation. Technical Report, University of Cambridge, Tech. Rep. 696 , Aug. 2007.