Apparatus and method for measuring the accurate position of moving objects in an indoor environment

[0001] The instant invention relates to an apparatus and method for measuring the accurate position of moving objects in an indoor environment.

[0002] Location sensing and tracking of moving objects has created a growing interest in numerous novel location based services and applications in various market segments. In the retail industry, for example, shopping carts equipped with personal shopping assistants enriched with some additional navigation functionality can guide customers through the store, provide them with location-based product information, and alert them to promotions and discounts as they walk through the aisles. However, customers will be satisfied only if this advanced shopping service can offer a tracking system which will accurately sense the location of the shopping cart. Determining at least the aisle in which the cart is located is crucial for obtaining customer satisfaction. The stringent requirement for estimating the location of the cart with an accuracy of less than one meter is a challenging situation. In addition, to make this system commercially viable it is essential that the total cost should be kept low.

[0003] Location tracking of objects in an indoor environment can be performed with various techniques, which can be based on mechanical, acoustical, ultra-sonic, optical, infrared, inertial, or radio-signal measurements. Among these systems, radio-based location positioning systems are most frequently used to sense and track the position of moving objects in an indoor environment. A radio receiver attached to the objects either measures the signal strength, the angle of arrival, or the arrival-time difference of received radio signals that are transmitted of multiple pre-installed reference transponder units. Since the locations of the radio transponders are known, a triangulation or signature method can be applied to determine the physical location of the moving object. The location estimates obtained with a radio-based location-positioning system are long-term stable, but are rather infrequently updated and suffer under a large error variance due the fading radio channel. Location estimates from measurements obtained with an Inertial Navigation System (INS), which comprises accelerometers, gyroscopes, and compass, are frequently applied in an outdoor environment (often in conjunction with the global positioning system) to track maneuverable vehicles like land crafts, aircrafts, and ships. INS systems can also be used for location tracking of objects in an indoor environment. These estimates are rather reliable, well-suited for short-term tracking, even though they suffer from drifts due to the integration operation required for the derivation of the location position from the acceleration and angular rate.

[0004] The present invention overcomes these problems and presents a solution for accurately estimating the position of a moving object in an indoor environment.

[0005] The following patents and papers are of particular interest for the disclosed tracking scheme:

[0006] The document U.S. Pat. No. 6,205,401 titled “Navigation system for a vehicle especially a land craft” describes a navigation system for a land vehicle, comprising one gyroscope, two accelerometers, and a velocity measurement device. In addition, a GPS receiver and/or map can provide position reference data. A Kalman-filter based approach is used to determine from the inertial measurements and the reference data the vehicle position and the direction of travel. The drawback of this invention is that it cannot be applied in an indoor environment because the reception of satellite signals within buildings is unreliable or even impossible due to wall shielding effects. Another document WO 2004/017569A3, titled “Transponder subsystem for supporting location awareness in wireless networks”, discusses an apparatus and method for determining the location of a communication device within an IEEE 802.11 standard based WLAN. The apparatus comprises transponders for communicating with the device when it is situated in the coverage area of the WLAN, and a processing unit for deriving the location of the device in dependence on information received from the transponders. For the determination of the location, measurement parameters such as received signal strength and/or time delay from a radio signal exchange between the device and transponders can be used. Adding inertial sensors to the positioning apparatus, and combining the radio-signal and inertial measurements with a Kalman-filter approach as proposed in the present invention can significantly enhance the positioning accuracy and reliability of the proposed method. The paper titled “Estimating optimal tracking filter performance for maneuvering targets” published in IEEE Transactions on Aerospace and Electronic Systems, vol 6, no 4, July 1970, deals with optimal tracking of manned maneuverable vehicles such as aircrafts, ships, and submarines. A Kalman-filter has been derived using a simple process model that closely represents the motions of the maneuvering targets in the 3-dimensional space. This paper describes the method of deriving from a continuous-time state-space description the equivalent discrete-time equations. However, the paper does not consider mode switching or forward-backward smoothing to enhance the accuracy of the vehicle tracker.

[0007] Various studies have shown that neither an inertial navigation system (INS) nor a radio-based positioning system can provide position estimates with the desired precision accuracy in an indoor environment. The present invention provides a method and apparatus that deploys and merges the location information obtained with a radio-based (or any other reference) positioning system and an inertial navigation system with a novel data fusion mechanism. This leads to a solution that offers higher accuracy and improved reliability than the application of any of the two basic type-specific solutions. Significant improvements in the position accuracy can only be gained if the parameters of the INS system leading to bias and drift in the location position can be reliably estimated and controlled, and the noise in the radio measurements introduced by radio channel effects can be eliminated. This task can be solved in an optimal way by, firstly, modeling the generation process of acceleration, bias, velocity, and position with a common state-space model and, secondly, estimating the position location from the measured data with an extended Kalman-filter approach. This novel approach provides gains in terms of position accuracy by, firstly, dynamically switching the Kalman-filter between several modes of operation and, secondly, incorporating a forward-backward smoothing mechanism into the Kalman-filter based location position estimator.

[0008] The object of the instant invention is to provide an apparatus and method to accurately estimate the position of a moving object in an indoor environment.

[0009] Another object of the invention is to simultaneously measure the movement of the object with an inertial navigation system and a reference positioning system, and optimally combine the measurements with an extended Kalman-filter based approach.

[0010] Another aspect of the present invention is the dynamic switching of the extended Kalman-filter between several modes of operation.

[0011] Yet another aspect of the present invention is the incorporation of forward-backward smoothing mechanism in the position estimation approach.

[0012] Accordingly, the present invention provides an apparatus for providing accurate position of a moving object in an indoor environment, which comprises:

• • [0013] a Reference Positioning System (RPS)
  • [0014] an Inertial Measurement Unit (IMU) installed on said moving object.
  • [0015] a Coordinate Transformer (COT) coupled to the outputs of said IMU, and
  • [0016] a Location Position Estimator (LPE) coupled to the outputs of said RPS and said COT.

[0017] Further, the present invention provides a method for accurately determining the position of a moving object in an indoor environment using a combination of an Inertial Measurement Unit (IMU) and a Reference Positioning System (RPS), comprising the steps of:

• • [0018] obtaining reference position data from said RPS,
  • [0019] obtaining additional data on moving object from said IMU, and
  • [0020] estimating the position of the moving object from the data obtained from the RPS and the IMU.

[0026] The present invention provides an apparatus and method for measuring the position of moving objects with an inertial navigation system, a reference positioning system, and a Kalman-filter based fusion mechanism, which leads to a solution that offers higher accuracy and improved reliability than the application of any of the two basic type-specific location positioning solutions. FIG. 1 shows the preferred embodiment of the location tracking system under consideration. It comprises a reference positioning system 101, an inertial measurement unit 102 with a gyroscope 121 and accelerometers 122, a co-ordinate transformer 103 with an angle estimator 131 and rotation unit 132, and a location position estimator 104. The outputs of the reference positioning system 101 and co-ordinate transformer 103 are connected to the location position estimator 104. The reference positioning system 101 provides estimates r^r_X(t) and r^r_Y(t) on the current position of the object in a target co-ordinate system (X,Y). The estimates are updated not necessarily continuously in time, but whenever new position measurements are available. Each estimate r^r(t) randomly deviates from the position r(t) of the object according to following relation
r^r(t)=r(t)+(t),
where υ_r(t) is additive white Gaussian noise (AWGN) with a variance σ²_υr. The inertial measurement unit 102 as disclosed above measures acceleration and angular velocity in a right-handed Cartesion co-ordinate system (x,y,z) that is body-fixed to the inertial measurement unit 102. In present embodiment, the inertial measurement unit 102 is mounted on the object in such a way that the z-axis is pointing in the opposite direction of the gravity vector; therefore, any movement of the object on a plain floor can be tracked with one gyroscope 121 and two accelerometers 122. Accelerometers 122 provide real-time measurements of acceleration components aⁱ_x(t) and aⁱ_y(t) of the moving object in x-and y-direction. As these measurements are rather noisy due to the vibrations of the object and often biased by a non-negligible, time-varying offset, each measurement aⁱ(t) is generated from the acceleration a(t) and bias b(t) according to the equation
aⁱ(t)=a(t)+b(t)+υ_a(t),
where υ_a(t) is additive white Gaussian noise (AWGN) with a variance σ²_υa.

[0027] The gyroscope 121 measures the angular velocity {circumflex over (ψ)}_zⁱ(t) around the z-axis. Based on this noisy measurement, the angle estimator 131 continuously estimates the rotation angle {circumflex over (ψ)}_zⁱ(t). In a low-noise environment, this operation can be performed with an integrator. The output value of angle estimator 131 has to be initialized so that the x-and y-axis are aligned to the target co-ordinate system (X,Y). After this initial calibration step, the rotation unit 132 continuously transforms the measurements aⁱ_x(t) and aⁱ_y(t) to the target co-ordinate system by

[0028] $\left[\begin{array}{c}{a}_X^i\left(t\right)\\ a_Y^i\left(t\right)\end{array}\right]=\left[\begin{array}{cc}\cos \left({\hat{\psi }}_x^i\right)& -\sin \left({\hat{\psi }}_x^i\right)\\ \sin \left({\hat{\psi }}_x^i\right)& \cos \left({\hat{\psi }}_x^i\right)\end{array}\right]\left[\begin{array}{c}{a}_x^i\left(t\right)\\ a_y^i\left(t\right)\end{array}\right]$

[0029] The dynamic and random movements of the object along the X-and Y-axis of the target co-ordinate system and the resulting signals provided by the inertial measurement unit 102 with co-ordinate transformer 103 and the reference positioning system 101 can be modelled as shown in FIG. 2. The acceleration a(t) of the object along one axis of the co-ordinate system is modelled by a first-order Markov process defined by
{dot over (a)}(t)=−αa(t)+w_a(t), α≧0
where w_a(t) represents white Gaussian noise with variance σ²_wa. α defines the correlation between successive acceleration values and is inversely proportional to the time maneuver constant of the object.

[0030] Similarly, a time-dependent bias b(t) introduced by sensor imperfections can be modelled by
{dot over (b)}(t)=−βb(t)+w_b(t), β≧0
where w_b(t) is additive white Gaussian noise (AWGN) with variance σ_wb². The correlation coefficient β, however, takes on a larger value than a because the bias b(t) changes with a much slower rate than the acceleration a(t). The position r(t) of the object, which is modeled at the output of the integrator 209, relates to its acceleration a(t), which is modeled at the output of the integrator 202, according to
{umlaut over (r)}(t)={dot over (υ)}(t)=a(t)
where υ(t) denotes the velocity of the object.

[0031] The above three linear differential equations represent the continuous-time process model of the investigated system, which can be written in the state-space form
{dot over (x)}(t)=Fx(t)+Gw(t)
where the state vector x(t), the process noise w(t), and the corresponding matrices F and G are given by

[0032] $x\left(t\right)=\left[\begin{array}{c}r\left(t\right)\\ \upsilon \left(t\right)\\ a\left(t\right)\\ b\left(t\right)\end{array}\right],F=\left[\begin{array}{cccc}0& 1& 0& 0\\ 0& 0& 1& 0\\ 0& 0& -\alpha & 0\\ 0& 0& 0& -\beta \end{array}\right],w\left(t\right)=\left[\begin{array}{c}{w}_a\left(t\right)\\ w_b\left(t\right)\end{array}\right],G=\left[\begin{array}{cc}0& 0\\ 0& 0\\ 0& 1\\ 1& 0\end{array}\right]$

[0033] The measurement model is chosen in accordance with equations r^r(t)=r(t)+v_r(t) and aⁱ(t)=a(t)+b(t)+υ_a(t) as
z(t)=Hx(t)+v(t),
where the output vector z(t), measurement noise v(t), and the matrix H are given by

[0034] $z\left(t\right)=\left[\begin{array}{c}{a}^i\left(t\right)\\ r^r\left(t\right)\end{array}\right],H=\left[\begin{array}{cccc}0& 0& 1& 1\\ 1& 0& 0& 0\end{array}\right],v\left(t\right)=\left[\begin{array}{c}{\upsilon }_a\left(t\right)\\ {\upsilon }_r\left(t\right)\end{array}\right]$

[0035] The process and measurement models given by above two equations reflects the behavior of the system if the object is moving and location estimates are provided by the reference positioning system. If the object is stationary, a different model for the dynamic behaviour of the object can be obtained by incorporating the constraint a(t)=υ(t)=0 into the system matrix F of the process model as explained above. Similarly, if reference location position estimates are not available, the matrix H in the measurement model can be modified so that the signal r^r(t) in the output vector z(t) is set to 0.

[0036] FIG. 3 elaborates the location position estimator 104 in FIG. 1, where the output signals from reference positioning system 101 and co-ordinate transformer 103 are connected to the forward Kalman-filter 303 for providing location-position estimates {circumflex over (r)}(t) in real-time, the backward Kalman-filter 305 with a recording 302 and smoothing 306 unit for providing optimal smoothed estimates {tilde over (r)}(t), and the motion and reference detector 301 with the control unit 304 for controlling the operation of the estimator 104. Here in this section the proposed location-positioning method and corresponding approach are only given for one dimension of the target co-ordinate system (X,Y).

[0037] The forward Kalman-filter 303 continuously monitors the noisy measured signals acceleration aⁱ(t) and reference position r^r(t) and computes in real-time a location-position estimate {circumflex over (r)}(t) of the object based on a chosen process and measurement model. This is achieved by, firstly, replicating the process and measurement model without noise sources in the Kalman-filter 303 to generate a state vector estimate {circumflex over (x)}(t) and a corresponding output vector {circumflex over (z)}(t)=H{circumflex over (x)}(t) and, secondly, continuously updating the state vector estimate according to
{circumflex over ({dot over (x)}(t)=F{circumflex over (x)}(t)+K(t)[z(t)−{circumflex over (z)}(t)]
{circumflex over (x)}(t) is an optimal estimate of the state vector x(t) with respect to a mean-squared error criterion. The Kalman-filter 303 is driven by the error between the measured signal vector z(t) and the reconstructed output vector {circumflex over (z)}(t) weighted by the Kalman gain matrix K(t). For obtaining the optimum Kalman gain settings, the covariance matrix of the state vector estimation error is computed.

[0038] FIG. 4 shows one of the embodiments for realizing the Kalman-filter 303 obtained for the process and measurement model given in corresponding equations as discussed above. The filter 303 comprises eight time-varying Kalman gains K₁₁(t), K₁₂(t), K₂₁(t) . . . K₄₂(t), two adders 401 and 402 at the input of the Kalman gains, four adders 404 . . . 407 at the output of the Kalman gains, and the process and measurement model 408. The estimates for the position {circumflex over (r)}(t), velocity {circumflex over (υ)}(t), acceleration â(t) of the object, and accelerometer bias {circumflex over (b)}(t) are provided at the output of four integrators in the process and measurement model 408. Since three of the four integrators are cascaded, the filter 303 is especially sensitive to DC-offsets present at the input of the integrators leading to severe drift effects in the integrator output signals. To avoid this inherent deficiency of the filter solution, additional bias and drift compensation techniques are applied as discussed below.

[0039] The forward Kalman-filter 303 provides estimates {circumflex over (x)}(t) of the state vector x(t) in real-time by taking into account all information of the measurement signals obtained up to time t. An even better estimate {tilde over (x)}(t) in terms of estimation accuracy can be obtained by performing forward-backward smoothing. This approach exploits additional information on the state vector which is contained in the measurement signals received after time t. The optimal smoothed estimate {tilde over (x)}(t) can thus not be computed in real time, but only after some delay required for collecting future measurements and afterwards post-processing all available data. However, this inherent disadvantage of optimal smoothing is of no importance if the state vector does not change its value when the approach is implemented. Therefore, we propose to apply forward-backward smoothing when the object is not moving in order to enhance the precision of state-vector estimates at past time instants and thus significantly improve the accuracy of the estimate of the current position of the object.

[0040] The forward-backward smoothing approach can be realized with a recording unit 302, a forward Kalman-filter 303, a backward Kalman-filter 305, and a smoothing unit 306 as shown in FIG. 3. When the object starts moving at any arbitrary time t₀, the recording unit 302 starts storing the signals acceleration aⁱ(t) and reference position r^r(t) until the object stops at time t₁. At this time t₁, the location position estimator 104 starts performing forward-backward smoothing over the fixed time-interval [t₀, t₁] based on the stored data to obtain optimal smoothed estimates {tilde over (x)}(t) of the state-vector x(t) for any t [t₀, t₁]. The forward Kalman-filter 303 is implemented as discussed above. The filter 303 uses at time t₀ the initial conditions {circumflex over (r)}(t₀)=r₀, {circumflex over (b)}(t₀)=b₀, â(t₀)={circumflex over (υ)}(t₀)=0, runs forward in time from t₀ to t, and computes the forward state vector estimate {circumflex over (x)}^f(t). The backward Kalman-filter 305 has the same structure as the forward Kalman-filter 303, but the algorithm runs backward in time from t₁ to t. It provides the estimate {circumflex over (x)}^b(t). As initial conditions for the backward filter, the acceleration â(t₁) and the velocity {circumflex over (υ)}(t₁) are set to zero because the object is not moving, while the initial position {circumflex over (r)}(t₁) and bias {circumflex over (b)}(t₁) are modelled as Gaussian random variables. The smoothing unit 306 optimally combines the estimates {circumflex over (x)}^f(t) provided by the forward Kalman-filter 303 and {circumflex over (x)}^b(t) provided by the backward filter 305 to generate the optimal smoothed estimate {tilde over (x)}(t). The optimal combining of the two estimates is accomplished by relatively weighting the estimates in accordance to the covariance matrices of the corresponding state vector estimation error, which are also provided by the forward Kalman-filter 303 and backward Kalman-filter 305.

[0041] The motion and reference detector 301 continuously monitors the acceleration signal aⁱ(t) and reference position signal r^r(t), firstly, to detect whether the object is in-motion or stationary and, secondly, to decide for the presence-of-reference or absence-of-reference position signal. The second event can also be directly signaled from the reference positioning system 101 to the location position estimator 104. Motion detector 301 detects the object's motion by continuously tracing the magnitude of the acceleration measurement signal aⁱ(t), which strongly oscillates when the object is moving. The detection can thus be performed by sampling this signal at the rate of 1/T, averaging N consecutive samples, and then deciding for the event object stationary if the obtained mean value is below a pre-defined threshold μ, as defined by

[0042] $\stackrel{\_}{a}\left(\mathrm{kT}\right)=\frac{1}{N}\sum _{l=0}^{N-1}a^i\left(\mathrm{kT}-\mathrm{lT}\right)\{\begin{array}{cc}\le \mu & \mathrm{no}\text{-}\mathrm{movement}\\ >\mu & \mathrm{in}\text{-motion}\end{array}$

[0043] The parameters N and μ are chosen so that even short stops of the object can be reliably detected and false decisions for not moving are avoided.

[0044] Depending on the detected events object in-motion/stationary and presence/absence-of-reference position signal, the control unit 304 dynamically switches the Kalman-filters 303 and 305 between the following modes of operation:

Mode 1: Object In-Motion and Presence-of-Reference Position Signal

[0045] In this configuration, the control unit 304 enables the operation of the forward Kalman-filter as shown in FIG. 4. The filter 303 provides estimates of the position, acceleration, and velocity of the object as well as estimates of the accelerometer bias in real-time. The acceleration signal aⁱ(t), reference position signal r^r(t), and the mode parameters are recorded with recoding unit 302 in a storage device in order to perform forward-backward smoothing in Mode 3.

Mode 2: Object In-Motion and Absence-of-Reference Position Signal

[0046] Since no reference signal is available, the location position estimator 104 has to derive an estimate which is entirely based on the accelerometer measurements aⁱ(t). This change can be modelled by setting all elements in the second row of the measurement matrix H to zero. The control unit 304 reconfigures the Kalman-filter 303 so that this change in the measurement model is also reflected in the filter 303.

[0047] Moreover, the control unit 304 freezes the bias estimate {circumflex over (b)}(t) in the filter 303 to its current value when the mode of operation if switched from presence-of-reference to absence-of-reference position signal. This freeze operation is recommendable to avoid observability problems of simultaneous changes of acceleration and bias in the system model at the absence of a reference signal. The acceleration signal aⁱ(t), reference position signal r^r(t), and the mode parameters are recorded in the storage device with recoding unit 302 to perform forward-backward smoothing in Mode 3.

Mode 3: No-Movement of the Object

[0048] Whenever the motion detector 301 detects no movement of the object, the control unit 304 resets the acceleration estimate â(t) and the velocity estimate {circumflex over (υ)}(t) in the Kalman-filter 303 to zero. This operation calibrates the integrator output signals in the process and measurement model 408 to the known reference values of zero, and thus prevents offset accumulation by the integrators. Since the acceleration and velocity of the object is zero, an enhanced process model for the dynamic behavior of the object is obtained by incorporating the constraint a(t)=υ(t)=0 into the process equations. The control unit 304 reconfigures the Kalman-filter 303 so that this change in the process model is also reflected in the filter 303. Since the location position estimator 104 has recorded the signals aⁱ(t) and r^r(t) while the object was moving as explained in Mode 1, the recorded data stored in the last time interval before the object reached its stop can be post-processed by performing forward-backward smoothing with smoothing unit 306. The control unit 304 initializes the execution of the smoothing procedure after the event no-movement has been detected. After post-processing the recorded data, the location position estimator 104 provides the optimal smoothed estimate of the objects location.

[0049] It is believed that the present invention and many of its attendant advantages will be understood by the foregoing description. It is also believed that it will be apparent that various changes may be made in the form, construction and arrangement of the components thereof without departing from the scope and spirit of the invention or without sacrificing all of its material advantages. The form herein before described being merely an exemplary embodiment thereof, and it is the intention of the following claims to encompass and include such changes.