Method for locating a transmitter

Abstract

The invention relates to a method for locating a transmitter, wherein the following steps are carried out: emitting a wave-based signal by means of the transmitter so that a wave field emanates from the transmitter, receiving the wave-based signal by means of at least one receiver, in particular a plurality of receivers, which has / have at least one receiving antenna, preferably an antenna array with a plurality of receiving antennas spatially offset from one another, and forming a measurement signal in each receiver of the at least one receiver for a plurality of receiving antennas, preferably each receiving antenna. The respective measurement signal is dependent on the spatial distribution of the wave field, and the measured phase profile thereof is characteristically influenced by the signal propagation time from the transmitter to the respective receiving antenna. The measured phase profile of the respective measurement signal is used to locate the transmitter and to estimate the receiver-specific measurement error level of at least one receiver.

Description

[0001] 000171-25 He Friedrich-Alexander-University Erlangen-Nuremberg D E - 91054 Erlangen^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Method for locating a transmitter ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The present invention relates to a method for locating a transmitter, in particular by a PDOA (phase difference of arrival) based determination of the position of the transmitter. There are already several providers of angle-based positioning systems, in particular in the Bluetooth standard, e.g.: Quuppa, infsoft, BlueIot, u-blox. These use the PDOA measurements on several receiver arrays distributed in space and whose positions are previously known, to first estimate the angle to the transmitter at each receiver array and then to localize the transmitter using multiangulation. Such a system is illustrated in Fig. 1. It is known from the prior art that the localization accuracy of PDOA systems depends directly on the aperture size of the receiver arrays in relation to the measurement distance, cf. Sippel, Erik; Geiss, Johanna; Brückner, Stefan; Gröschel, Patrick; Hehn,Markus Vossiek, Martin: Exchanging Bandwidth With Aperture Size in Wireless Indoor Localization - Or Why 5G / 6G Systems With Antenna Arrays Can Outperform UWB Solutions. In: IEEE Open Journal of Vehicular Technology, Vol. 2 (2021), pp. 207–217. Known angle-based PDOA localization approaches do not work optimally for large receiver arrays, since the transmitter to be located is usually located in the near field of the receiver. This, however, contradicts the assumption of plane receive waves of angle estimators. Due to the spherical waves emitted by the transmitter, this assumption only applies to limited spatial regions (namely, the far field) and can therefore only be considered valid for small receive arrays. The near field is often referred to in the literature as the Fresnel region, where the Fresnel approximation, but not the Fraunhofer approximation, applies. The far field, on the other hand, is often referred to as the Fraunhofer region.in which the Fraunhofer approximation applies. Furthermore, according to the state of the art, receiver arrays for angle estimation are typically fully populated with receiving antennas, so that the entire area is covered with antennas, usually at a distance of half the wavelength, which is very complex and cost-intensive for large receiving arrays. For positioning with large arrays, sparsely populated receiver arrays are therefore often used, cf. Pavlenko, T.; Reustle, C.; Dobrev, Y.; Gottinger, M.; Jassoume, L.; Vossiek, M.: Design and Optimization of Sparse Planar Antenna Arrays for Wireless 3-D Local Positioning Systems. In: IEEE Transactions on Antennas and Propagation, Vol. 65 (2017), No. 12, pp. 7288–7297. Consequently, for precise and cost-effective PDOA-based positioning systems, the evaluation of raw data (e.g., complex amplitudes of received signals) without angle estimation is necessary. For example, this is known from DE102019110512A1.that the position of the transmitter can be efficiently determined by recursively evaluating the phase differences between the spatially distributed receiving antennas of the receiving arrays. This is usually done by applying recursive filters, in particular the extended Kalman filter in the form of the holographic extended Kalman filter (HEKF). See BRÜCKNER, STEFAN; SIPPEL, ERIK; LIPKA, MELANIE; GEISS, JOHANNA; VOSSIEK, MARTIN: Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter. In: IEEE Open Journal of Vehicular Technology, Vol. 3 (2022), pp. 55–67. The metric is always implicitly least squares. Calculating difference phases from the measured absolute phases creates correlated measurement noise, which is represented within the covariance matrix by entries outside the main diagonal. The correct implementation of this covariance matrix within the HEKF is essential, as it ensuresthat the information from the relative phases of distant antenna pairs, which are not directly calculated within the phase difference evaluation matrix, is implicitly included. See, for example: SIPPEL, ERIK: Holographic 3D Indoor Localization, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2022. Due to the least-squares metric, the position estimation assumes normally distributed additive noise (AWGN – Additive White Gaussian Noise). The position estimation is thus implicitly based on the assumption of constant measurement conditions from each receiver to the transmitter. However, this is not correct in large, changing environments. The reason for this is, for example, the fact that measurement conditions in real-world situations are constantly changing. In particular, the evaluated line of sight is regularly blocked, which automatically leads to poorer positioning results, since the measurements are assumed to be correct and included in the evaluation. This leads tothat automatic detection of the current measurement conditions of the receiver is necessary. Systems that adaptively estimate the covariance matrices of measurement and system noise within Kalman filters are known from the state of the art, cf. AKHLAGHI, SHAHROKH; ZHOU, NING; HUANG, ZHENYU: Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. In: 2017 IEEE Power Energy Society General Meeting, 2017, pp. 1–5; MEHRA, R.: On the identification of variances and adaptive Kalman filtering. In: IEEE Transactions on Automatic Control, Vol. 15 (1970), No. 2, pp. 175–184; MEHRA, R.: Approaches to adaptive filtering. In: IEEE Transactions on Automatic Control, Vol. 17 (1972), No. 5, pp. 693–698. MOHAMED, AH; SCHWARZ, KP: Adaptive Kalman Filtering for INS / GPS. In: Journal of Geodesy, Vol. 73 (1999), No. 4, pp. 193–203 or WANG, JINLING: Stochastic Modeling for Real-Time Kinematic GPS / GLONASS Positioning. In: NAVIGATION, Vol. 46 (1999), No. 4, pp. 297–305. This results inthat the estimation of the covariance matrices of the measurement noise is primarily relevant for the HEKF. This is always fully estimated in the proposed systems. However, due to the evaluation of phase differences, there are very many entries in the covariance matrix of the measurement noise, which makes it difficult to estimate. If the covariance matrix of the measurement noise is estimated, it has an arbitrarily indeterminate form. However, the evaluation of difference matrices within the HEKF necessarily requires a representation that is shaped according to the evaluation of the phase differences. It follows that the complete estimation of the covariance matrices of the measurement noise, known in the literature, is not possible with the HEKF. The present invention overcomes the disadvantages listed above or at least eliminates them. According to the invention, a method for locating a transmitter is provided,wherein the following steps are carried out in the method: radiating a wave-based signal by the transmitter so that a wave field emanates from the transmitter, receiving the wave-based signal by at least one, in particular several, receivers having at least two receiving antennas, preferably an antenna array with several spatially offset receiving antennas, and forming a measurement signal in each of the at least one receiver for several, preferably each, receiving antenna, wherein the respective measurement signal depends on the spatial distribution of the wave field and whose measured phase characteristic is characteristically influenced by a signal propagation time from the transmitter to the respective receiving antenna,and the measured phase characteristic of the respective measurement signal is used both to locate the transmitter and to estimate a receiver-specific measurement error power at at least one receiver. According to a further optional development of the present invention, it can be provided that an Extended Kalman Filter (EKF), in particular a Holographic Extended Kalman Filter (HEKF), is used to locate the transmitter. Advantageously, it can be provided that a covariance matrix of a residual of the localization is estimated or generated, and the receiver-specific measurement error powers at at least one receiver are inferred from the entries of the covariance matrix of the residual. According to a further modification of the present invention, it can be provided that a receiver is only used to locate the transmitter if its currently estimated receiver-specific measurement error power is low.preferably below a predetermined threshold value. Furthermore, according to an advantageous embodiment of the present invention, it can be provided that the estimated receiver-specific measurement error power of a respective receiver is used to weight the influence of the receiver on the localization of the transmitter, in particular wherein a higher estimated receiver-specific measurement error power leads to a reduction in the weighting of the respective receiver. According to a further optional development of the present invention, it can be provided that at least one receiving array of the at least one receiver is so large that the transmitter to be localized is located in the near field of this receiver. Furthermore, according to an advantageous modification of the present invention, it can be provided that a Doppler evaluation, a magnetic field-based position determination, an optical system, an ultrasound signal and / or additional sensor values,In particular, sensor values ​​from an inertial sensor system are taken into account for the localization of the transmitter. The method according to the invention can be further developed by the following refinement, according to which subgroups of receiving antennas are formed at at least one receiver, the receiver-specific measurement error power of which is estimated. Advantageously, it can be provided that the estimated receiver-specific measurement error power of a subgroup is used to weight the influence of the subgroup on the localization of the transmitter, in particular, wherein a higher estimated receiver-specific measurement error power leads to a reduction in the weighting of the subgroup. The invention further relates to a system for carrying out a method according to one of the previously discussed aspects. Further features,Details and advantages of the invention will become apparent from the following description of the inventive concept and a detailed exemplary embodiment. Figures 1 and 2 show: a representation of the prior art with a localization environment in which three receivers, each with six receiving antennas, localize a transmitter; Figure 2 shows a sketch of a receiver from Figure 1, with the dashed lines representing a "spanning tree" for evaluating the phase differences; Figure 3 shows a representation of a HEKF with adaptive estimation of the measurement error power of the individual receivers; and Figure 4 shows a representation of an EKF with adaptive estimation of the entire covariance matrix. Basic Concept of the Invention: According to the invention, a concept is provided which enables the adaptive detection of the measurement error power at each array within a localization system, which can be constructed, for example, as shown in Figure 1, from many receiving arrays.The HEKF is used as a localization algorithm, as already known from DE 10 2019 110 512 A1. The adaptive detection of the measurement error performance enables the detection of challenging multipath conditions, blocked lines of sight, poorly calibrated receivers, etc. Kalman filters always assume that a system state ^, ^ which changes according to a system model over the time steps ^ and is to be estimated using a measurement model. The system model ^^ = ^^(^^^^) + ^^in the HEKF consists of an arbitrarily selectable state transition model ^ ^ and system noise ^ ^A typical system model for a localization algorithm such as the HEKF is the constant velocity model, which assumes that the target moves at a roughly constant speed. The system model can be extended arbitrarily with additional parameters to be estimated that have little or no connection to the actual evaluation of the HEKF, e.g., inertial sensors. Furthermore, the influence of system noise can be eliminated by choosing the system model and the system noise. The changing system state ^ ^ is estimated using the measurement model ^^ = ^^(^^) + ^^, where ^ ^ the measuring function and ^ ^ represents the measurement noise. In the case of the HEKF, ^ ^ The phase differences between the spatially distributed antennas are evaluated at each receiver. This assumes that at time step ^ other ^ ^ ten antenna out of a total of ^ ^,^R Antennas of the ^ ^th recipient of a total of ^ ^ Recipients the phase is measured, cf. BRÜCKNER, STEFAN; SIPPEL, ERIK; LIPKA, MELANIE; GEISS, JOHANNA; VOSSIEK, MARTIN: Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter. In: IEEE Open Journal of Vehicular Technology Vol. 3 (2022), pp. 55–67 and SIPPEL, ERIK: Holographic 3D Indoor Localization, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2022, where mod ^^^ (∙) maps the ambiguous phases with if mod^^(^) ≤ ^if mod^^(^) > ^to (−^, ^], ^^ = 2^^^ corresponds to the angular frequency, ^in,^R,^ represents the unknown incoherent phase of the transmission-reception process and the delay ^ ^R,^^,^ from the position of the transmitter ^ ^^,^ and the receiving antenna ^ ^R,^^ to To use the measurements for position estimation, the phase measurements are combined and phase differences are determined. For the ^ ^ten receiver thus results in the measurement vector The corresponding noisy measurement vector results from the superposition with the noise vector ^^^,^,^^^^ = ^^^,^ + ^^^,^.The measurement errors ^ ^^,^ have the time-varying receiver-specific power . This is referred to below as the receiver-specific measurement error power. Depending on the system and measurement model, the time-varying receiver-specific measurement error power ^ ^ ^ R ,^ by a model from the previous receiver-specific measurement error performance The measurement errors can be caused by various effects such as noise, calibration errors, multipath propagation, or shadowing. Multipath propagation and shadowing are the dominant error sources in indoor localization systems. The evaluation matrix ^ ^R the phase differences to and The evaluation matrix ^^R calculates the phase differences between two antennas in each row and contains only one "1" and one "-1" per row and is otherwise filled with zeros. In order to evaluate all information, this must have the rank ^^,^R − 1, whereby one degree of freedom is lost due to the unknown incoherent phase. Since the evaluated phase difference becomes more ambiguous with increasing antenna distance, it is advisable to use the evaluation matrix ^ ^Rto be designed in such a way that the evaluation of the phase differences of neighboring antennas creates a “spanning tree” that connects all antennas with one another, cf. BRÜCKNER, STEFAN ; SIPPEL, ERIK ; LIPKA, MELANIE ; GEISS, JOHANNA ; VOSSIEK, MARTIN: Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter. In: IEEE Open Journal of Vehicular Technology Vol. 3 (2022), pp. 55–67 and SIPPEL, ERIK: Holographic 3D Indoor Localization, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2022. An example “spanning tree” in which the evaluation matrix evaluates the phase differences of the antenna pairs 1-2, 2-3, 3-4, 4-6, 6-5 can be seen in Fig. 2. Assuming that the measurement error contributions at the individual antennas were uncorrelated from each other, i.e. the covariance matrix of ^ ^^,^ the shape , evaluating the phase differences for ∆^ ^^,^ the covariance matrix for the measurement errors on ^ ^ ten receiver. This characteristic structure ^ ^R ^ T ^R the resulting covariance matrix ^ ^R is fundamental for the functionality of the HEKF, as it enables the relative phase differences of antenna pairs to be implicitly evaluated within the HEKF, which are not directly within ^ ^Rcalculated, but are only connected via the spanning tree, such as the phase difference of antenna pair 1-5 in Fig. 2. At the same time, the assumption that the measurement error contributions at the individual antennas were uncorrelated with each other is incorrect for multipath propagation. This directly follows that the complete estimations of the covariance matrices within Kalman filters established in the literature cannot be applied to the HEKF, since their estimation of the covariance matrices can usually be arbitrarily structured. This leads to the correlation of the multipath propagation being reproduced by entries in the estimated covariance matrix. Thus, the estimation of the receiver-specific measurement error power ^ R ,^ necessary, in which the knowledge of the characteristic structure of the HEKF. To combine the phase differences of all receivers, they are combined into a vector The corresponding erroneous measured values ​​are where the covariance matrix is ⋯ In general, Kalman filters alternately use Predict Step) the system model to determine from the estimated system state ^ ^^^|^^^ and its estimated covariance ^^^^|^^^ from the previous time step ^Prediction for the system state ^ ^|^^^ and its estimated covariance ^ ^|^^^ of the current time step, and in the correction step (English: Update Step) the measurement model to determine the system state ^^|^ of the current time step and its covariance^ from the prediction made and the available measurements ^|^ In this invention report, the correction step is extended by the estimation of the receiver-specific measurement error powers at all receivers expanded. ^ ^ ^ , ^ ^^ denotes the estimated measurement error powers and their covariance matrices. In the ^th step, only the measurement error power estimate from the (^ − 1) −th step can be used for localization using HEKF. This results in the following algorithm steps: 1. ^^^|^^^, ^^|^^^^ = Prediction^^^^^|^^^, ^^^^|^^^^ 2. ^^^^^ = Composition(^^^^^) 4. ^ = ^ + 1 and go to step 1. These are shown in Fig. 3 as a sketch. Here, in step 1, the prediction about the current system state ^ ^|^^^ and its uncertainty ^ ^|^^^ To combine this information with the measurements, in step two the previously estimated receiver-specific measurement error powers at all receivers ^ ^ ^^^ at each receiver the covariance matrix of the measurement errors of the phase difference evaluation at the individual receivers calculated and the total covariance matrix ^ ^ ^^^Since steps 1 and 2 are executed independently, their order of execution is irrelevant. The total covariance matrix created in step 2 ^ ^ ^^^ is used in combination with the prediction from step 1 and the measured phase differences in step 3 to determine the current system state ^ ^|^ , the covariance matrix ^ ^|^ and receiver-specific measurement error performance at all receivers ^ ^ ^to be estimated. The next time step ^ + 1 is then used. Depending on the currently estimated receiver-specific measurement error performance, only those receivers that currently have good measurement conditions, i.e., low measurement error performance, can be used for position estimation. Furthermore, the algorithm shown can be implemented so that the measurement error performance of any subgroup of receiving antennas can be estimated at any receiver. In comparison to HEKF with adaptive estimation of the measurement error performance of the individual receivers, the established process of adaptive estimation of the covariance matrix in Kalman filters is shown in Fig. 4. Here, the covariance matrix ^^^ is estimated directly and evaluated unchanged in the next time step, which would prevent the evaluation of the difference phases in HEKF from working.Detailed Example The following shows a detailed example of how adaptive measurement error estimation can be performed within the HEKF. The implementation of the HEKF is based on STEFAN BRÜCKNER; ERIK SIPPEL; MELANIE LIPKA; JOHANNA GEISS; MARTIN VOSSIEK: Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter. In: IEEE Open Journal of Vehicular Technology, Vol. 3 (2022), pp. 55–67. For a simpler illustration, the iterative execution of the correction step is omitted. Steps 1–3 of the estimation process are successively performed. Step 1: The widely used constant velocity model is used for prediction. The system state is determined by the system state. from the position ^ ^ and the speed ^ ^ of the sender. This allows the prediction of the system state to be calculated with ^^|^^^ = ^^^^^|^^^, where the matrix provides a linear movement model. The corresponding covariance matrix is calculated, where ^ ^ represents the covariance matrix of the constant velocity model. Step 2: The covariance matrices of the measurement errors of all receivers in the ^th step are calculated using the previously estimated receiver-specific measurement error performance and the preprocessing matrices used to give ^^^R,^^^ = ^^^ ^ TR ,^^^ ^ ^R ^ ^R The entire covariance matrix of the measurement errors of all receivers is then calculated to Step 3: The implementation of step 3 is carried out by first performing the known correction of the HEKF in step 3.1 and then evaluating the residual in step 3.2 to determine the measurement error power. Step 3.1: First, the phase differences of the measured data at all receivers ∆^^^,^,^^^^ are calculated. calculated and the measurement vector ∆^ ^,^,^^^^ summarized. The hypothetical phase differences ^^^^^|^^^ ^, which result from the predicted system state ^^|^^^, are subtracted from the measurement vector. From this, the Measurement difference (English: residuum) to ^ To determine the correction of the system state, the Kalman gain determined, where ^^ is the Jacobian matrix of the measurement function ^^(^^) at the predicted system state ^ ^|^^^ This results in the corrected system state and the corresponding covariance matrix − Step 3.2: The previously estimated system state ^ ^|^ and the corresponding covariance matrix ^^|^ are now used to calculate the receiver-specific measurement error performance R,^ The measurement residual is calculated as ^^ = mod^^ ^^^ − ^^^^^|^ ^^. If one evaluates the expected covariance matrix of the residual, it can be transformed to Consequently, for the estimation of the covariance matrix of the measurement noise ^ ^^ ^ an estimate of the covariance matrix of the residual is necessary. The covariance matrix of the measurement noise ^ ^^ ^ only a calculation aid for estimating the measurement error performance of the arrays ^^ ^ ^ R ,^ The covariance matrix of the residual is recursively determined by estimated, where ^ represents a "forgetting" factor, with which the convergence speed can be controlled. ^^^ thus represents another system state to be stored. From this, the corresponding covariance matrix of the measurement noise can be calculated as This now has the structure from which the covariance matrix of the measurement noise occurred ^ ^ ^^ R,^ for each recipient ^ R is extracted. Since the structure of ^ ^ ^ ^ R,^ is known from the calculation of the phase differences within the HEKF, this is used to calculate the receiver-specific measurement error performance of the ^ R recipient ^ ^ ^ ^ R,^ to extract by first multiplying by ^^ ^^ ^ ^ ^^ ^ ^ ^ a matrix is ​​created with the receiver-specific measurement error power to be estimated on its main diagonal. The estimated receiver-specific measurement error power is then extracted by averaging this main diagonal. This yields ^ ^^^ ^ ^^ ^ ^ ^ ^ ^ , where trace(∙) evaluates the sum of the main diagonal of a matrix. These are finally converted to ^^^ combined. Since the formulas used in step 3.2 are linear mappings, the calculations can also be performed individually for each receiver. The method presented in step 3.2 for estimating the receiver-specific measurement error performance represents an algorithm that is both efficient and high-performance. The very high performance of estimating the receiver-specific measurement error performance instead of entire covariance matrices is also immediately apparent. State-of-the-art methods typically determine angles from the measured phase curves in order to use them for position estimation via Kalman filters. If the covariance matrix of the measurement errors is adaptively estimated in such a method, this is done exclusively via a direct comparison of the angle measurement with the hypothetical angles resulting from the estimated transmitter position.Since the transmitter position is estimated exclusively using the determined angles, this results in an inherently unstable estimation process, as the erroneous angle estimates directly result in an erroneous position estimate and thus, in turn, in an incorrect estimation of the covariance matrix. In comparison, in the presented inventive combination of the HEKF with the adaptive estimation of the receiver-specific measurement error power, all evaluated phase differences of the measurement are compared with the hypothetical phase differences resulting from the estimated transmitter position. Thus, the phase differences measured at a receiver relative to each other are also implicitly used for the adaptive estimation of the receiver-specific measurement error power. This makes it possible to capture the deterioration of the measurement conditions even in cases where the overall position estimate matches the impaired measured values.In the detailed example shown, this is done by using all entries of the covariance matrix, including the entries beyond the main diagonal, of the measurement noise ^ that occurred. ^ ^ ^ R,^ to calculate Thus, the presented HEKF with the adaptive estimation of the receiver-specific measurement error performance is far superior to all known methods.

Claims

000171-25 He Friedrich-Alexander-University Erlangen-Nuremberg D E - 91054 Erlangen^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Method for locating a transmitter^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Claims 1. A method for locating a transmitter, wherein the method comprises the following steps: emitting a wave-based signal by the transmitter, so that a wave field emanates from the transmitter, receiving the wave-based signal by at least one, in particular several, receivers having at least two receiving antennas, preferably an antenna array with several spatially offset receiving antennas, and forming a measurement signal in each of the at least one receiver for several, preferably each, receiving antenna, wherein the respective measurement signal is dependent on the spatial distribution of the wave field and whose measured phase characteristic is characteristically influenced by a signal propagation time from the transmitter to the respective receiving antenna, characterized inthat the measured phase characteristic of the respective measurement signal is used both to locate the transmitter and to estimate a receiver-specific measurement error power at at least one receiver.

2. Method according to the preceding claim 1, wherein an Extended Kalman Filter (EKF), in particular a Holographic Extended Kalman Filter (HEKF), is used to locate the transmitter. - 2 -3. Method according to the preceding claim 2, wherein a covariance matrix of a residual of the localization is estimated or generated, and the receiver-specific measurement error powers at at least one receiver are inferred from the entries of the covariance matrix of the residual.

4. Method according to one of the preceding claims, wherein a receiver is only used to localize the transmitter if its currently estimated receiver-specific measurement error power is low, preferably below a predetermined threshold value.

5. Method according to one of the preceding claims, wherein the estimated receiver-specific measurement error power of a respective receiver is used to weight the influence of the receiver on the localization of the transmitter, in particular wherein a higher estimated receiver-specific measurement error power leads to a reduction in the weighting of the respective receiver. 6.Method according to one of the preceding claims, wherein at least one receiving array of the at least one receiver is so large that the transmitter to be located is in the near field of this receiver.

7. Method according to one of the preceding claims, wherein a Doppler evaluation, a magnetic field-based position determination, an optical system, an ultrasound signal and / or additional sensor values, in particular sensor values ​​of an inertial sensor system, are taken into account for the localization of the transmitter.

8. Method according to one of the preceding claims, wherein subgroups of receiving antennas are formed at at least one receiver, the receiver-specific measurement error performance of which is estimated. - 3 -9. The method according to claim 8, wherein the estimated receiver-specific measurement error power of a subgroup is used to weight the influence of the subgroup on the localization of the transmitter, in particular wherein a higher estimated receiver-specific measurement error power leads to a reduction in the weighting of the subgroup.

10. A system for performing a method according to any one of the preceding claims.

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