# US 2016/0259043 A1 · Method for determining trajectories of moving physical objects in a space on the basis of sensor data of a plurality of sensors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2016/0259043 A1 - Document ID: US20160259043A1 - Status: Published application - Kind: A1 - Application number: 14/902,152 - Filed: 2014-07-01 - Published: 2016-09-08 - Earliest claimed: 2013-07-04 - Assignees: Schweizerische Eidgenossenschaft EidgenöSsisches Departement FüR Verteidigung, BevöLkerungsschutz - Applicants: Schweizerische Eidgenossenschaft EidgenöSsisches Departement FüR Verteidigung BevöLkerungsschutz Un - Inventors: Sacha SCHÄR - Web page: https://patentgrep.com/patent/US20160259043A1 ## CPC - G01S 13/58: Velocity or trajectory determination systems; Sense-of-movement determination systems - G01S 13/723: {by using numerical data} - G01S 13/87: Combinations of radar systems, e.g. primary radar and secondary radar - G01S 7/40: Means for monitoring or calibrating - G01S 7/41: using analysis of echo signal for target characterisation; Target signature; Target cross-section - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES ## Abstract In a method for determining trajectories of movable physical objects in a space on the basis of sensor data from a plurality of sensors, first data objects are generated based on the sensor data, wherein each first data object comprises at least one timestamp and one location specification of an object detected by at least one of the sensors, and a validation function. At least one model object, which represents a parameterization of a trajectory of one of the movable objects with at least one variable parameter, is provided. Then, a correspondence value is calculated for the first data objects, for an assignment of the respective data object to at least one of the model objects provided by way of evaluating the validation function of the data object for the model objects. An additional model object is provided and the data object is assigned to the additional model object if none of the correspondence values lie in a predetermined range. The data object is assigned to the model object provided that corresponds to the best correspondence value if at least one of the correspondence values lies in the predetermined range. A numerical optimization of a target function for the model object to which the data object was assigned is performed by varying the at least one variable parameter of the model object, wherein the target function is a function of parameter adaptation functions of at least some of the data objects assigned to the model object. Finally, the at least one variable parameter of this model object is updated. ## Claims ### Claim 1 A method for determining trajectories of movable physical objects in a space on the basis of sensor data from a plurality of sensors, comprising the following steps: a) generating first data objects based on the sensor data, wherein each first data object comprises at least one timestamp and one location specification of an object detected by at least one of the sensors, and a validation function; b) providing at least one model object, which represents a parameterization of a trajectory of one of the movable objects with at least one variable parameter; for the first data objects: c) calculating a correspondence value for an assignment of the data object to at least one of the model objects provided by way of evaluating the validation function of the data object for the model objects; d) providing an additional model object and assigning the data object to the additional model object if none of the correspondence values lie in a predetermined range; e) assigning the data object to the model object provided that corresponds to the best correspondence value if at least one of the correspondence values lies in the predetermined range; and f) performing a numerical optimization of a target function for the model object to which the data object was assigned, by varying the at least one variable parameter of the model object, wherein the target function is a function of parameter adaptation functions of at least some of the data objects assigned to the model object, and updating the at least one variable parameter of this model object. ### Claim 2 (depends on 1) The method of claim 1, wherein the validation function of the first data objects supplies a value which is a measure for a probability that a considered one of the model objects corresponds to a physical object in the observed space, which physical object is represented by the data of the data object. ### Claim 3 (depends on 1) The method of claim 1, wherein the sensor data are radar data, in particular distance and angle information from a plurality of search radars that are spaced apart in space. ### Claim 4 (depends on 1) The method of claim 1, wherein second data objects are provided and assigned to one or more model objects, wherein these second data objects represent previously known information, in particular in respect of realistic whereabouts and/or suspected future events, or boundary conditions in relation to possible trajectories, in particular maximum speeds or accelerations. ### Claim 5 (depends on 4) The method of claim 4, wherein the validation function of at least one of the second data objects supplies a value which is a measure for a probability that a considered one of the model objects corresponds to a physical object in the observed space, which physical object is compatible with the data from the second data object. ### Claim 6 (depends on 1) The method of claim 1, wherein model-assisted mapping to temporal and/or spatial specifications is applied for evaluating the validation function of the data object for the model objects and for calculating at least one of the parameter adaptation functions, wherein the employed model models interference effects which influence the sensor data detected by the corresponding sensor. ### Claim 7 (depends on 6) The method of claim 6, wherein the model-assisted mapping generates two or more images in order to detect interference effects which lead to a multiplication of signals of a physical object to be detected. ### Claim 8 (depends on 1) The method according to claim 1, wherein the target function comprises a function for the numerical optimization which links sensor data representing different times from the same sensor, wherein the function is defined in such a way that unchanging influences of systematic errors on the sensor data of the various times are preferred in the numerical optimization. ### Claim 9 (depends on 1) The method of claim 1, wherein a reference is transferred to the respective model object for calculating the correspondence values of the relevant data objects such that the data of the model object are accessible to the validation function of the data object. ### Claim 10 (depends on 1) The method of claim 1, wherein the model objects comprise further parameters, which describe properties of the modeled object, in particular represent an object type and/or specify physical properties of the object. ### Claim 11 (depends on 1) The method of claim 1, wherein conditioning of the target function of the optimization is carried out for the numerical optimization of the correspondence value by virtue of the parameter adaptation function of at least one of the first data objects being modified in such a way that, independently of the unmodified parameter adaptation function, a gradient of the modified parameter adaptation function has a magnitude in a predetermined range, except for in a region of the parameter space corresponding to a spatial region which surrounds the location that is expected on the basis of the data object. ### Claim 12 (depends on 11) The method of claim 11, wherein the conditioning comprises the following steps: a) cutting off an unmodified parameter adaptation function along a boundary which corresponds to a lower bound of a probability of presence; b) complementing the resulting function by an auxiliary function such that the gradient of the modified parameter adaptation function has the magnitude in the predetermined range, except for in a region within the boundary. ### Claim 13 (depends on 11) The method of claim 11, wherein, except for a first constant term, the modified parameter adaptation function is substantially proportional to the logarithm of a probability of presence at the corresponding location at locations at which an object was present with a high probability at the considered time on the basis of the data object and it is such that, except for a second constant term, the modified validation function at locations with a low probability of presence is at least approximately proportional to the distance between said location and a location with a highest probability of presence. ### Claim 14 (depends on 11) The method of claim 11, wherein, in the region of the parameter space which corresponds to the spatial region that surrounds the expected location on the basis of the data object, the modified parameter adaptation function represents a quadratic form of the parameters, which are to be optimized numerically, of the parameter adaptation function and in that the magnitude of the gradient of the modified parameter adaptation function outside of this region is constant or monotonically decreasing with increasing distance from the region. ### Claim 15 (depends on 14) The method of claim 14, wherein the modified parameter adaptation function Pmod({right arrow over (x)}) for conditioning the target function is constructed as follows: P mod  ( x → ) = R  ( x → ) + 1 W  - log  ( P   D   F ′  ( x → ) ) , where PDF({right arrow over (x)}) is a probability density function and the following applies to R({right arrow over (x)}): a) R({right arrow over (x)}) is proportional to −log(PDF′({right arrow over (x)})) within the region of the parameter space which corresponds to the spatial region that surrounds the expected location on the basis of the data object; R({right arrow over (x)}) is constant outside of the region of the parameter space which corresponds to the spatial region that surrounds the expected location on the basis of the data object, and where W is a predeterminable parameter. ### Claim 16 (depends on 15) The method of claim 15, wherein R({right arrow over (x)})=−2 log(PDF′({right arrow over (x)}) for −2 log(PDF′({right arrow over (x)})