Tracking device, tracking method, and non-transitory computer readable storage medium

A tracking device for tracking a target object based on observation information from a sensing device that observes a reflection wave from the target object, the tracking device comprising:

• an estimation unit that acquires an estimation state by estimating a state of the target object at a specific time;
• an assumption unit that assumes a plurality of reflection sources of the reflection wave that gives an observation point observed at the specific time, with respect to the estimation state;
• a validity unit that acquires a state validity, which is a validity of the estimation state, based on an occurrence rate of each of the plurality of reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of reflection sources in the estimation state;
• and
• an update unit that updates the estimation state based on the state validity, wherein:
• the estimation unit acquires a prior distribution for the estimation state at the specific time by a Gaussian mixture model based on the estimation state updated at a past time with respect to the specific time;
• and
• the update unit updates a posterior distribution, for the estimation state at the specific time represented by a Gaussian mixture model including the state validity and the prior distribution, using an extended Kalman filter.

A tracking method executed by a processor for tracking a target object based on dimensional information of an observation space including a relative velocity of the target object with respect to a sensing device as observation information of the sensing device that observes a reflection wave from the target object, the tracking method comprising:

• an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
• an assumption step for assuming a reflection source of the reflection wave that gives an observation point observed at the specific time, with respect to the estimation state;
• a validity step for acquiring a state validity that is a validity of the estimation state;
• and
• an update step for updating the estimation state based on the state validity expressed including a difference between dimensional information of the reflection source projected onto the observation space in the estimation state and the observation information, wherein:
• the estimation step further includes:
• acquiring a prior distribution for the estimation state at the specific time by a Gaussian mixture model based on the estimation state updated at a past time with respect to the specific time;
• and
• the update step further includes:
• updating a posterior distribution, for the estimation state at the specific time represented by a Gaussian mixture model including the state validity and the prior distribution, using an extended Kalman filter.

A tracking method executed by a processor for tracking a target object based on observation information from a sensing device that observes a reflection wave from the target object, the tracking method comprising:

• an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
• an assumption step for assuming a plurality of reflection sources of the reflection wave that gives an observation point observed at the specific time, with respect to the estimation state;
• a validity step for acquiring a state validity, which is a validity of the estimation state, based on an occurrence rate of each of the plurality of reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of reflection sources in the estimation state;
• and
• an update step for updating the estimation state based on the state validity, wherein:
• the estimation step further includes:
• acquiring a prior distribution for the estimation state at the specific time by a Gaussian mixture model based on the estimation state updated at a past time with respect to the specific time;
• and
• the update step further includes:
• updating a posterior distribution, for the estimation state at the specific time represented by a Gaussian mixture model including the state validity and the prior distribution, using an extended Kalman filter.

A non-transitory tangible computer readable storage medium comprising instructions for causing a processor to execute tracking a target object based on information in an observation space including a relative velocity of the target object with respect to a sensing device as observation information of the sensing device that observes a reflection wave from the target object, wherein:

• the instructions includes:
• an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
• an assumption step for assuming a reflection source of the reflection wave that gives an observation point observed at the specific time, with respect to the estimation state;
• a validity step for acquiring a state validity that is a validity of the estimation state;
• and
• an update step for updating the estimation state based on the state validity expressed including a difference between dimensional information of the reflection source projected onto the observation space in the estimation state and the observation information;
• the estimation step further includes:
• acquiring a prior distribution for the estimation state at the specific time by a Gaussian mixture model based on the estimation state updated at a past time with respect to the specific time;
• and
• the update step further includes:
• updating a posterior distribution, for the estimation state at the specific time represented by a Gaussian mixture model including the state validity and the prior distribution, using an extended Kalman filter.

A non-transitory tangible computer readable storage medium comprising instructions for causing a processor to execute tracking a target object based on observation information of a sensing device that observes a reflection wave from the target object, wherein:

• the instructions includes:
• an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
• an assumption step for assuming a plurality of reflection sources of the reflection wave that gives an observation point observed at the specific time, with respect to the estimation state;
• a validity step for acquiring a state validity, which is a validity of the estimation state, based on an occurrence rate of each of the plurality of reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of reflection sources in the estimation state;
• and
• an update step for updating the estimation state based on the state validity;
• the estimation step further includes:
• acquiring a prior distribution for the estimation state at the specific time by a Gaussian mixture model based on the estimation state updated at a past time with respect to the specific time;
• and
• the update step further includes:
• updating a posterior distribution, for the estimation state at the specific time represented by a Gaussian mixture model including the state validity and the prior distribution, using an extended Kalman filter.