Tracking device, tracking method, and non-transitory computer readable storage medium
A target object is tracked based on dimensional information of an observation space including a relative velocity of the target object with respect to a sensing device for observing a reflection wave from the target object. An estimation state is acquired by estimating a state of the target object at a specific time. A reflection source of the reflection wave that gives an observation point observed at the specific time is assumed with respect to the estimation state. A state validity of the estimation state is acquired. The estimation state is updated 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.
1 . A tracking device 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 for observing a reflection wave from the target object, the tracking device comprising:
at least one processor with a memory storing computer program code, wherein:
the at least one processor with the memory is configured to cause the tracking device to execute:
acquiring an estimation state by estimating a state of the target object at a specific time;
identifying—therefor, based on the estimation state, a candidate reflection source of the reflection wave that gives an observation point observed at the specific time;
acquiring a state validity, which is a validity of the estimation state; and
updating the estimation state based on the state validity expressed including a difference between dimensional information of the candidate reflection source projected onto the observation space in the estimation state and the observation information, wherein:
the acquiring of the estimation state 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 updating of the estimation state 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.
2 . 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:
at least one processor with a memory storing computer program code, wherein:
the at least one processor with the memory is configured to cause the tracking device to execute:
acquiring an estimation state by estimating a state of the target object at a specific time;
identifying—therefor, based on the estimation state, a plurality of candidate reflection sources of the reflection wave that gives an observation point observed at the specific time;
acquiring a state validity, which is a validity of the estimation state, based on an occurrence rate of each of the plurality of candidate reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of candidate reflection sources in the estimation state; and
updating the estimation state based on the state validity, wherein:
the acquiring of the estimation state 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 updating of the estimation state 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.
3 . The tracking device according to claim 2 , wherein:
the identifying of the plurality of candidate reflection sources of the reflection wave includes identifying a clutter reflection source, which is identified to be one of the plurality of candidate reflection sources due to clutter; and
the acquiring of the state validity includes setting the occurrence rate of the clutter reflection source to be lower as a reflection intensity of the reflection wave at the observation point is higher.
4 . The tracking device according to claim 2 , wherein:
the identifying of the plurality of candidate reflection sources of the reflection wave includes identifying a closest reflection source, which is identified to be one of the plurality of candidate reflection sources at a closest position on an outer periphery of the estimation state; and
the acquiring of the state validity includes setting the occurrence rate of the closest reflection source to be higher as a reflection intensity of the reflection wave at the observation point is higher.
5 . The tracking device according to claim 4 , wherein:
the acquiring of the state validity includes setting the occurrence rate of the closest reflection source in a case where the closest reflection source is identified to be disposed on a sensing device side of the outer periphery to be higher than the occurrence rate of the closest reflection source in a case where the closest reflection source is identified to be disposed on a side opposite to the sensing device of the outer periphery.
6 . The tracking device according to claim 4 , wherein:
the identifying of the plurality of candidate reflection sources of the reflection wave includes identifying a symmetrical reflection source identified to be one of the plurality of candidate reflection sources at a position symmetrical to the closest position on the outer periphery; and
the acquiring of the state validity includes setting the occurrence rate of the symmetrical reflection source to be higher as a reflection intensity of the reflection wave at the observation point is higher.
7 . The tracking device according to claim 6 , wherein:
the acquiring of the state validity includes setting the occurrence rate of the symmetrical reflection source in a case where the symmetrical reflection source is identified to be disposed on a sensing device side of the outer periphery to be higher than the occurrence rate of the symmetrical reflection source in a case where the symmetrical reflection source is identified to be disposed on a side opposite to the sensing device of the outer periphery.
8 . The tracking device according to claim 2 , wherein:
the identifying of the plurality of candidate reflection sources of the reflection wave includes identifying an internal reflection source identified to be one of the candidate reflection sources within the outer periphery when the observation point is disposed within the outer periphery of the estimation state.
9 . The tracking device according to claim 2 , wherein:
the acquiring of the state validity includes merging a numerical number of Gaussian distributions constituting the Gaussian mixture model of the posterior distribution below a number of the plurality of candidate reflection sources.
10 . The tracking device according to claim 2 , wherein:
the sensing device is a radar whose reflection wave is a millimeter wave.
11 . 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 identifying, based on the estimation state, a candidate reflection source of the reflection wave that gives an observation point observed at the specific time;
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 candidate 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.
12 . 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 identifying, based on the estimation state, a plurality of candidate reflection sources of the reflection wave that gives an observation point observed at the specific time;
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 candidate reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of candidate 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.
13 . 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 include:
an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
an assumption step for identifying, based on the estimation state, a candidate reflection source of the reflection wave that gives an observation point observed at the specific time;
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 candidate 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.
14 . 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 include:
an estimation step for acquiring an estimation state by estimating a state of the target object at a specific time;
an assumption step for identifying, based on the estimation state, a plurality of candidate reflection sources of the reflection wave that gives an observation point observed at the specific time;
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 candidate reflection sources in the estimation state and an assumption validity, which is a validity of each of the plurality of candidate 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.