Vehicle tracking method, computer readable storage medium, and electronic device

A non-transitory computer readable storage medium, wherein a computer program is stored thereon, and the computer program implements a vehicle tracking method when being executed by a processor, wherein the method comprises:

• determining a predicted value of state information of a target tracking vehicle based on a sequential Kalman filtering model according to a most recently acquired tracking value of the state information of the target tracking vehicle, wherein the state information at least comprises location information;
• obtaining a detected value of the state information of the target tracking vehicle;
• and
• in response to the obtained detected value, acquiring a new tracking value of the state information of the target tracking vehicle based on the sequential Kalman filtering model according to the detected value and the most recently determined predicted value;
• wherein the method further comprises:
• determining a first estimation difference between the new tracking value and the detected value, and a second estimation difference between the new tracking value and the most recently determined predicted value respectively;
• and
• determining a gain matrix of the sequential Kalman filtering model according to the first estimation difference and the second estimation difference so as to update the sequential Kalman filtering model.

An electronic device, comprising:

• a memory, wherein a computer program is stored thereon;
• and
• a processor, configured to execute the computer program in the memory to implement a vehicle tracking method comprising:
• determining a predicted value of state information of a target tracking vehicle based on a sequential Kalman filtering model according to a most recently acquired tracking value of the state information of the target tracking vehicle, wherein the state information at least comprises location information;
• obtaining a detected value of the state information of the target tracking vehicle;
• and
• in response to the obtained detected value, acquiring a new tracking value of the state information of the target tracking vehicle based on the sequential Kalman filtering model according to the detected value and the most recently determined predicted value;
• wherein the method further comprises:
• determining a first estimation difference between the new tracking value and the detected value, and a second estimation difference between the new tracking value and the most recently determined predicted value respectively;
• and
• determining a gain matrix of the sequential Kalman filtering model according to the first estimation difference and the second estimation difference so as to update the sequential Kalman filtering model.