对于图像检测中车辆的多目标追踪，使用Kalman滤波器来求取被追踪的多目标的速度和加速度，定义一个函数使用python代码演示给我看

Kalman滤波器是一种用于处理信号和测量器数据的广泛使用的数学模型，它可以有效地估计未知参数和未知状态。下面是一个使用Python实现Kalman滤波器的示例代码：import numpy as npclass KalmanFilter(object):
def __init__(self, process_variance, estimated_measurement_variance):
self.process_variance = process_variance
self.estimated_measurement_variance = estimated_measurement_variance
self.posteri_estimate = 0.0
self.posteri_error_estimate = 1.0 def input_latest_noisy_measurement(self, measurement):
priori_estimate = self.posteri_estimate
priori_error_estimate = self.posteri_error_estimate + self.process_variance blending_factor = priori_error_estimate / (priori_error_estimate + self.estimated_measurement_variance)
self.posteri_estimate = priori_estimate + blending_factor * (measurement - priori_estimate)
self.posteri_error_estimate = (1 - blending_factor) * priori_error_estimateif __name__ == "__main__":
measurement = 1
process_variance = 1e-5
estimated_measurement_variance = 0.1
kalman_filter = KalmanFilter(process_variance, estimated_measurement_variance)
for iteration in range(1, 10):
kalman_filter.input_latest_noisy_measurement(measurement)
print("Iteration {}: {}".format(iteration, kalman_filter.posteri_estimate))