[{"createTime":1735734952000,"id":1,"img":"hwy_ms_500_252.jpeg","link":"https://activity.huaweicloud.com/cps.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=V1g3MDY4NTY=&utm_medium=cps&utm_campaign=201905","name":"华为云秒杀","status":9,"txt":"华为云38元秒杀","type":1,"updateTime":1735747411000,"userId":3},{"createTime":1736173885000,"id":2,"img":"txy_480_300.png","link":"https://cloud.tencent.com/act/cps/redirect?redirect=1077&cps_key=edb15096bfff75effaaa8c8bb66138bd&from=console","name":"腾讯云秒杀","status":9,"txt":"腾讯云限量秒杀","type":1,"updateTime":1736173885000,"userId":3},{"createTime":1736177492000,"id":3,"img":"aly_251_140.png","link":"https://www.aliyun.com/minisite/goods?userCode=pwp8kmv3","memo":"","name":"阿里云","status":9,"txt":"阿里云2折起","type":1,"updateTime":1736177492000,"userId":3},{"createTime":1735660800000,"id":4,"img":"vultr_560_300.png","link":"https://www.vultr.com/?ref=9603742-8H","name":"Vultr","status":9,"txt":"Vultr送$100","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":5,"img":"jdy_663_320.jpg","link":"https://3.cn/2ay1-e5t","name":"京东云","status":9,"txt":"京东云特惠专区","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":6,"img":"new_ads.png","link":"https://www.iodraw.com/ads","name":"发布广告","status":9,"txt":"发布广告","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":7,"img":"yun_910_50.png","link":"https://activity.huaweicloud.com/discount_area_v5/index.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=aXhpYW95YW5nOA===&utm_medium=cps&utm_campaign=201905","name":"底部","status":9,"txt":"高性能云服务器2折起","type":2,"updateTime":1735660800000,"userId":3}]
可以理解为现代控制里的状态反馈，
2、卡尔曼滤波追踪
5个基本的公式，这里直接给出公式：
公式1：X(k|k-1) = FX(k-1 | k-1) + BU(k) + W(k)
公式2：P(k|k-1) = FP(k-1|k-1)F’ + Q(k)
公式3：X(k|k) = X(k|k-1) + Kg(k)[Z(k) - AX(k|k-1)
公式4：Kg(k) = P(k|k-1)A’/{AP(k|k-1)A’ + R} //卡尔曼增益
公式5：P(k|k) = (1- Kg(k) H) P(k|k-1)
另外，Z(k) = HX(k) +
V，Z是测量值，X是系统值，W是过程噪声，V是测量噪声，H是测量矩阵，A是转移矩阵，Q是W的协方差，R是V的协方差，X(k|k-1)是估计值；X(k|k)是X(k|k-1)的最优估计值，即滤波估计值；P(k|k-1)是估计值误差方差矩阵，P(k|k)是滤波误差方差矩阵。
2.卡尔曼滤波器：
卡尔曼滤波被广泛应用于无人机、自动驾驶、卫星导航等领域，简单来说，其作用就是基于传感器的测量值来更新预测值，以达到更精确的估计。
在目标跟踪中，需要估计track的以下两个状态：
均值(Mean)：表示目标的位置信息，由bbox的中心坐标 (cx, cy)，宽高比r，高h，以及各自的速度变化值组成，由8维向量表示为 x = [cx,
cy, r, h, vx, vy, vr, vh]，各个速度值初始化为0。
协方差(Covariance )：表示目标位置信息的不确定性，由8x8的对角矩阵表示，矩阵中数字越大则表明不确定性越大，可以以任意值初始化。
卡尔曼滤波分为两个阶段：(1) 预测track在下一时刻的位置，(2) 基于detection来更新预测的位置。
