文章针对高斯-牛顿迭代法和UofC(University of Calgary)模型,采用了联合高斯-牛顿迭代法和UofC模型的定位解算方法。对算法用GPS/BDS组合系统进行实测数据处理,分别与高斯-牛顿迭代法和采用UofC模型的定位解算方法做了比较。对比和分...文章针对高斯-牛顿迭代法和UofC(University of Calgary)模型,采用了联合高斯-牛顿迭代法和UofC模型的定位解算方法。对算法用GPS/BDS组合系统进行实测数据处理,分别与高斯-牛顿迭代法和采用UofC模型的定位解算方法做了比较。对比和分析了伪距联合单点定位算法在GPS、BDS、GPS/BDS组合系统三种模式下的单点定位精度。结果表明伪距联合单点定位算法在GPS/BDS组合系统中的单点定位精度有明显提高。展开更多
There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of track...There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.展开更多
文摘文章针对高斯-牛顿迭代法和UofC(University of Calgary)模型,采用了联合高斯-牛顿迭代法和UofC模型的定位解算方法。对算法用GPS/BDS组合系统进行实测数据处理,分别与高斯-牛顿迭代法和采用UofC模型的定位解算方法做了比较。对比和分析了伪距联合单点定位算法在GPS、BDS、GPS/BDS组合系统三种模式下的单点定位精度。结果表明伪距联合单点定位算法在GPS/BDS组合系统中的单点定位精度有明显提高。
基金supported in part by the National Key Research and Development Plan,China(No.2017YFB1301101)the National Natural Science Foundation of China(Nos.61673317 and 61673313)。
文摘There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.
