Passive localization method has a wider applicable prospect as a rising localization resort with well flexibility. The acquisition of phase difference changing rate is a very practical problem in passive localization for the way how to get the phase difference, and a better error precision directly affects the accuracy of the localization. Firstly, based on the analysis of the positioning principle of phase difference changing rate, it studies the characteristics of phase difference changing rate and proposes a novel resolving phase ambiguity algorithm based on a double baselines system cosine theorem (DBSCT), which can eliminate the phase difference ambiguity efficiently. Secondly, it studies two typical methods to extract the high‐precision phase difference changing rate from the unambiguity phase difference, namely, difference algorithm and Kalman algorithm. Finally, it compares the Kalman algorithm with the difference algorithm through computer simulation. Simulation results show that the Kalman algorithm can efficiently smoothen the phase difference data and suppress the measurement noise to achieve a high positioning accuracy. Simulation results also show that with a certain phase difference measurement accuracy, the phase difference rate accuracy extracted by the Kalman algorithm is higher than that we require. Therefore, we can appropriately relax the phase difference measurement accuracy requirements to achieve the same positioning accuracy. So, it has a great application value.
[1]
Yinglin Wang,et al.
Practical Applications of Intelligent Systems
,
2012
.
[2]
Dong Wang,et al.
A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment
,
2016,
Sensors.
[3]
Yong Wang,et al.
An Improved Airborne Passive Location & Tracking Algorithm Using the Rate-of-change of Phase Difference: An Improved Airborne Passive Location & Tracking Algorithm Using the Rate-of-change of Phase Difference
,
2014
.
[4]
Zhu Xiao,et al.
Hybrid Cooperative Vehicle Positioning Using Distributed Randomized Sigma Point Belief Propagation on Non-Gaussian Noise Distribution
,
2016,
IEEE Sensors Journal.
[5]
Zhang Mi.
A Single Moving Observer Auto-Calibrated Localization Method Using LBI
,
2013
.
[6]
Taoyun Zhou,et al.
Single Observer Passive Location Using Phase Difference Rate
,
2014
.
[7]
Zhu Xiao,et al.
A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions
,
2018,
Inf. Fusion.