Receiver autonomous integrity monitoring based on particle swarm optimization particle filter
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摘要:
接收机自主完好性监测（RAIM）是航空卫星导航接收机必不可少的功能，为保持全球卫星导航系统（GNSS）在卫星发生故障时系统性能不降级，需要对卫星故障进行检测和隔离。针对接收机观测噪声非高斯分布的特点，提出一种基于粒子群优化粒子滤波（PSO-PF）的故障检测和隔离算法。通过粒子群优化粒子滤波对状态估计进行一致性检验实现故障检测。采集实测数据验证算法的检测性能，并与基于基本粒子滤波的完好性监测算法进行比较，结果表明：本文所提算法在非高斯测量噪声下可检测并隔离全球定位系统（GPS）故障卫星，其性能优于基于基本粒子滤波的完好性监测算法性能，对研究北斗卫星导航系统（BDS）接收机自主完好性监测具有一定的意义。
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关键词:
- 全球定位系统(GPS) /
- 接收机自主完好性监测(RAIM) /
- 粒子滤波(PF) /
- 故障检测 /
- 粒子群优化(PSO) /
- 北斗卫星导航系统(BDS)
Abstract:Receiver autonomous integrity monitoring (RAIM) is an inseparable part of aviation satellite navigation receiver. Failures or faults due to malfunctions in the global navigation satellite system (GNSS) should be detected and isolated to keep the integrity of the GNSS intact. Because measurement noise does not follow the Gaussian distribution perfectly, a fault detection and exclusion algorithm using the particle swarm optimization particle filter (PSO-PF) was proposed. Failure detection was undertaken by checking the consistency. Through the measured data, the proposed algorithm was compared with that based on PF. The results show that under the condition of non-Gaussian measurement noise, the effectiveness of the proposed approach is illustrated in a problem of global positioning system (GPS) RAIM. Moreover, the performance of the proposed algorithm is better than that based on PF. Meanwhile, the results are instructive for the study of the autonomous integrity monitoring of BeiDou navigation satellite system (BDS).
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图 2 偏差为15 m时累加LLR值和故障检测判决函数比较
Figure 2. Cumulative LLR and comparison of decision function for fault detection under 15 m step failure
图 3 偏差为20 m时累加LLR值和故障检测判决函数比较
Figure 3. Cumulative LLR and comparison of decision function for fault detection under 20 m step failure
图 4 偏差为30 m时累加LLR值和故障检测判决函数比较
Figure 4. Cumulative LLR and comparison of decision function for fault detection under 30 m step failure
表 1 PSO-PF RAIM算法和PF RAIM算法的故障检测告警时刻比较
Table 1. Comparison of alarm time for fault detection for PSO-PF RAIM algorithm and PF RAIM algorithm
伪距偏差/m 告警时刻/s PSO-PF RAIM算法 PF RAIM算法 15 510 518 20 505 514 30 505 509 
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表 2 不同偏差下PSO-PF RAIM算法和PF RAIM算法的平均有效粒子数(N=50)
Table 2. Average effective number of particles under different bias for PSO-PF RAIM algorithm and PF RAIM algorithm (N=50)
伪距偏差/m 平均有效粒子数 PSO-PF RAIM算法 PF RAIM算法 15 43.8 42.1 20 44.7 42.0 30 44.6 42.8
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