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Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS, and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably.

This article aims at improving positioning precision and stability of the SINS/Doppler/Platform Compass integrated navigation system. Firstly, based on considering of the characteristic and performance of the integrated system, the system model is established. And the discrete state and measurement equations are achieved. Secondly, traditional Kalman filter for this system is usually unstable, and easy to divergent. So reasons for divergence of integrated navigation with traditional Kalman filter is analyzed. Thirdly, Sage-Husa Kalman filter based on UD decomposition is designed. And some improvements have been made according to the analysis results. Finally, several simulation experiments were made to test the effect of the improved algorithms. The simulation results indicate that improved Sage-Husa Kalman filter based on UD decomposition can guarantee the precision and stability of the SINS/Doppler/Platform Compass integrated navigation system.

In the initialization process of the strapdown initial navigation system (SINS) of a launched weapon before its release, transfer alignment (TA) uses the data from the aircraft SINS or other navigation aids. However, stochastic measurement aid information is unreliable in practical integrated navigation system. The paper is the filtering stability for a class of nonlinear stochastic systems with intermittent measurements when the arrival of the observations is described by Bernoulli independent and identically distributed process. The stochastic stability of the a novel Gaussian filter through presenting Gaussian approximate about one step posterior predictive probability density function (PDF) of the state and intermittent measurements is investigated. The estimation errors remain bounded if the system satisfies some sufficient conditions, and its error covariance matrices are statistically convergent by providing the existence of a super-threshold value for the intermittent measurement probability. Finally, a nonlinear integrated navigation method based on the proposed filter is presented, and the ground vehicle test of missile-board SINS is given to illustrate the effectiveness of the investigated techniques.

In designing BP neural network, it is difficult to determine the network parameters and weights, or to achieve the best effect under random perturbation, and impossible to satisfy the real-time requirement of system. To solve these problems, this paper introduces an evolutionary neural network and puts forward some improvements on conventional evolutionary programming. This algorithm is then used in the integrated navigation system. Experiment results indicate that BP neural network based on evolutionary programming can not only overcome the shortcomings of BP artificial neural network, but also avoid problems of genetic algorithms caused by binary-coded and cross operation. Also the navigation system based on this algorithm can get rid of the problems of kalman filter when the outside observation data are unreliable. Finally, the normal operation of kalman filter is ensured with a higher accuracy.

Poor environment of ship-based navigation system usually leads to decrease in navigation precision and even leads to divergence. BP neural network is introduced to solve this problem. According to characteristics of the navigation system, conventional BP algorithm is improved. And the improved algorithm is used in the ship-based navigation system. The simulation results show that the algorithm can overcome the problem of the navigation system, and ensure the normal operation of the filter.

There are matters of non-linear, computing errors, dimension tragedy, and model errors in conventional data fusion algorithms in integrated navigation. Fuzzy neural networks based on genetic algorithms not only has the capacity of the fuzzy neural networks, such as expressing approximate and qualitative knowledge, study, and expressing non-linear, but also has the competence of fast global searching of the genetic algorithms. So fuzzy neural networks based on genetic algorithms can be treated as an effective data fusion algorithms. And it was used in integrated navigation in this paper; the simulation results testified its validity.