Power inversion algorithms (PI) are widely used by ant-jamming receivers in harsh environment, but it also induces pseudo-range measurement error which is fatal to high precision receivers. In this paper, it proposes a more accurate method based on the S Curve Bias (SCB) to evaluate the pseudo-range bias which can actually affect the positioning results, as it excludes a fixed time delay contained in the directly differing method. When using this method to assess the bias, it is found that pseudo-range errors can be influenced by many factors including the type of interference. Simulation as well as experiment based on a 4-element array antenna are designed and carried out. The simulation has shown that both SOP-PI and STAP-PI will bring bias to pseudo-range measurement. Specifically, the maximum bias caused by SOP-PI is 1m while the bias caused by STAP-PI is up to 10m. Moreover, comparing to the method which directly differs the pseudo-range of output signal and the reference signal, the method proposed in this paper has better performance. Furthermore, the experimental results verify the effectiveness of the proposed evaluating method.

The pseudolite system can be used to provide positioning and timing service for users in a specific area. In order to provide better positioning and timing service, a good geometric configuration needs to be formed for the pseudolite system. For the problem of pseudolite system deployment, the average and mean square values of geometric dilution of precision (GDOP) are optimized in this paper for users in a target area. We proposed a space-based pseudolite deployment method based on GDOP geometry, starting from the minimum GDOP value for users in the central area. From the simulation results, it can be seen that, compared with the empirical method and the NSGA-II method, the method in this paper has the smallest average GDOP, a better robustness, and a higher positioning accuracy in the target area, through which a pseudolite deployment proposal can be quickly obtained.

When a pseudolite is used to provide positioning services to users, it needs to provide position information by broadcasting ephemeris parameters. As part of resilient positioning, navigation, and timing (PNT), a UAV pseudolite system can serve users independently as a GNSS backup system, but unlike satellites that move periodically, the motion of UAVs is often variable, so it brings difficulties for how to make accurate predictions of the UAV's trajectory in a future segment within the ephemeris parameter update period. Therefore, based on a cruise UAV, we proposed an ephemeris based on a prediction method combined with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks. The true position is divided into positions on the intended route and their deviations by mapping, and we use the motion model, the Singer model with Kalman filter and the CNN-LSTM networks to predict the position. Moreover, the network updates are achieved by a network monitoring loop and adaptive adjustment of the parameter update period is achieved by a prediction accuracy monitoring loop. Moreover, the ephemeris parameters are obtained by fitting the prediction results. The experimental results verify the performance of the designed ephemeris, the root mean squared error (RMSE) value of ephemeris parameter characterization error is less than 5 m, and the average occupied bits is about 35 bits; even using the sample data of UAV maneuvering at extreme speed with poor control performance, the RMSE of ephemeris parameter characterization error is 6.56 m, and the average occupied bits is 69.0 bits.

The next‐generation navigation tools are developed to enable precision and reliability. Military users are more likely to encounter jamming, so it is necessary to seek the technologies which can steadily provide high‐precision positioning services in all‐weather and harsh conditions. However, when users use an array antenna for interference suppression, it usually introduces pseudorange and carrier phase biases, which deteriorates the accuracy and integrity of positioning. To address this issue, the authors propose a blind anti‐interference algorithm with biases suppression. Compared to the commonly used blind space–time adaptive anti‐jamming algorithms, this algorithm can not only retain the advantages of the simple structure of the blind anti‐jamming algorithm but also suppress pseudorange biases and carrier phase biases, both of which are crucial for users to obtain the availability of positioning. Moreover, the algorithm proposed can be applied easily and quickly to the actual high‐precision positioning system with minor hardware changes in some cases. The relevant theoretical analysis and experiments have proved the effectiveness of the proposed algorithm: (1) code phase biases and carrier phase biases can almost be ignored and (2) it also can significantly improve the accuracy and integrity of positioning; the average positioning error is 1.6 mm, and it can keep the positioning accuracy better than 0.1 m for 97.5% of the time.