Shuxin Chen

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor

The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers’ misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers.

Stochastic Feedback Based Continuous-Discrete Cubature Kalman Filtering for Bearings-Only Tracking

Bearings-only tracking only adopts measurements from angle sensors to realize target tracking, thus, the accuracy of the state prediction has a significant influence on the final results of filtering. There exist unpredictable approximation errors in the process of filtering due to state propagation, discretization, linearization or other adverse effects. The idea of online covariance adaption is proposed in this work, where the post covariance information is proved to be effective for the covariance adaption. With theoretical deduction, the relationship between the posterior covariance and the priori covariance is investigated; the priori covariance is modified online based on the feedback rule of covariance updating. The general framework integrates the continuous-discrete cubature Kalman filtering and the feedback rule of covariance updating. Numerical results illustrated that the proposed method has advantages over decreasing unpredictable errors and improving the computational accuracy and efficiency.

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

The optimal maneuver of observer for bearing-only tracking (BOT) in a threat environment is a complex problem which involves nonlinear filtering, threat avoidance, and optimal maneuver strategy. Under comprehensive consideration, the reward function comprised of the lower bound on detFIM and threat cost was established; the finite-horizon MDP principle was applied to obtain the optimal strategy. The quantization method was used to discretize the BOT process and calculate the transition matrix of Markov chain; to achieve quantization in the beginning of each period, CKF was applied to provide the initial state estimate and the corresponding error covariance. The numerical simulations illustrated the applicability and superior performance for static and dynamic target tracking in several scenarios in the threat environment.

A novel power control approach of multiple GNSS spoofing signals

In terms of the objective receiver being interfered by multiple Global Navigation Satellite System (GNSS) spoofing signals, the problem still exists that the performance of the other spoofing signals is quite poor since the power of a certain path signal among multiple spoofing signals increases. To this end, a novel power control scheme was proposed. First of all, the influence of multiple spoofing power on noise floor was analyzed based on cross-correlation interference of different Pseudo-Random Noise(PRN) codes, along with the performance of acquiring each spoofing branch with the prerequisite for this noise floor. Then an objective function was constructed and the genetic algorithm was utilized to obtain the optimized distribution of spoofing power. The simulation results indicate that the proposed approach can obviously improve the performance of acquiring each spoofing signal compared to the authentic signals on the condition of noise floor increasing less than 10dB. In addition, the proposed approach paves the way for many actual applications in theory.

A power distributing strategy of GNSS spoofing signal under dual-threshold

The traditional GNSS spoofing signals power distribution strategy only considers availability and validity in single channel mode without multiple channel mode. Therefore, a new strategy based on acquisition and tracing performance threshold is proposed. The relationship between the signal acquisition/tracking performance and carrier-to-noise ratio (C/N0) is analyzed. Then the influence of spoofing signal power on C/N0 is also analyzed through taking cross-correlation interference into consideration. The optimal spoofing power is calculated by the strategy under the C/N0 constraints and corresponding objective functions to different spoofing signal detecting. Simulations show that spoofing signal injection will be realized if the multiple spoofing power is −143.8dBW and denial power is 158.86dBW or the minimum elevation spoofing power is −124.04dBW. The concealment of interference is enhanced when it deals with the wideband white noise detection.

Robust bearings-only tracking algorithm using structured total least squares-based Kalman filter

A nonlinear approach called the robust structured total least squares kalman filter (RSTLS-KF) algorithm is proposed for solving tracking inaccuracy caused by outliers in bearings-only multi-station passive tracking. In that regard, the robust extremal function is introduced to the weighted structured total least squares (WSTLS) location criterion, and then the improved Danish equivalent weight function is built on the basis, which can identify outliers automatically and reduce the weight of the polluted data. Finally, the observation equation is linearized according to the RSTLS location result with the structured total least norm (STLN) solution. Hence location and velocity of the target can be given by the Kalman filter. Simulation results show that tracking performance of the RSTLS-KF is comparable or better than that of conventional algorithms. Furthermore, when outliers appear, the RSTLS-KF is accurate and robust, whereas the conventional algorithms become distort seriously.