Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance. Aiming to solve the spatial registration and track association problem in the case where incomplete measurements are provided by different sensors, this paper proposes a residual bias estimation registration (RBER) method based on maximum likelihood and the sequential m-best track association algorithm based on the new target density (SMBTANTD). The RBER method realizes the update of incomplete measurements by sequential filtering technology and eliminates the systematic bias of sensors by using information on the significant targets. The SMBTANTD method introduces a new target density in the correlation matrix, which effectively solves the association problem in the scenarios where the numbers of targets measured by multiple sensors are inconsistent. The reported simulation results demonstrate that the proposed algorithm can not only accurately estimate the systematic bias of the sensors, but also significantly improve the performance of track association.

A fast and automated technique is crucial for bearing faults diagnosis during operation. To circumvent the intricacies of signal spectrum analysis, a diagnostic method named the pulse signal-to-noise ratio (PSNR) test is proposed by exploiting the time-domain sparsity of fault signals under a constant angular rate, which are modeled as periodic pulses with consistent duty cycle and power. The algorithm employs a statistic called pulse signal-to-noise ratio to both identify faults and determine their location. A simplified variant of the PSNR test, named pulse signal-to-noise amplitude ratio (PSNAR) test, is further proposed for near multiplication-free fast diagnosis. Data from Machinery Failure Prevention Technology (MFPT) and Case Western Reserve University (CWRU) were used to verify the algorithm.

The synthetic aperture radar (SAR) system equipped on the hypersonic platform in the near space presents many advantages compared with the conventional airborne SAR. However, due to the complex aerodynamic configuration and flight characteristics of the hypersonic platform, the radar design is facing some new challenges. Taking into account the new features of the platform, in this study the authors analyse the conformal design requirements of the array antenna, as well as the prominent contradiction between the high resolution and wide swath of SAR. After establishing the model of the conformal array antenna and the space coordinate system of the far‐field radiation, a specific process of synthesising the antenna pattern is given. Furthermore, the antenna mask templates of optimising the antenna pattern are generated based on the main performance measures of the SAR system, and the modified meta particle swarm optimisation algorithm is exploited to obtain the optimal excitation phase of each array element in synthesising the ultimate antenna pattern. Finally, simulation results verify the effectiveness of the method, where the performance of the optimised antenna pattern is evaluated numerically.

In this work, a combined system for vehicle occupant detection and non-contact vital sign monitoring is proposed which can be used in the automotive industry. First, an occupant detection and radial range-azimuth positioning method based on frequency modulated continuous wave (FMCW) radar is applied. By separating the radar echoes of multiple life targets, the intermediate frequency (IF) signals containing vital sign micro-movement information of each passenger’s chest are extracted. And then the successive variational mode decomposition (SVMD) algorithm is used to decompose and reconstruct the breathing and heartbeat signals of each passenger. Furthermore, we propose an IF signal speed compensation method to eliminate the impact of random body movement (RBM) on vital signal extraction. Our system can successfully extract the breathing and heartbeat signals of multiple passengers in the cabin. The average estimation accuracy of the breathing rate (BR) and heart rate (HR) of a single target in different physical states at a distance of 1.5m can reach 91.6% and 91.9% respectively. And the proposed RBM correction algorithm can significantly improve the distortion of vital signals caused by radial RBM.

Accurate georegistration is required in multi-sensor data fusion, since even minor biases in spatial registration can result in large errors in the converted target geolocation. This paper addresses the problem of estimating and correcting sensor biases in target geolocation. Aiming to solve the spatial registration problem in the case where heterogeneous measurements are provided by mobile sensor (active or passive) platforms, this paper proposes a moving heterogeneous sensor registration (MDSR) algorithm based on maximum likelihood estimation. The MDSR algorithm decouples the offset biases from the attitude biases and updates heterogeneous measurements using linear minimum mean square error fusion. In particular, the MDSR algorithm is a batch algorithm that outputs estimates of the offset biases, attitude biases, and target location estimates, expressed in a common coordinate system. Calculation of the Cramér–Rao lower bound and conducting various simulation results demonstrate that the MDSR algorithm is effective and robust for moving heterogeneous sensors.

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.