The health monitoring and damage prognosis of aerospace hotspots is important for reducing maintenance costs and increasing in-service capacity of aging aircraft. One of the leading causes of structural failure in aerospace vehicles is fatigue damage. Based on the physical mechanism of damage nucleation and growth, a physics-based multiscale model is considered for fatigue damage assessment in metallic aircraft structures. A guided-wave based sensing approach is utilised to enable effective damage detection in a common structural hotspot: a lug joint. Finite element analysis is carried out with piezoelectric wafers bonded to the host structure and the simulated sensor signals are analysed. A damage classification strategy is developed, which integrates physically motivated time-frequency approaches with advanced stochastic modelling techniques. In particular, a variational Bayesian learning scheme is used to estimate the optimal model complexity automatically from the data, adapting the classifier for real-time use. Classification performance is studied as a function of signal-to-noise ratio and results are reported for the detection of fatigue crack damage in the lug joint. An adaptive hybrid prognosis model is proposed, which estimates the residual useful life of structural hotspots using damage condition information obtained in real-time.

Fatigue crack growth during the service life of aging aircraft is a critical issue and monitoring of such cracks in structural hotspots is the goal of this research. This paper presents a procedure for classification and detection of cracks generated in bolted joints which are used at numerous locations in aircraft structures. Single lap bolted joints were equipped with surface mounted piezoelectric (pzt) sensors and actuators and were subjected to cyclic loading. Crack length measurements and sensor data were collected at different number of cycles and with different torque levels. A classification algorithm based on Support Vector Machines (SVMs) was used to compare signals from a healthy and damaged joint to classify fatigue damage at the bolts. The algorithm was also used to classify the amount of torque in the bolt of interest and determine if the level of torque affected the quantification and localization of the crack emanating from the bolt hole. The results show that it is easier to detect the completely loose bolt but certain changes in torque, combined with damage, can produce some non-unique classifier solutions.

We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.

Recent advances in sensor technology and information processing afford a new flexibility in the design of waveforms for agile sensing. Sensors are now developed with the ability to dynamically choose their transmit or receive waveforms in order to optimize an objective cost function. This has exposed a new paradigm of significant performance improvements in active sensing: dynamic waveform adaptation to environment conditions, target structures, or information features. The manuscript provides a review of recent advances in waveform-agile sensing for target tracking applications. A dynamic waveform selection and configuration scheme is developed for two active sensors that track one or multiple mobile targets. A detailed description of two sequential Monte Carlo algorithms for agile tracking are presented, together with relevant Matlab code and simulation studies, to demonstrate the benefits of dynamic waveform adaptation. The work will be of interest not only to practitioners of radar and sonar,...