Multiple manoeuvring target tracking is an extremely difficult problem in the target tracking field, especially under the non-linear systems. The probability hypothesis density (PHD) and cardinalised PHD (CPHD) algorithms based on the particle filter have proved to be promising algorithms for multi-target tracking. However, they have a heavy computational burden because of the particle clustering in the stage of state extraction. Especially, the additional calculation is added to the CPHD algorithm because of the estimation of the cardinality distribution. To solve the problem, the authors propose a novel multiple manoeuvring target tracking algorithm by extending the multi-model method to the cardinality-balanced multi-target multi-Bernoulli filter and then using the sequential Monte Carlo implementation. Moreover, in order to obtain the individual target tracks, the particle labelling technique is introduced in the proposed algorithm. Simulation results show that the proposed algorithm can effectively achieve the track continuity for the multiple manoeuvring target tracking and has a higher accuracy of state estimates than the multiple model particle PHD and CPHD algorithm with a better computational efficiency.

Polarimetric calibration intercalibrating the polarisation channels of the radar system results in a better characterisation of the target and of the backscattering mechanisms. However, system distortion because of miscalibration may affect the classification performance to some extent. The study described in this article is an attempt at deriving the polarimetric calibration requirements on several algorithms for the classification of land features. In the monostatic backscattering case, the model representing the effects of the residual distortion of the calibrated polarimetric data is presented first, followed by analysing the effects of miscalibration on a few commonly used polarimetric parameters and several classification schemes. Experimental results show that polarimetric calibration requirements not only are strongly dependent on classification methods but also directly relate to the underlying physical scattering mechanisms of the observables. For each classification algorithm under study, a set of numbers summarising the polarimetric calibration goals concludes this study.

A model-based adaptive synthetic aperture radar (SAR) image formation algorithm is proposed in this study. A parametric model is used to formulate the received SAR signal, each scatterer can be characterised by a set of its parameters. In the model, the instantaneous slant range is approximated by a polynomial. The parameters of each scatterer are estimated by maximising the cost function, named matching degree indicator. The root-mean-square errors of parameter estimation are close to the Cramer–Rao bounds. The image is formed by mapping the estimated parameters onto the grid formed by iso-range and iso-Doppler lines. The algorithm does not require explicit knowledge of the data collection geometry. Experimental results are given to validate the algorithm.

Chaff centroid jamming released by vessel makes the target indicated azimuth angle of radar seeker of anti-ship missile bias off the target. The failure to detect the presence of chaff centroid jamming is catastrophic to target tracking for the radar seeker. In this study, the authors take the azimuthal in-phase monopulse ratio of radar seeker as test statistic and detect the presence of chaff centroid jamming with the aid of GPS/INS. Probability density functions of the azimuthal in-phase monopulse ratio when jamming is absent and present are derived at first. Then the authors propose a method using the measured information of both GPS/INS and radar seeker to estimate the target off-boresight azimuth angle which is vital for detection threshold setting. Finally, the authors show the performance of the chaff centroid jamming detection by simulations and analyse the effects of some key jamming parameters on detection performance.

Advanced and unexpected applications are recently raising new interest in Global Navigation Satellite System (GNSS) codeless techniques that can be used, for example, for signal authentication and spoofing detection. Moreover, the presence of a subcarrier in modern GNSS signals introduces new challenges and opportunities. To take advantage of modern signal structures, codeless processing needs to be modified to recover the subcarrier which can subsequently be used for signal quality monitoring. In this study, codeless tracking is modified for processing binary offset carrier (BOC) modulated signals where a subcarrier lock loop is used to remove the subcarrier component. The performance of the suggested architecture is thoroughly analysed and theoretical results are supported by Monte Carlo simulations. An open-loop, multi-correlator codeless architecture is also proposed to monitor the BOC subcarrier correlation function. The effectiveness of the proposed codeless framework is demonstrated using real data collected from the Galileo in-orbit validation satellites. The analysis supports the validity of the proposed architecture which enables quality monitoring of encrypted GNSS signals.

Pareto distribution has been introduced into the radar community recently as a suitable model for X-band high resolution maritime sea clutter returns. This intensity clutter model is a simple two parameter power law distribution. It is thus important to consider the development of constant false alarm rate detection processes for targets embedded within such clutter. It is shown that a simple functional transform can produce such detection schemes, whose false alarm probability and threshold are related through simple analytical expressions. These relationships are related intrinsically to Gaussian detection counterparts. Three detectors will be introduced and their performance analysed in both homogeneous and heterogeneous clutter environments. The effect of interfering targets in the training cells will also be examined.

A two-stage blind adaptive anti-jamming algorithm for global positioning systems (GPS) is proposed in this study. The new algorithm cancels interferences by projecting the array received signals on the orthogonal complement space of interferences. Then the algorithm utilises the CLEAN method to estimate direction of arrivals  of GPS signals according to the period repetitive feature of GPS coarse/acquisition (C/A) code and form the multi-beam, where each beam can point one GPS signal. Test data and simulation results show the effectiveness of the new algorithm.

The authors study the radar signature of a new type of wind turbine, named the Wind Lens. This design includes a flanged shroud around the turbine which concentrates the wind flow past the turbine blades and hence improves the efficiency. The design also offers improved safety and reduces acoustic noise. Furthermore, it may offer a significantly lower radar signature, which may make the design much more attractive for use in situations where conventional wind turbine designs may disturb the operation of radars. The authors present the results of an experimental trial, carried out in the UK, to measure the radar cross section (RCS) of a 5 kW Wind Lens turbine prototype and provide a reference database that can be used for comparing the Wind Lens RCS with that of conventional turbines. The authors also investigate the methods to further reduce the Wind Lens RCS and present the results of a time-varying Doppler analysis. The results show that the addition of a metallic mesh around the shroud obscures the rotating blades, and hence mitigates the RCS by 15 dB, at angles where the radar interference is highest.

The unwanted return signals from wind turbines can contaminate the weather-radar data that are used by forecasters and automatic algorithms to issue forecast and warnings for severe weather. Since wind turbines have moving components that generate return signals with non-zero Doppler velocity, traditional ground clutter filters are ineffective at removing wind turbine clutter (WTC). In this study, a WTC mitigation algorithm using the range-Doppler spectrum is developed and tested with simulated weather and WTC signals. Once the general locations of the WTC contamination are known, the proposed range-Doppler regression (RDR) algorithm exploits the spatial continuity of weather signals in the range domain to mitigate the WTC contamination while retaining as much weather signal as possible. In contrast to other proposed mitigation algorithms, the RDR algorithm is suited for real-time implementation on typical operational weather radars. Simulated data are used to optimise the parameters of the algorithm and evaluate its performance for stratiform- and convective-precipitation cases with different degrees of WTC contamination. Finally, a real data case is processed to illustrate the RDR algorithm's effectiveness. The results show that the RDR algorithm has the potential to effectively reduce the bias in spectral-moment estimates caused by WTC contamination in an operational environment.

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this study, an algorithm based on the expectation-maximisation approach is proposed to estimate all the registration errors involved in the grid-locking problem, that is, attitude, measurement and position biases. Its statistical performance is investigated by Monte Carlo simulation and compared with that of a previously derived linear least squares estimator and to the hybrid Cramér–Rao lower bound.