To increase the mobility of an unmanned ground vehicle, this study describes new approach to detect targets in front of mono-static terrain imaging radar (TIR), which is a prototypical ground-based forward looking radar. Since the TIR employs a mono-static configuration and a real aperture antenna array, the conventional back-projection method is very useful in spite of its poor processing time. However, it is difficult to employ another method because the TIR is an ultra-wide-band type of radar and employs a dechirp-on-receive process. To overcome these difficulties, a new approach based on scaling function processing is proposed in this study. This scaling function is based on a spectral analysis approach and the proposed method conducts range cell migration compensation, secondary range compression and azimuth compression using this scaling function. In this study, the complete derivation of the proposed method is presented. A very useful formulation for a dechirped mono-static radar signal in the range Doppler domain is also proposed, in which the signal is expressed by the scaling function. The results of simulations and field tests are demonstrated to show the performance and validity of the proposed method.

This study presents a new algorithm for near-field source localisation by using a uniform linear array consisting of velocity-sensor pairs (VSPs). Two schemes are used for range and direction of arrival (DOA) estimation in the proposed algorithm. The first one exploits the acoustic velocity vector field's two Cartesian components and the triangular location relationship among the near-field sources and the VSPs. The resulting range and DOA estimates are ambiguity-free, regardless of the inter-sensor spacing. Whereas the second scheme uses the so-called electric angles to determine the ranges and DOAs. Although the derived estimates may be ambiguous in the case of the inter-sensor spacing larger than a quarter wavelength, this ambiguity can be removed by using the estimates obtained in the first scheme. Therefore the proposed algorithm can offer high estimation accuracy by increasing inter-sensor spacing (because of array aperture extension). In addition, the proposed algorithm does not require multidimensional search, pairing parameters or high-order statistics, hence, has a low computational complexity.

For the acquisition of weak global positioning system L5 signals in high dynamic environments, coherent integration time is necessarily extended for signal-to-noise ratio improvement. However, the incidental problems, such as high-rate sign transitions, tiered codes and particularly the concurrence of Doppler shift and Doppler rate, will bring challenges to the acquisition. This study proposes a novel acquisition algorithm based on the discrete chirp-Fourier transform (DCFT) to solve the problems above. The 1 ms complex correlation is modelled as a chirp signal to account for high dynamics effect. Then the DCFT is introduced to simultaneously estimate the Doppler shift and Doppler rate residuals. In this way, the 1 ms complex correlations are coherently post-integrated and a high processing gain can be obtained when both the primary and secondary code phases are aligned. Furthermore, the false alarm and detection probabilities are derived to statistically characterise the acquisition performance. Simulation results demonstrate that the proposed algorithm is tolerant of high dynamics and robust to noise. Specifically, it can acquire the L5 signal with up to 20 g acceleration and only 25 dB Hz carrier-to-noise ratio, which is impossible for the conventional fast Fourier transform scheme.

This study describes the design of forward-looking monopulse arrays able to reconfigure the radiation pattern from the sum mode to the difference one by electronically switching a set of parasitic dipoles placed in front of a driven array of radiating dipoles. The antenna architecture is synthesised by optimising the geometric parameters of the passive elements, namely their positions and lengths. The generation of the difference beam is yielded by imposing a phase displacement of π to the excitations of half active array and activating the parasitic array by turning-on the switches that partition their lengths. As for the sum pattern, the effect of the parasitic dipoles is made negligible by turning-off the switches. A set of representative results is reported and discussed to show the effectiveness of the proposed approach.

In this study, the authors exploit the sparse nature of radar targets, and propose a universal, target-oriented ‘compressive feature’ and kernel sparse coding-based radar target recognition approach via the recent developed compressive sensing theory. Inspired by the visual attention mechanism, pulse contourlet transform is proposed to derive the target-oriented compressive features, and a kernel sparse coding classifier is advanced inspired by the fact that kernel trick can make the features more clustered in higher dimensional space, so resulting in accurate and robust recognition of targets. Some experiments are taken on recognising three types of ground vehicles in the moving and stationary target acquisition and recognition public release database, to compare the performance of the proposed scheme with its counterparts, and the results prove its efficiency.

To achieve ultra-high resolution images, processing algorithm of spaceborne synthetic aperture radar (SAR) faces significant challenges such as the curved orbit, the unneglectable propagation time and the space-variant effective velocity. In the light of imaging requirements of the ultra-high resolution spaceborne SAR, an advanced range migration algorithm (ARMA) is presented in this study. Firstly, a new slant range model is developed by incorporating straight orbit approximation with additional linear, cubic and quartic term. Subsequently, a two-dimensional spectrum is derived by making use of Fourier transform pairs and the approximate azimuth stationary point based on the new range model. Finally, a novel RMA is derived. In this algorithm, the accurate range cell migration correction (RCMC) is done through two steps: the effective velocity dependence RCMC and the bulk RCMC, and the range-variant azimuth filtering is accomplished. Simulations are carried out to verify our proposed algorithm, which indicate that ARMA can keep precise even the resolution is up to decimeter level.

Maintaining the constant false alarm rate (CFAR) is an important issue for the dynamic programming-based track-before-detect (DP-TBD) in low signal-to-noise ratio environment. However, the existing method for analysing the false alarm probability, based on extreme value theory (EVT), leads to the inaccuracy of the obtained detection threshold. In this study, a new analytical approach to compute the false alarm probability of DP-TBD is presented. In the proposed method, the generalised Pareto distribution is utilised to approximate the false alarm probability based on the peaks over threshold model, which can maintain CFAR for DP-TBD method in the low signal-to-noise environment effectively. Simulation results show that this approach provides a more accurate false alarm probability estimation than previous EVT methods.

In this study, an ensemble of differential evolution (DE) algorithms is presented to classify electromagnetic targets in resonance scattering region. The algorithm aims to synthesize a special incident signal for each target, which is defined as the main discrimination feature in the given target recognition method. In the proposed algorithm, the amplitudes of basis functions and the duration of this incident signal are optimised to give minimum late-time scattered signal's energy, which is the main fitness function of the algorithm. The proposed DE algorithm is applied to a target set consisting of lossless dielectric spheres and correct recognition rates for both noiseless and noisy signals are obtained. The results for both developed DE algorithm and other DE variants of traditional DE, adaptive differential evolution with optional external archive (JADE), jDE are also given to compare the algorithms and show the effectiveness of the proposed one.

Vehicle positioning with global navigation satellite system in urban environments has three challenges: signal attenuation, multipath and dynamic. In this study, the direct position tracking loop (DPTL) is proposed. Different from non-linear navigation domain vector tracking loops, DPTL is a linear scheme derived from the linearised model of baseband signal. Thus, the computational cost of DPTL is not too expensive. Different from pseudorange-based vector tracking loops (PB-VTLs) taking pseudorange residuals as intermediate variables to synthesise user position residuals, DPTL directly discriminates user position residuals in the domain of user position and receiver clock bias. Thus, DPTL takes full advantage of geometric relationships between the receiver and satellites to improve positioning accuracy. Moreover, for the purpose of improving positioning accuracy and maintaining sufficiently wide dynamic range at the same time, DPTL incorporates the vehicle movement characteristic in urban environments to configure early–late distance spacings of the discriminator and process noise power spectral densities of Kalman filter separately for each component of user position and receiver clock bias. Performance analysis and simulation confirm the tracking and positioning performance of DPTL is better than that of the PB-VTL in both dynamic weak signal and multipath environments.

Time-delay estimation has essential applications in the field of radar, sonar and robotics. For a very distant source, time-delay vector estimation across an M-sensor array is realised using the generalised cross-correlation (GCC) function and the estimates combined with their covariance matrix, expressed in terms of a priori known signal and noise spectra, to yield a linear minimum variance unbiased estimator, known as the Gauss–Markov Estimate. In the absence of a priori information, spectral estimation has to be done at one of the sensors. For close range sources, the use of amplitude attenuation information across the array will improve this estimate further. This study presents a new amplitude information related Gauss–Markov estimate, which calculates the power spectral density (PSD) at all the sensors of the array and gives more accurate time delay estimates in terms of the mean-square error when compared to the earlier constant amplitude-based technique using the PSD at the closest sensor. The performance has been evaluated against signal-to-noise ratio for varying distance of a source from the receiving array. The results have been verified by simulations and experiments for a two-dimensional source localisation problem.

Real time slant path tropospheric delay (STD) is of benefit in near real-time weather forecasting, climatologic applications, and reconstructing refractivity profile. For low elevation ( < 5°), the traditional method precision of calculating STD is poor. A new method of estimating real-time STD at very low elevation (0° − 5°), which is based on singular ground-based Global Positioning System (GPS) with International GPS Service ultra-rapid product, is presented in this study. An experiment was carried out to validate the feasibility on an island in Shantou, south China during July 2009. Results from radiosondes, GPS and models are compared and show that GPS-derived results have agreement with those from radiosonde for low elevation ( < 5°). Consequently, the method can potentially offset some drawbacks of traditional method, and even may be used for monitoring the troposphere without radiosondes in some aspects in the future.

In recent years, cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and become an area of vigorous research and development. This study presents a dynamic waveform selection algorithm to strive for tracking error minimisation for CR manoeuvering target tracking in clutter. Based on the concepts of resolution cell and measurement extraction cell, the statistical characteristics of radar measurements are discussed without dependence upon the Cramér-Rao lower bound of the measurement errors and the high signal-to-noise ratio assumption. A particle filter combined with probabilistic data association is used as a tracker. To quantify the utility of available waveforms, the predicted tracking mean-square error, because of its dependence on actual future measurements, is approximated efficiently via Gaussian fitting of the prior density of the target state and statistical linearisation of the measurement equation. Monte Carlo simulation results show that the proposed dynamic waveform selection algorithm can improve tracking performance considerably, especially in terms of track loss probability.