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A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.

A novel dual-band compact patch wireless fidelity (Wi-Fi) antenna using μ-negative (MNG) transmission lines (TLs) for the smart helmet is presented. In the smart helmet scenario, rescue agent is able to communicate with the other rescue agents, control centre, and drones via the helmet by Wi-Fi dual bands. For the smart helmet communication, the antenna, which consists of two MNG-TL loops located on the outside and inside, is devised. The dual-band corresponds to the zeroth-order resonance (ZOR) and second-positive-order resonance (SPOR) of the outer MNG-TL. The ZOR frequency of the outer MNG-TL is near the Wi-Fi 2.4 GHz band, and the SPOR frequency of the outer MNG-TL, excited by the coupling between the inner and outer MNG-TL loops, is the Wi-Fi 5 GHz band. The prototype of the proposed antenna has been fabricated and measured. The bandwidth of the antenna includes Wi-Fi 2.4 and 5 GHz bands with high radiation efficiencies of >49% and a compact size of 27.6 mm × 27.6 mm × 1.27 mm (1/4.5 λ × 1/4.5 λ × 1/98.4 λ at the lowest operating frequency).

Presented is a novel monopulse estimator that uses the inverse function of a monopulse ratio (MR) curve of the maximum likelihood (ML)-based monopulse estimator. The proposed estimator exploits the fact that the ML-based MR curve is approximated by one tangent function. The proposed estimator works in two steps, in which the ML-based estimator is applied first and then its output is corrected by the inverse function of the MR curve of the ML-based estimator. Its derivation is presented, in addition to simulation results showing that it outperforms the conventional ML-based estimator.