Due to the excessive cost of 360° "multiple-transmitter and multiple-receiving " near field measurement in practical engineering applications, a truncated angular near field measurement method with a given accuracy requirement is proposed in this paper by analyzing the influence of the near-field scattering characteristics and the near-to-far field transformation on the RCS to greatly reduce the angular range of near-field antennas. In addition, it is often necessary to focus on components with strong scattering echoes while analyzing and diagnosing the local RCS. Therefore, an engineering-oriented near-field measurement method is proposed in this paper to achieve local RCS diagnoses of the targets. It includes two scanning modes, an arc array and a linear array, to achieve the goal of quickly performing local RCS analyses and diagnoses of coupled scattering targets through near-field testing. Regardless of the cost of the test site, the operability of the test system, and the efficiency of the work, this method provides a new technique for RCS measurement of stealth targets and has important research significance in weapon engineering test phases.

Because of the distortion of scatter pattern when measuring multiple-scattering targets in the condition of not satisfying the far-field condition, existing measurements are not favorable. We present a novel algorithm to research near-field to far-field transformation (NFFFT) for multiple-scattering targets. This paper introduces the method of NFFFT using “multiple emission multiple reception” and then chooses a dihedral angle, which has multiple-scattering structure to simulate. The results show a good agreement with the far-field radar cross section (RCS) pattern. Taking into account the feasibility of design, the cost of system, and other limitations in practical application, the bistatic measurement system with a full 360° is obviously hard to achieve. In the bandwidth-limited scattering field, we give the rule of angular truncation based on the transfer function for reducing receiving region. To make the conclusion more reliable, simulations should be set in different parameters, concluding testing frequencies, target size and measuring distance, to observe their effects on the receiving region. We get the mean error of different receiving region by comparing the results of NFFFT and far-field RCS. As a result, a principle for choosing a proper receiving region, say angular truncation, is summarized for multiple-scattering targets, with mean errors 0.5 dB below the full 360° results near the center of far-field pattern.

Considering the neighbour discovery problem in mobile ad hoc networks (MANETS) with omnidirectional antennas, all nodes want to discover the nodes within a single hop and obtain their identities. However, many existing algorithms spend an extra overhead for neighbour discovery and there is a need of response from neighbours. In this study, a slot allocation scheme is introduced for duplex communication, making all nodes recognisable to each other with a unique codeword. Then, a novel neighbour discovery algorithm based on compressed sensing to apply in this scheme is proposed; it enables all nodes to update neighbour information through the received signal after one or several frames and spends no extra overhead. Furthermore, many algorithms cannot be applied in the overload networks, but the algorithm proposed here is suited for overload networks; the upper bound for its active neighbours is limited to the performance of the network. Finally, the algorithm is validated through simulation and the effect of the three main factors on the performance of the algorithm is researched. The result implies that this algorithm can adapt to various scenarios of the MANETS.

Detecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.