Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

This paper is focused on the detection and resolution of Unmanned Aerial Vehicles (UAVs) via a multi-channel phased-array radar with emphasis on the challenging situation where multiple targets are unresolved in the range-Doppler domain and closely spaced in angle. An adaptive processor is devised which, after triggering a detection in a given range-Doppler cell, implements a binary decision about the presence of a single or multiple targets. In the former situation, the plain monopulse algorithm is used to estimate the target azimuth whereas, in the latter case, a super-resolution technique is activated to get the angular parameters of the UAVs. The performance of the signal processor is assessed both on simulated and on measured data (endowed by ground-truth) collected by a 12-channel phased-array radar operating in the C-band. The results highlight the capabilities of the architecture to detect the presence of targets, to discriminate the single from the multiple targets scenario, and to resolve the UAVs in angle domain when they are unresolved in range and Doppler.

Various kinds of data are generated from industrial Internet of Things, and these data can be applied for connecting production equipment, identifying and locating items, etc. These data should be forwarded to the decision center for further analyses, especially in wartime. Thus, the channel status information (CSI) for industrial big data transmission has to be acquired. In this article, we develop a system architecture for industrial big data (BD) transmission based on radar-communication integration with arbitrary geometrical array. The traditional channel estimation method, which usually utilizes the regular antenna array to estimate the CSI, cannot be applied to the arbitrary geometrical array. Here, we use the manifold separation technique to transform the complex array configuration into regular array and the downlink channel covariance matrix is estimated by exploiting the frequency calibration technique when the uplink channel covariance matrix is received. The computational complexity for the proposed method and other state-of-the-art methods are analyzed. The simulation results prove that the proposed method can achieve excellent estimation performance for its application in radar-communication integration.

Direction of arrival (DOA) estimation is an important topic of array signal processing and channels without gain-phase errors or with time-invariant gain-phase errors are assumed in most of the DOA estimation algorithms. However, with the measured data of a reconnaissance receiver system, we verify that the gain-phase errors between channels is time-variation. Based on this fact, a more practical signal model with time-variant gain-phase errors is constructed. To obtain accurate DOA estimation, an algorithm termed as coherent integration-sparse Bayesian learning with time-variant gain-phase errors (CI-SBLVGPE) is devised which has two superiorities, i.e., 1) the CI operation integrates the signal energy into a single snapshot, which decreases the gain-phase errors, makes the compensation possible and guarantees a low computational complexity; 2) the priori information of the gain-phase errors, source DOA and noise is used to reduce the computational complexity and improve the robustness. With mathematical analyses, numerical simulations and real data processing, we validate the constructed model and superiorities of the proposed method.

Azimuth multichannel (AMC) technology is one of the mainstream technical approaches to realize high-resolution wide-swath (HRWS) imaging. It has been successfully applied to several synthetic aperture radar (SAR) satellites in orbit. However, the inevitable imbalance between channels can seriously affect the azimuth reconstruction spectrum, introducing ghost targets into the final imaging results and degrading the SAR image quality. In order to address this issue, this paper proposes a channel imbalance estimation method based on minimizing the sum of the sub-band norm (MSSBN) for the reconstructed azimuth spectrum. First, the amplitude imbalance is calibrated in the range-Doppler domain. Then, the echo in each channel with phase imbalances is reconstructed by filters separately and converted to the range-Doppler domain. Finally, the global optimization algorithm is used to find the phase error of each channel so that the reconstructed postcompensation spectrum has the smallest sub-band spectrum norm sum. By two-dimensional blocking, this method can also estimate the space-varying phase imbalance in the range dimension and the time-varying phase imbalance in the azimuth dimension. Experimental results using simulated and actual AMC SAR data from the GF-3 system validate the proposed algorithm’s high estimation accuracy and excellent computational efficiency.

Spaceborne Bistatic SAR (BiSAR) systems utilize an inter-satellite link to achieve phase synchronization. However, Radio Frequency Interference (RFI) from communication satellites and ground-based radars often contaminates the synchronization signal, leading to inaccuracies in the inverted Digital Elevation Model (DEM). Therefore, this paper puts forward an advanced phase-preserving synchronization RFI suppression method and validates it using data from an L-band BiSAR system, LuTan-1 (LT-1). The method involves detecting and locating RFI within a monopulse synchronization signal, and the signal at the estimated RFI position is removed to obtain a preprocessed signal. Then, based on the preprocessed signal and the RFI model, the RFI is estimated using a gradient-based approach. Finally, the estimated RFI is subtracted from the monopulse signal to obtain the desired signal. In addition, synchronization RFI suppression and DEM generation experiments are performed on the LT-1 data to verify the proposed method. Experimental results demonstrate that the method effectively suppresses synchronization RFI and improves DEM accuracy, and it has extensive application prospects in future low-band distributed Interferometric SAR (InSAR) missions.