With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone’s propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250 g step with high accuracy.

This paper presents a method of imaging a two-dimensional section of a walking person using multiple Doppler radar systems. Although each simple radar system consists of only two receivers, different radial speeds allow target positions to be separated and located. The signal received using each antenna is processed employing time-frequency analysis, which separates targets in the time-range-velocity space. This process is followed by a direction-of-arrival estimation employing interferometry. The data obtained using the multiple radar systems are integrated using a clustering algorithm and a target-tracking algorithm. Through realistic simulations, we demonstrate the remarkable performance of the proposed imaging method in generating a clear outline image of a human target in unknown motion.

Ultra wideband (UWB) radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB radar imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional radar imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

Conventional radar imaging systems use Fourier transform for image formation, but due to the target's complicated motion the Doppler spectrum is time-varying and thus the reconstructed image becomes blurred even after applying standard motion compensation algorithms. Therefore, sophisticated algorithms such as polar reformatting are usually employed to produce clear images. Alternatively, Joint Time-Frequency (JTF) analysis can be used for image formation which produces clear image without using polar reformatting algorithm. In this paper, a new JTF-based method is proposed for image formation in inverse synthetic aperture radars (ISAR). This method uses minimum entropy criterion for optimum parameter adjustment of JTF algorithms. Short Time Fourier Transform (STFT) and Fractional Fourier Transform (FrFT) are applied as JTF for time-varying Doppler spectrum analysis. Both the width of Gaussian window of STFT and the order of FrFT, α, are adjusted using minimum entropy as local and total measures. Furthermore, a new statistical parameter, called normalized correlation, is defined for comparison of images reconstructed by different methods. Simulation results show that α-order FrFT with local adjustment has much better performance than the other methods in this category even in low SNR.

UWB pulse radars enable us to measure a target location with high range-resolution, and so are applicable for measurement systems for robots and automobile. We have already proposed a robust and fast imaging algorithm with an envelope of circles, which is suitable for these applications. In this method, we determine time delays from received signals with the matched filter for a transmitted waveform. However, scattered waveforms are different from transmitted one depending on the target shape. Therefore, the resolution of the target edges deteriorates due to these waveform distortions. In this paper, a high-resolution imaging algorithm for convex targets is proposed by iteration of the shape and waveform estimation. We show application examples with numerical simulations and experiments, and confirm its capability to detect edges of an object.

Radars utilizing ultra-wide-band (UWB) pulses are attractive as an environment measurement method for various applications including household robots. Suitable filtering is essential for accurate ranging, which requires an accurate waveform estimation. This paper presents a high-resolution algorithm of estimating target location and scattered waveforms, whose accuracies are interdependent. The technique relies on iterative improvements of estimated waveforms. Description of the algorithm is followed by statistical simulation examples. The performance of the algorithm is contrasted with conventional ones and statistical bounds. Results indicate that our proposed algorithm has a remarkable performance, which is close to the theoretical limit. Next, we clarify the problem of applying HCT to multiple targets. HCT for multiple targets can not be used as an estimated waveform because of interference waves from other targets. We propose an interference suppression algorithm based on a neural network, and show an application example of the algorithm.

This paper presents an imaging technique using the MUSIC algorithm to localize cylindrical reflectors in cross-borehole radar arrangements. Tomographic measurement, in which a transmitting and a receiving antenna are individually moved in separate boreholes, can be considered as a combination of a transmitting and a receiving array. A decorrelation technique with the transmitting array, which has been proposed for imaging point reflectors, is applied for imaging cylindrical reflectors using the MUSIC algorithm. Simulated and experimental results are shown to verify the validity of this algorithm for cylindrical targets. We analyze the evaluation error caused by the increase in the radius of the cylinder.

Signal processing antennas have been studied not only for interference suppression but also for high-resolution estimation of radio environment such as directions-of-arrival of incident signals. These two applications are based on the common technique, that is, null steering. This tutorial paper reviews the MUSIC algorithm which is one of the typical high-resolution techniques. Examining the eigenvector beam patterns, we demonstrate that the high-resolution capability is realized by steering nulls. The considerations will be useful for understanding the high-resolution techniques in the signal processing antennas. We then describe a modified version of MUSIC (Root MUSIC). We show the performance and robustness of the method. Furthermore, we introduce radar target identification and two-dimensional radar target imaging. These study fields are new applications of the signal processing antennas, to which a great deal of attention has been devoted recently.