The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time–frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database.

In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new method is based on LU distribution with partial pivoting. Simulation results confirm the validity of the presented model and theoretical assumptions. In addition, more accurate object detection results were demonstrated for specific computational examples than for other statistical methods. Considering the current analysis of the literature, it is noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Hence, it should be emphasized that the posted study fills a gap in current research on STAP.

This paper presents a novel measurement method and briefly discusses the basic properties of direction of arrival (DoA) measurement in a multiple-input multiple-output (MIMO) radar system by using orthogonality with time-division multiplexing (TDM), where only one transmitting antenna element is active in each time slot. This paper presents the mathematical model of the TDM-MIMO radar operating at 10 GHz, transmitting a string of pulses, the method of transmitting and receiving the signal, and the method of measuring the angle of arrival of the signal based on the use of the Capon algorithm and its modifications. Finally, the correctness of the theory, algorithm and method of measuring the direction of arrival of the signal is verified by experimental simulation. The work discussed in this paper is of great significance to practically demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations like reconnaissance and electronic warfare systems.

This research presents findings from laboratory experiments on a novel method for identifying and differentiating objects using radar signatures and a specialized convolutional neural network architecture. Previously introduced by the authors, this method has been validated through real-world measurements in an urban environment with a 24 GHz frequency-modulated continuous-wave radar. This study describes how radar signatures, generated in the MATLAB (R2023b) environment from I and Q signals captured by the uRAD USB v1.2 radar, were processed. A database of radar signatures for pedestrians, cyclists, and vehicles was created, and a tailored convolutional neural network was trained. The developed solution achieves an accuracy of over 95% in distinguishing between various objects. The simulation results and successful tests support the application of this system across various sectors. The key innovations include distinguishing multiple objects from a single radar signature, a custom architecture for the convolutional neural network, and an application that processes radar data to produce near-real-time recognition results.

Unmanned aerial vehicles (UAVs), birds, and foreign objects near airport runways pose a serious threat to aviation safety, particularly during takeoff and landing phases, when aircraft operate at high speeds and low altitudes. Although drone-related incidents remain infrequent, they can cause...

Space-time adaptive processing (STAP) of signal is a modern signal processing technique applied to radar systems. The STAP technique is used for the detection of objects moving on the ground through a airborne radar. STAP is designed to filter out echoes from sources of interference and preserve the signal coming from the object of interest. The article presents the problem of the influence of aircraft motion and, at the same time, the influence of atmospheric conditions on the quality of clutter filtering in the received echo signal using STAP. The paper presents a model of STAP signal processing for these phenomena, while simulations performed confirmed the existence of the analyzed problems.