Optimal power-aperture balance

The role of the Royal Navy has changed dramatically since the end of the Cold War. Ships and personnel are now expected to fulfil a wider range of possible missions in theatres all over the world. Consequently ship's radar systems are now expected to give a high level of performance in regions that include the Middle East and the Eastern Mediterranean, despite being originally designed to operate predominately in the North Atlantic. The problem is that the local scenario and environmental conditions can dramatically affect radar system performance in two important ways. Firstly the environment can change the way that energy propagates outwards from the radar leading to large variations in target and clutter strength between different locations. Secondly these variations are often hidden by automatic threshold changes that reduce sensitivity to prevent the radar picture from becoming cluttered. This can give the false impression that the radar is operating optimally and could prevent ship's personnel from adapting their tactics to fit to the local situation. This paper outlines an updated model that is compared to real data in ducting conditions. The model is used to examine radar performance in littoral scenarios and to estimate sea state and duct strength from real radar data. The ultimate aim is to combine information collected by the ship's surveillance radar with modelling to allow ship's personnel to monitor the effect of the local environment on radar performance.

<div class="section abstract"><div class="htmlview paragraph">Radars based ADAS solutions are growing at a substantial rate in the automotive market since they play a fundamental role in increasing passengers and pedestrian safety.</div><div class="htmlview paragraph">To support radar designers in reducing time and design cost, to meet the stringent vehicle safety norms, to reduce risks about final installed radar performance etc., more effective and efficient industrial approach have to be devised both in terms of simulation (working at 77 GHz poses several numerical challenges) and testing.</div><div class="htmlview paragraph">This paper presents IDS/Siemens PLM Software technical approach and CAE tools, based on a synergic use of high-fidelity simulation models and measurement set-up’s to support the whole design, optimization and verification cycle, starting from the stand-alone radar antenna performance up to on-car installed radar operational performance verification.</div></div>

Radar usually employs several signal processing methods successively for detection. Each processing step influences the radar detection performance. Thus, analysis of the radar detection performance considering the complete signal processing flow provides significant reference for radar detector design. In this paper, with the assumption that matched filtering, square-law detection, non-coherent integration, cell averaging constant false alarm rate detection and double-threshold detection are employed sequentially for radar detection, radar detection performance in K distributed sea clutter plus Rayleigh distributed thermal noise is analyzed. Mathematical expressions of radar false alarm probability and detection probability are derived. Radar detection performances for four types of Swerling fluctuating targets in various circumstances are presented by simulation. And comprehensive effect of K distributed sea clutter plus Rayleigh distributed thermal noise on radar detection performance is analyzed.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

Aiming at the phenomenon of complicated process and imperfect algorithm in the evaluation of radar anti-stealth performance,a new method for the evaluation based on improved evidence theory was presented. Firstly,to overcome the shortcomings of strong subjectivity and weak correlation,the credibility assignment function was corrected in real time according to the relationship among credibility factor,entropy weight and supported coefficient,which is based on the objective analysis of entropy theory and Euclidean distance.Secondly,combined with the multi-hierarchy indexes of radar anti-stealth performance,the rank order of whole radars anti-stealth performance on utility interval was attained. At last,an objective and accurate assessment results were obtained. The analysis of theory and simulation proved that,in contrast to other fusion algorithm,the improved fusion algorithm had better ability of assessment and better robustness.

SDSC SHAR, ISRO equipped with ground-based Precision Radars for tracking the launch vehicles for range safety operations and trajectory computations. The Design of Radar parameters, waveform generation and Receiver signal processing [4] always appears to be challenging owing to the wide effects it can cause on the performance of radars [3]. The radars under consideration are Tracking Radars, intended for track Launch Vehicle. It is designed for skin mode and Transponder mode operation which calls for accurate estimation of Signal strength to achieve sufficient link margin for a target complex Radar Cross Section (RCS) [6] at slant range. In radar technology, scattering phenomenology is quantified by the target parameter RCS, can be modelled as random process. Probability density functions techniques are used to describe the target scattering and RCS estimation. Receiver that employs an envelope detector followed by a threshold decision technique [5]. The input to the receiver is composed of radar echo s(t) and additive zero mean white Gaussian noise n(t), with variance 2 . The input noise is assumed to be spatially incoherent and uncorrelated with signal. The IF filter output is a complex random variable that is composed of either noise alone or noise plus target return signal. The noise quadrature components nI(t) and nQ(t) are uncorrelated zero mean low pass Gaussian noise with equal variance 2 . The Joint Probability Density Functions of random variables is Rician PDF if target is available or Rayleigh PDF if noise alone present. Variations in radar-target geometry [8], target vibration, radar frequency, and polarization changes can lead to variations in target RCS, resulting in fluctuating targets having Swerling models [9] of target RCS fluctuation, The effect of these models on radar detection, signal strength estimation for a given radar parameters are considered.

This article presents a comprehensive survey on the integration of Generative Artificial Intelligence (AI) technologies in radar applications, with a focus on enhancing radar data processing and system capabilities. Generative AI techniques, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are explored for their potential to address persistent challenges in radar technology such as noise management, data augmentation, and target classification. The study investigates how GANs can generate synthetic radar datasets, aiding in model training when actual data is scarce, and how VAEs contribute to signal processing by denoising and reconstructing accurate radar signals. The analysis includes case studies on clutter suppression, radar data augmentation, beam blockage correction, and data fusion, highlighting the transformative impact of Generative AI on radar systems. This paper aims to provide insights into the current advancements and future directions of Generative AI applications in radar, suggesting that these technologies hold significant promise for improving the accuracy and efficiency of radar systems in diverse and dynamic environments.

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In recent years, joint radar and communication (JRC) has gained substantial attention due to its high spectrum efficiency and equipment utilization. In this paper, we consider a JRC-enabled multi-UAV cooperative detection scenario, in which each UAV equipped with a JRC unit simultaneously performs the detection function to sense multiple targets and the communication function to transmit the detected data to a fusion center. To strike a trade-off between radar sensory and communication performance for all UAVs, we optimize the transmit power, resource allocation and navigation for each UAV to maximize their sensing scores and the geographical fairness of the targets on the condition of the quality requirements of communication and radar sensing. This problem is a mixed integer non-convex optimization that is challenging to be solved in practice. Considering the complexity of the optimization and dynamics of the UAV environment, we design a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> earning based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> rajectory <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> lanning and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> esource <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> llocation (LTPRA) algorithm that leverages multiple learning agents to find effective policies from experiences while guaranteeing communication and sensing performance. To improve the environmental exploration of agents, we design an incentive mechanism for their detection behavior and introduce a policy regularization method to mitigate policy overfitting in multi-agent cooperation. Numerical results reveal the convergence performance of the proposed algorithm and show the improvement on detection and communication performance in JRC-enabled multi-UAV detection systems compared with state-of-the-art approaches.

The traditional probe–pass integration system embeds communication information into a radar waveform, which leads to a high level of waveform autocorrelation sidelobes and a poor false symbol rate at low signal-to-noise ratios. This article proposes a three-dimensional indexed modulation-based design method for probe–pass integration waveforms. This method realises communication information modulation and demodulation by simultaneously indexing orthogonal waveform selection, transmitting pulse PRI changes and carrier frequency changes in three dimensions, and applying compressed perception technology to solve the problems of PRI shortcuts and carrier frequency, resulting in a velocity term in the received waveform that cannot be accumulated by phase reference to realise velocity super-resolution. Finally, the radar detection performance and communication performance are simulated and analysed, and the simulation results reveal that the method proposed in this paper can not only satisfy the radar detection performance requirements but also achieve a lower unsigned rate on the basis of an improved communication rate.

In this paper, the integrated radar communication system based on the subcarrier multiplexing OFDM is proposed. The subcarrier multiplexing integrated waveform is introduced, and the corresponding transmission, reception, and signal processing flow are given. Since the allocation of subcarriers has significant impact on the performance of radar and communication, three kinds of subcarrier configuration are analyzed, including continuous configuration, cross configuration, and random configuration. Except the radar echoes after pulse compression and communication bit error rate (BER), mutual information (MI) and data information rate (DIR) are also used to analyze the performance of the radar signal and the communication signal respectively. The simulation results have verified the feasibility of the proposed integrated radar communication system.

Providing artillery or missile with firing data, the Fire Control Radar (FCR) is often the key target of hostile electronic attack. Therefore, in order to better counter electronic attack, it has to promote its capabilities in detecting, intercepting and tracking enemy targets, controlling firepower and ensuring its own safety in such six technological aspects as adopting such technologies as phased array radar, broadened spectrum, complex signal processing, photoelectricity combination, bait laying and multi-radar networking, which is of considerable significance in the overall design of Fire Control Radar (FCR) anti-jamming.

Background: Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks. Objective: The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA). Method: This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point (AP) to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling converted into convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment. Results and Conclusion: The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.

Though radar systems have many specific applications, radars perform three general functions, with all the specific applications falling into one or more of these general functions. The three primary functions are search, track, and image. The radar search mode implies the process of target detection. Target tracking implies that the radar makes measurements of the target state in range, azimuth angle, elevation angle, and Doppler frequency offset. This is not to exclude the fact that a search radar will perform target measurements to provide a cue for another sensor, for example, or that a track radar will perform the detection process.

This paper presents a technique for assessing the performance of continuous wave Doppler radar systems for physiological sensing. The technique includes an artificial target for testing physiological sensing radar systems with motion analogous to human heart movement and software algorithms leveraging the capabilities of this target to simply test radar system performance. The mechanical target provides simple to complex patterns of motion that are stable and repeatable. Details of radar system performance can be assessed and the effects of configuration changes that might not appear with a human target can be observed when using this mechanical target.