Iet Radar Sonar and Navigation最新文献

A main challenge within inertial navigation systems involves attitude determination, which refers to the process of estimating the angles that define orientations. A new algorithm is presented to estimate a reliable and proper model for a low-cost attitude and heading reference system (AHRS), while also, including long-term global navigation satellite system (GNSS) outages with various dynamical manoeuvres. In the proposed approach, the initial AHRS model is continuously refined at each time step through the incorporation of complementary terms using a new prediction method. These complementary terms are determined using attitude information from the accelerometers output and the GNSS as reference systems. The proposed estimation technique undergoes mathematical analysis to ascertain stochastic stability and is further assessed through real-world aerial experimental tests conducted with hardware-in-the-loop mechanisation. The obtained results demonstrate a significant enhancement in the reliability and accuracy of the AHRS through the proposed estimation algorithm, even under GNSS blockages. The comparative results highlight the higher performance of the suggested data fusion scheme in providing a reliable and accurate model for the AHRS in normal conditions and assisting a powerful neural network-based algorithm to provide reliable heading information even during long-term GNSS outages under dynamical manoeuvres.

Zhou et al. (2022) proposed a method for instantaneous source depth estimation in deep water using matched beam intensity processing (MBIP) that leverages depth-related oscillatory features arising from Lloyd's mirror interference in the frequency domain (Appl. Acoust., 2022, 186, 108493). However, the efficacy of MBIP diminishes with a random source spectrum. To counteract the effect of a random source spectrum, a vertical line array (VLA) positioned near the sea bottom is employed and a cross-spectrum is generated from the conventional beamforming output, which is achieved by dividing the VLA into upper-half and lower-half arrays. The authors find that the cross-spectrum phase interference pattern is determined by the source depth and is unaffected by the source spectrum. Consequently, a matched cross-spectrum phase processing method is proposed, which demonstrates appreciable depth estimation accuracy compared to that of the existing techniques, as evidenced by the results from both simulated and experimental data.

The Advanced Receiver Autonomous Integrity Monitoring (ARAIM) provides an Aircraft-Based Augmentation System function for aircraft Global Navigation Satellite System equipment. Currently, the consideration of temporal correlations of test statistics is an important update to the ARAIM baseline algorithm. However, due to the tight budgets of integrity and continuity, the updated ARAIM Fault Detection and Exclusion (FDE) is not satisfactory in availability coverage. The baseline algorithm is based on equal allocation of integrity and continuity, which causes ARAIM to be susceptible to faults and satellite outages. An availability optimisation of ARAIM FDE based on dynamic budget allocation is proposed, aiming to make full use of the prior knowledge provided by the Integrity Support Message (ISM). The contribution of each solution separation test in the ARAIM FDE to the total continuity and integrity risks is estimated, and the continuity and integrity allocation constraints are obtained. The impacts of faults and satellite outages on availability are analysed, and a predictive Protection Level (PL) for ARAIM FDE is proposed for use as an objective function for optimising risk allocation. Then, a risk allocation method based on the multiplier penalty function method is proposed. The performances of the baseline algorithm and the proposed method under the maximum Number of Effective Samples are simulated. For dual-constellation H-ARAIM, the optimised Horizontal PL (HPL) is 40% lower than that of the baseline algorithm on average, and the average number of critical satellites supporting RNP 0.3 is less than 1 worldwide. For the three-constellation V-ARIAM, the optimised HPL is 27% lower than that of the baseline algorithm on average, and the optimised Vertical PL (VPL) is 18% lower. The proposed method has high availability coverage and can better respond to the requirements for high-performance navigation services.

The authors propose a multi-agent, multi-dimensional joint optimisation decision-making strategy to tackle the challenges jammers encounter when interfering with multi-functional radars, which possess advanced anti-interference capabilities. The strategy is divided into optimising the interference style and three parameters: timing, power, and duration, to enhance interference effectiveness. Furthermore, state value reuse is combined with the duelling double deep Q-learning and multi-agent deep deterministic policy gradient algorithms to effectively manage the multi-dimensional optimisation of the interference strategy. Simulation results demonstrate that the proposed algorithm outperforms Ant-QL, heuristic accelerated Q-learning, and asynchronous advantage actor-critic algorithms in terms of convergence speed and stability. It identifies a more efficient strategy for transitioning the radar from its initial state to the state with the lowest threat level to the jammer, achieving a 24% improvement in convergence speed. Additionally, the performance curve exhibits better stability.

The problem of detecting the direction of range-spread targets in the presence of subspace interference, along with homogeneous and partially homogeneous Gaussian clutter backgrounds characterised by an unknown persymmetric covariance matrix, is being investigated. It is assumed that the interference subspace is linearly independent of the signal subspace, and the coordinates of both subspaces are unknown. Leveraging the property of persymmetry, the two-step Generalised Likelihood Ratio Test and the two-step Wald test are proposed to formulate three detectors in the presence of subspace interference, homogeneous, and partially homogeneous Gaussian clutter. Theoretical derivations and simulations demonstrate that three proposed detectors maintain a constant false alarm rate with respect to the unknown clutter covariance matrix. In comparison to existing detectors in similar backgrounds, particularly in scenarios with limited training data, these three detectors exhibit superior detection performance.

Reconfigurable intelligent surface (RIS) refers to a two-dimensional smart surface consisting of massive number of reflecting elements. Both active RIS (ARIS) and passive RIS (PRIS) are promising technologies that can adapt and reconfigure the wireless environment, enhancing the reliability and capacity of wireless networks. The authors focus on enhancing the detection ability of ARIS/PRIS-aided multiple-input-multiple-output radar system in the presence of signal-dependent clutter. Constant-envelope constraint is imposed on the sought radar waveforms to improve the practicability of the radar system. To tackle the resultant non-convex problem, a cyclic method based on minorisation–maximisation and alternating direction method of multipliers is derived to iteratively optimise the receive filters, the transmit waveforms, and the RIS coefficients. The major finding is that the distance between the radar and the ARIS has little to no impact on the radar signal-to-interference-plus-noise ratio (SINR). For the PRIS-aided radar system, a simplified model can significantly improve the operational efficiency and has little impact on the radar SINR. Numerous results verify the effectiveness of the proposed algorithm.

Rotor unmanned aerial vehicles (UAVs) play an important role in both military and civilian fields nowadays. The safety risks associated with the UAVs increase the urgent need for detecting UAVs in urban environments as well. Moreover, UAVs are easily located in the non-line-of-sight (NLOS) building sheltered area relative to the radar, making it very challenging to detect and localise. A novel algorithm for localising the rotor UAV in the common L-shaped street building sheltered area is proposed. First, the authors establish a multipath signal model for a rotor UAV hovering over an L-shaped street scene, leveraging the frequency-modulated continuous wave signal. Then, the multipath information of the UAV is extracted by identifying the rotating periodicity of the blade embedded in the time-frequency spectrum of the received signal. The back projection imaging is then conducted on the UAV-related multipath. After extracting multipath ghosts in the image, the street area, where the UAV locates, can be determined, and the UAV is further localised using the path reflection characteristics of this area. Simulations and practical experiments based on millimetre waves indicate that the proposed method can enable high-accuracy estimation of rotor UAV in the NLOS building sheltered area.

The critical role of specifying micro-Doppler mode performance in the modelling and development of modern radar systems is investigated. The authors focus on the detection of micro-Doppler modulation from light aircraft, analysing data from eight helicopters and nine propeller aircraft. With the growing need for accurate target classification in radar technology, incorporating micro-Doppler detection metrics into radar performance specifications has become increasingly important. This research offers a novel approach to measuring the detectability of micro-Doppler modulation relative to returns from the main fuselage. The investigation covers the impacts of various preprocessing techniques, polarisation, and aspect angle on detection capabilities. Findings reveal that, on average, micro-Doppler modulation from propellers is detectable at distances between 50% and 100% of the range at which the fuselage is detected. For helicopters, this range decreases to between 30% and 80%. Additionally, the study introduces empirically derived statistical models designed to predict micro-Doppler detection ranges in relation to fuselage returns, enhancing the predictability and specificity of radar system performance. This novel contribution presents a basis for improving radar system specifications, leading ultimately to more predictable and reliable light aircraft classification.

Drone surveillance with multi-function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub-RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non-periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub-RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X-band MFR platform called ENAVI was developed in-house. The proposed non-periodic burst scheme optimisation approach was validated with in-field tests with the ENAVI radar model. The in-air target was a 3-metre fixed-wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low-profile dual-polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell-averaging technique which proved to correctly detect drones within a 7-km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.

It has become an interesting topic for modern radar possessing capabilities of polarimetric measurement, high-resolution imaging, and good delay-Doppler resolution. The linear frequency modulation (LFM) scheme is widely used in radar systems as it can achieve both high Doppler tolerance and resolution. Based on the LFM scheme, a novel waveform is proposed, namely, the orthogonal double V-linear frequency modulation (ODV-LFM), which exhibits the capability to mitigate the influence of delay-Doppler coupling and reduces the risk of false targets and ghosts in multiple-target scenes. Based on the ODV-LFM signal, a simultaneous polarimetric measurement method is proposed to obtain fully polarimetric information of the target. According to the unique chirp rate characteristic of the ODV-LFM signal, the pointwise linear and non-linear processing methods are proposed to enhance delay-Doppler resolution, suppress sidelobes, and achieve high-resolution imaging without substantially compromising the signal detection performance. Finally, simulated experiments are conducted to demonstrate the superiority of the proposed waveform and methods.