Air Surveillance Radar Research Articles

Mitigation Measures for Windfarm Effects on Radar Systems

Windfarms can have a significant impact on radar systems, especially air surveillance radar. This is because it is usually designed to show only moving targets and cancel out stationary objects. However, the rotation of wind turbine blades can be detected by radar as a false flight or target. Clutter or interference caused by windfarms can reduce radar sensitivity in critical regions, making real targets disappear. This could in turn affect the deployment of windfarms and lead to the cancellation of future renewable energy projects and plans. This paper presents the different strategies and techniques used to mitigate the windfarm impact on radar systems. Precisely, we aim throughout this article to classify the main currently adopted approaches, view the recent trends and research directions in this field, and try to trace the advantages and drawbacks along with the challenges of deploying such schemes.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

A Target Detection and Tracking Method for Multiple Radar Systems

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this paper, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatio-temporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted three-dimensional Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one on actual data from a radar network with two homogeneous air surveillance radars, and the third one on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches.

Wind farm clutter suppression for air surveillance radar based on a combined method of clutter map and K‐SVD algorithm

A clutter suppression method based on a combined method of clutter map and K-SVD algorithm has been proposed, which aims at reducing false alarm rate of air surveillance radar caused by wind farm clutter. First, a clutter map, based on the fixed position and Doppler spectral width characteristics of wind turbines, is established using constant false alarm rate detection. The obtained clutter map is then used to process the traces detected by radar. To retain the target while the clutter is suppressed, the traces in the clutter area are processed using the K-SVD algorithm, which is first proposed to mitigate wind turbine clutter in this study. The experimental results of measured data show that the false alarm caused by the wind turbines can be effectively reduced.

Spatial Modelling to Optimize the Positioning of Air Defense Surveillance Radars with Minimal Shadow Zone

Indonesia’s strategic position between two continents and two oceans has resulted in an obligation for Indonesia to be involved in various international cooperation and international conflicts. The declaration of the development of the maritime sector to be the cornerstone of Indonesia’s main strength requires support from the air defense sector. The current condition of Indonesia’s air defense sector, especially the Air Surveillance Radar has not worked optimally resulting in an air defense operation that has not been maximized. The properties of high-frequency electromagnetic waves used in Doppler radar have a straight line of sight. Mountains, hills, and artificial objects/buildings can become obstacles, create blank areas (areas that cannot be covered by radar) behind them, and reduce the performance of the radar coverage. The use of sensing technology, in particular, the geographic information system to determine the position of air surveillance radar placing is an effective solution to optimize the coverage radars exist. The rectangular rule approach method combined with the land surveying technique is a combination of methods that can be used to measure the volume of air defense radar coverage. The level optimization of radar position planning point can be determined by processing the obstacle volume, land slope, earthquake and landslide vulnerability, and land cover conditions into the calculation of the fuzzy inference system technique. This study examines the concept of using Geographic Information Systems to measure the level of radar optimization when placed in a planned position. This research aims to produce a method review on optimizing the performance of air surveillance radar coverage by utilizing remote sensing technology to determine the radar placement position.

Design and analysis of aperture coupled micro strip patch antenna for radar applications

The aperture coupled- Micro strip Patch Antenna (AC-MPA) is designed for long range (≈400 ​km) air Surveillance radars. The design of patch antennas with aperture coupled power feed is crucial and designed for 1 ​GHz–2 ​GHz (D-band) frequency. The proposed aperture coupled feed design gives wideband behavior, better return loss, gain, Voltage Standing Wave Ratio (VSWR), less compatibility and smaller in size by applying aperture feed. The theoretical analysis is done manually and designed using Advanced Design System (ADS). In aperture coupled feeding technique, the input signal couples to the radiating patch through the aperture (slot) that appears on the ground plane of the feed line and with their feeding structure of the radiating patch element it is different from other micro strip patch antenna. In this work, the design and implementation of aperture coupled patch antenna are analyzed in terms of return loss, gain, efficiency, directivity and power parameters and compared with inset line feeding. The proposed design gives 5 ​dB gain, 4.5 ​dB directivity, efficiency of 30%, impedance bandwidth of above 15% higher than inset feed line with 1.5 ​GHz as centre frequency. The maximum return loss of-40dB is achieved compared to −19dB with aperture feed compared to inset feed line micro strip patch antenna with proper impedance matching. The main advantage of this antenna over single layered patch antenna is increased bandwidth and this antenna will act as dual band also with variation in air gap (1 ​mm–5 ​mm) between single and aperture coupled patch antenna.

Evolutionary multi‐objective optimisation of the pulse burst waveform in solid‐state VHF moving target detection radar

A solid-state, very high frequency (VHF) band, moving target detection air surveillance radar requires a complex pulse burst waveform to mitigate the visibility issues originating from the blind Doppler intervals and range eclipsing. The waveform employs multiple pulse repetition frequencies to mitigate the effects of the blind Doppler intervals and interleaves short and long pulses to mitigate range eclipsing. In the authors’ previous works, they pointed out that the waveform design is a multi-objective optimisation problem and defined the mathematical model of the waveform optimisation problem. They also presented how the exact Pareto optimal (PO) set can be determined by means of exhaustive search. In this paper, they improve the mathematical model of the waveform optimisation problem by altering the way in which one of the objective functions is calculated and adding a new constraint, which eliminates meaningless solutions. Finally, they propose a solution method based on a multi-objective evolutionary algorithm. The performance evaluation test indicates that compared to the exhaustive search, the proposed method provides a solution that is insignificantly different. However, the proposed method is more scalable and requires over three orders of magnitude smaller number of comparisons to determine the PO set, which makes it more viable for the online waveform adaptation.

Radio Frequency Interference Detection and Mitigation Using Compressive Statistical Sensing

AbstractQuasiperiodic radio frequency interference (RFI), such as those generated by telecommunication and active radar systems, is commonly encountered in radio astronomy observations. Such RFI‐contaminated signals contain hidden periodicities due to cyclic features involved in their formation (e.g., carrier frequencies, periodic keying of the amplitude, and baud rates). RFI signal characterization and its subsequent excision based on the well‐known cyclic spectrum analysis have been previously demonstrated; however, the high complexity of the algorithm and the computational cost of its implementation have limited its utility in radio astronomy, rendering less sophisticated solutions. To overcome this challenge, we present a novel method for RFI detection and mitigation based on efficient estimation of the cyclic spectrum by compressive statistical sensing (CSS) of sub‐Nyquist data. CSS performs second‐order statistical estimation such as cyclic spectrum using a reduced number of input samples, thereby enabling accelerated performance. To validate the feasibility of the proposed method, we conduct experiments with simulated data and assess the detection and mitigation results under different parameter settings, for example, interference‐to‐noise ratio, additional RFI sources, frequency resolution, and input data size. We demonstrate the real performance of the method by analyzing radio astronomy data (∼1.3 GHz) acquired with the L‐wide band receiver at the Arecibo Observatory, which is typically corrupted by active air surveillance radars located nearby. Our CSS‐based solution enables robust and efficient detection of the RFI frequency bands present in the L‐band data, and subsequent excision by blanking is also demonstrated.

Design of radar display of Indonesian airspace monitoring application

In this research, the design and manufacture of radar display software using Plan Position Indicator (PPI) format for air surveillance radar application are presented. The PPI display shows interpretations of echo detections of radar signal reflected from the flying objects/targets. The detection results will be displayed on a circular 360ᵒ area, where the radar position is at the center. System configuration is done via interface from the display, by adjusting the level of transmit signal, by setting the gain for threshold, and by enabling moving target indicator (MTI) mode. The MTI mode only displays moving objects and no non-moving objects are shown such as mountains and buildings. Based on the results of this research, the PPI display shows the targets on the display according to its position, some desired targets can also be tracked, information on target, GPS location, target ID, required parameters, and some settings. The radar display fulfills all the required capabilities for air surveillance radar.

A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking.

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

A novel hybrid model for inversion problem of atmospheric refractivity estimation

Atmospheric refractivity estimation is an important issue for performance evaluation of communication systems and air surveillance radars. A novel hybrid model based on artificial neural networks (ANNs) and genetic algorithms (GAs) for inversion problem of atmospheric refractivity estimation is introduced. In this paper, inversion problem and clutter model problem of refractivity from clutter (RFC) method are separated and only inversion problem is studied. A problem specific ANN structure is designed and an original GA is developed to fulfill atmospheric refractivity estimations. In hybrid method, ANNs make pre-estimation and GAs use these results as a starting population for post-estimation. When the results obtained from the single solutions of ANNs and GAs are compared to the results obtained from hybrid model, a significant improvement in the accuracy of estimated results is observed.

Wind Turbine Interference Mitigation Using a Waveform Diversity Radar

Interference from the proliferation of wind turbines is becoming a problem for ground-based medium-to-high pulse repetition frequency (PRF) pulsed–Doppler air surveillance radars. This paper demonstrates that randomizing some parameters of the transmit waveform from pulse to pulse, a filter can be designed to suppress both the wind turbine interference and the ground clutter. Furthermore, a single coherent processing interval (CPI) is sufficient to make an unambiguous range measurement. Therefore, multiple CPIs are not needed for range disambiguation, as in the staggered PRFs techniques. First, we consider a waveform with fixed PRF but diverse (random) initial phase applied to each transmit pulse. Second, we consider a waveform with diverse (random) PRF. The theoretical results are validated through simulations and analysis of experimental data. Clutter-plus-interference suppression and range disambiguation in a single CPI may be attractive to the Federal Aviation Administration and coastal radars.

Application of SysML on a simplified model of an air surveillance radar

Systems Modeling Language (SysML) is a Unified Modeling Language (UML) profile intended for application in systems engineering. This text is inspired by lack of literature on SysML in Serbian language. Its goal is to introduce this interesting modeling language to the engineering and academic community, with a special emphasis on the differences from UML. An overview is given, using an original example - a simplified model of an air surveillance radar. A short analysis of practical aspects of SysML is made - its scope of acceptance, the prospects of its further application, etc. We concluded that SysML is a useful, accepted and promising modeling language.

Bistatic weak target detection method using non-cooperative air surveillance radar

At present, most of the passive radar system researches utilize FM radios, TV broadcasts, navigation satellites, etc. as illuminators. The transmitted signals are not specifically designed radar waveforms. In this work, the frequency agile, phased array air surveillance radar (ASR) is used as the illuminator of opportunity to detect the weak target. The phased array technology can help realize beam agility to track targets from different aspects simultaneously. The frequency agility technology is widely employed in radar system design to increase the ability of anti-jamming and increase the detection probability. While the frequency bandwidth of radar signals is usually wide and the range resolution is high, the range cell migration effect is obvious during the long time integration of non-cooperative bistatic radar. In this context, coherent integration methods are not applicable. In this work, a parametric non-coherent integration algorithm based on task de-interweaving is proposed. Numerical experiments verify that this is effective in weak target detection.

False Target Discrimination of Air Surveillance Radar Based on Track Velocity Modification and Frequency Spectrum Analysis

摘要: 该文研究了防空雷达虚假目标判别问题，针对航迹欺骗干扰、杂波剩余假航迹干扰等能够形成航迹的虚假目标，从回波频谱分析角度着手，结合航迹速度修正处理，提出了一种全新的判别方法。该方法将虚假目标判别归结为二元假设检验问题，推导了以多普勒频率差为统计量的似然比判别表达式，给出了判别门限的计算公式，并基于实际防空雷达数据验证了方法的有效性，为解决防空雷达目标真伪判别提供了有效的技术途径。 关键词: 虚假目标判别 / 防空雷达 / 频谱分析

COHERENT-PULSE RADAR SIGNALS SPACE-TIME AND TIME-SPACE FILTERING PERFORMANCE EVALUATION

The article describes the electronic counter-countermeasures of 36D6 and 80K6 air surveillance radars in jamming and clutter conditions. The algorithm of space-time processing that uses jamming signal cancelling at the first stage and Doppler filtering at the second one has been described. The algorithm of time-space processing that performs main and auxiliary channel signals Doppler filtering at the first stage has also been described. Jammer signal cancelling is performed in frequency domain at the second stage. The efficacy of both systems by simulation using actual interference signals recorded in real life conditions has been determined. The simulation results showed that the space-time filter with use of adaptation inside the coherent processing interval (CPI) is more effective than the same filter that adapts between CPIs. Time-space filtering in jamming and clutter conditions has advantages over space-time filtering. The efficiency of time-space filtering is limited because of Doppler filter magnitude response sidelobes.

Radar Detection of Helicopters with Application of CLEAN Method

We discuss detection and identification of helicopters in an air surveillance radar (ASR). The presented solution consists in detecting characteristic elements of the helicopter echo, with application of a CLEAN method for rejection of the fuselage echo that masks weaker components of the signal. This enables identification of rotor blade and hub echo, and, finally, identification of a helicopter. All ideas presented have been verified with synthetic signals, as well as with real recorded signals.

A Pseudo-Measurement Approach to Simultaneous Registration and Track Fusion

In multi-sensor tracking, registration is expected to be performed at the track level instead of the measurement level especially for the distributed sensor networks. However, registration at the track level becomes more difficult due to the implicit sensor biases hidden behind the local tracks. We propose a pseudo-measurement approach to solve the simultaneous registration and fusion problem at the track level. A pseudo-measurement equation is derived from the local trackers, which explicitly reveals the relationship between the pseudo-measurements and the sensor biases in a closed-form expression. The resulting registration model then allows us to formulate the track registration and fusion as a maximum likelihood (ML) estimation problem. We propose using the expectation maximization (EM) approach to perform track registration and fusion simultaneously. Both batch and recursive EM algorithms are developed, accompanied by the performance analysis. Simulation results demonstrate that both EM algorithms are capable of providing accurate estimates. Moreover, we apply the proposed method to an air surveillance radar network which suffers from relatively serious registration problems. The proposed method is verified to effectively fuse and register the tracks generated by local radars and to provide a consistent air picture.

Modernization of En Route Air Surveillance Radar

The design of a service life extension program that is being applied to the modernization of continental U.S. en route air surveillance radar known as long range radar (LRR) network is presented. The LRR network consists of 69 L-band radars that are used for the joint purposes of air traffic control and surveillance. Initially the architecture of the radar system is presented and key components selected for upgrade are identified. In addition to hardware upgrades innovative signal processing algorithms have also been added. The upgraded radar consists of a solid state transmitter, a digital receiver, and a signal data processor. With advanced signal processing algorithms, the upgraded radar system provides 200 nmi coverage in natural interference environments while minimizing the false alarms. The radar has also been upgraded to enhance weather detection performance.

Continuous–discrete estimation for tracking ballistic missiles in air-surveillance radar

A problem of tracking tactical ballistic missiles (TBMs) in a long-range air-surveillance radar is considered. An effective tracking algorithm is presented, which is based on continuous-time dynamic models of object motions and on a discrete-time model of measurement process. The estimator utilises an innovative combination of kinematic models of TBM movements in radar ‘range–azimuth–elevation’ coordinates, continuous–discrete extended Kalman filtering, interacting multiple-model state estimation and probabilistic data association. The performance and robustness of the new algorithm was proved by extensively testing using real and simulated radar data. For the purpose of this study its properties are illustrated using simulations.