Sub-optimal signal processing in marine forward scatter radar

The feasibility of an X-ray Forward Scatter Radar (FSR) system, which exploits X-ray pulsars as opportunity transmitters, is examined. We provide a power budget estimate for detection of large cosmic objects using such a FSR system and assume the use of the high-resolution X-ray telescopes mounted on a satellite as the receivers. The numerical results are obtained for three types of pulsars and small asteroids, whose diameters equal and overcome 50m.

This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal’s joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc.

The forward scatter signature of a target provides the radar systems designer with an alternative approach to the detection of 'very low radar cross section (RCS)' targets. This paper outlines the rationale for a forward scatter radar system, the forward scatter principle, and the architecture of a forward scatter radar systems performance model. This model has been developed to analyse the effect of variations in the choice of operating frequency, transmitter and receiver characteristics, detection and tracking algorithms. The paper discusses these aspects with results from modelling and performance predictions for target forward scatter signature, transmit and receive antenna designs, analogue to digital converter (ADC) performance and coverage for a forward scatter radar system operating in the ultra high frequency (UHF) band.

Bistatic radars, specifically forward-scatter radars, are proposed as an alternative to standard monostatic radars against targets whose radar cross sections (RCS) have been reduced by passive means. Forward-scatter radars operate by detecting echoes from a targets forward-scatter RCS, which is insensitive to effects of passive RCS reduction techniques. However, the performance of the forward-scatter radar is compromised when the angular separation between the interference, which propagates directly from the transmitter to the receiver, and the target return is less than the Rayleigh resolution limit of the receiving antenna. This research presents the results of a parametric study of the ability of a forward-scatter radar to detect and measure the bearing of a large target, whose RCS is reduced via passive means. Super-resolution array processing techniques, particularly root-MUSIC (multiple signal classification), are used to overcome the traditional limitations resulting from the Rayleigh resolution limit of the antenna. The study compares the received power and the bearing measurement accuracy of the forward-scatter radar to that of an equivalent monostatic radar system. The results indicate that forward-scatter radars enjoy advantages in detection and bearing measurement when the backscatter RCS of the target has been reduced and when the target is close to the baseline. The results also indicate that, through the use of super-resolution array processing the capability of the forward-scatter radar to accurately measure the bearing of the target is dependent upon the amount of interference from the direct wave (i.e., the wave which propagates from the transmitter directly to the receiver) and the correlation between the direct wave and the target echo. Good bearing estimates can be achieved if the correlation coefficient is less than 0.95. Bearing measurements may be improved by suppressing the direct wave by either sidelobe control or null steering techniques. >

In a forward scatter radar system, the forward scatter shadow ratio to characterize a target is introduced as the ratio of the total received power density to the incident power density for a receiver at a certain location. Numerical simulations show that the forward scatter shadow ratio is related to the size of the target as well as the position of the receiver relative to the target. It is a more suitable parameter for studying target detectability than the radar cross-section.

Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

The target motion parameter estimation is an important index to measure the performance of radar system. Accurate estimation of acceleration parameter has important influence on improving the detection and tracking performance of high maneuvering target. When radar transmits a signal with fixed frequency, the maneuvering target echo signal can be approximated as linear frequency modulation signal. Therefore, the acceleration parameter estimation of maneuvering target is transformed into the parameter estimation of linear frequency modulation signal. In this paper, a novel method for acceleration parameter estimation of maneuvering target is proposed based on the compressed sensing theory. The linear frequency modulation signal has sparse characteristic in fractional Fourier transform domain and compressed sensing theory asserts that we can employ nonadaptive linear projections to preserve the structure of the signal and reconstruct the signal with these projections using an optimization process. Theoretical analysis and experimental results verify that the method can effectively solve the precision problem of parameter estimation caused by the large sampling rate in the traditional signal processing method, and effectively reduce the data storage and transmission pressure in radar signal processing.

Based on random matrix theory (RMT), a blind target detection algorithm for bistatic MIMO radar is investigated under correlated noise. In relatively snapshot-starving scenarios in which the number of snapshots is comparable in magnitude to the number of the arrays, robust detection performance of the proposed algorithm can be guaranteed. Exploiting the standard condition number (SCN) detector, a novel decision threshold under correlated noise is derived by the use of Stieltjes transform and multiplicative free convolution. The proposed algorithm does not require any prior information of the noise variance and the target scattering matrix. Compared with the existing target detector using M-P law-based threshold, the proposed algorithm significantly improves the robustness of target detection in bistatic MIMO radar under correlated noise environment. Simulations are conducted to illustrate its effectiveness.

Bistatic radars are parts of coherent multistatic radar systems for air targets positioning and tracking in various systems including the Air Traffic Control. The signal processing in the bistatic radar invariably encounters with a competitive signal, reflected from terrain objects - the land clutter. In this paper a research in the area of bistatic radar clutter modeling is presented. After the bistatic clutter scenario is defined two land-clutter models, based on a common general geometry are described. The bistatic radar clutter power equation derived here ([9]) is used for computation of interesting parameters like a bistatic radar effective clutter cross section, a total clutter power and a clutter reflectivity. In the end the two models are compared with actually measured bistatic radar clutter with use of a real terrain profile around the radar.

This paper investigates the use of Deep Learning (DL) in multiple input multiple output (MIMO) radar target detection, focusing on azimuth and elevation estimation. Traditional methods face challenges like interference and reflections, especially in multi-target scenarios. Feature extraction conventionally relies on range correlation, Doppler filtering, and angle beamforming, followed by detection after constant false alarm rate (CFAR) processing. However, early data sparsification by bin selection often leads to information loss, particularly with large data cubes required for practical implementation. Deep Learning techniques offer an alternative, specifically in azimuth and elevation detection at earlier stages of radar data processing. We developed a convolutional neural network (CNN) model that achieved Mean Square Errors (MSE) of 0.149 for azimuth and 0.168 for elevation on single-target data from 5,000 samples. The model's performance in dual-target scenarios showed MSEs ranging from 0.838 to 1.845, tested on 8,000 samples from a dataset of 72,000. This paper details the model development process, its impact on radar target detection, and potential future research directions involving the substitution of multi-bin Deep Learning blocks with traditional methods.

Instrumentation grade, coherent, bistatic radar cross section (RCS) measurement systems require a reliable low-noise method to link the reference, local oscillator (LO) and intermediate frequency (IF) coherent signals between the transmit and receive subsystems. One approach to this is the use of a fiber optic link (FOL). Phase noise tests were performed on a distributed feedback (DFB) type laser transmitter-photodiode receiver link with a delay distance of 2.26 kilometers. The measurements were conducted using a standard Hewlett Packard HP 3048 A phase noise test set, and a variety of link configurations at a variety of frequencies from 100 MHz to 5 GHz. Further, system tests have been performed to incorporate a FOL into a coherent bistatic instrumentation radar system local oscillator path, and perform image processing on an emulated target. A Lintek elan system was configured to emulate bistatic operation by separating the transmit and receive local oscillator lines, allowing one of them to be excited through a fiber optic link. A target with one or two scatterers was emulated using a delay line between a sample of the transmit signal and a receiver injection port. This paper describes the theoretical effects of phase noise (close-in and higher frequency) on target scatterer imaging performance, describes the FOL tests and the architecture of the bistatic radar tests performed, and presents the results of the tests, including baseline system performance and performance using the FOL. Images representing closely spaced scatterers are presented, and the point spread function of a long dwell (40 seconds) data file, representing an ISAR process is presented. One of the limiting factors in the use of a FOL for long dwell times is that of the variable delay, caused by variation of the propagation velocity with laser frequency drift (dispersion), and the change in index of refraction with temperature. A method for mitigating the effects of this drift is presented.

This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection as a proof of the concept. Detecting a drone by using satellite-based passive radar possess inherent challenges, such as the small radar cross section and low speed. Therefore, this paper proposes a unique method by leveraging the advantage of forward-scattering radar (FSR) topology and characteristics to detect a drone; in other words, the system is known as a passive FSR (p-FSR) system. In the signal-processing algorithm, the empirical mode decomposition (EMD) is applied to the received signal to extract the unique feature vector of the micro-Doppler frequency from the drone’s rotating blades. The paper highlights the p-FSR experimental setup and experiment campaign to detect drones. The experimental results show the feasibility of the p-FSR using a signal transmitted from a satellite to detect flying drone crossing the forward-scatter baseline between the satellite and ground station.

In recent years radar target detection environment is more and more complex. Traditional CFAR (Constant False Alarm Rate) is a technology in which the radar system discriminates the output signal and noise of the receiver to determine whether the target signal exists under the condition that the False Alarm probability is kept Constant. In order to improve the radar target detection performance, a radar target detection method based on NN (Neural Network) is proposed. In this paper, the radar signal received by a single RBFNN is used for network training, and the probability of detection target is studied by combining the binary detection theory. Simulation results show that the proposed algorithm can effectively improve the radar target detection probability.KeywordsRadarConstant false alarm rateNeural networkTarget detection

Radar detection of small maritime targets is of great interest in the context of coastal and port security for prevention of activities such as smuggling and piracy. Multistatic radar and forward scatter radar offer detection advantages compared with conventional monostatic systems, such as advantageous multi-perspective target view for the former and target radar cross-section enhancement for the latter. In this study, experimental results are presented to characterise simultaneous measurements investigating the detection of a small inflatable boat by a multistatic radar and a forward scatter radar. These results are believed to be the first example of simultaneous experimental comparison of such systems.

Two classical aspects of signal processing for a monostatic radar are extended to a bistatic radar. These are the determination of target range and relative velocity when using a continuous-wave (CW) transmitter. A simple moving-target indicator using the chirp Z-transform is used to determine the velocity while reducing clutter. A digital phase detector provides the range measurement. The application discussed is the detection of a single target that has jamming capabilities. The bistatic radar has inherent advantages against such a target due to the receiver site being passive and the basic geometry of the radar system. >