Analysis of radar detection performance for low altitude small target

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method.

Polarization cancellation is an effective method to improve the radar target detection performance under the main lobe interference environment. Interference suppression ratio is widely used to assessment polarization filter performance. However, the radar target detection performance is determined by the Signal Interference plus Noise Ratio (SINR). It is important to analyze the polarization filter's effect on the improvement of SINR. The paper proposes a mathematical model of the relationship between the changes of SINR with the relevant factors under different interference conditions. The relevant factors conclude the degree of polarization (DoP) of interference and the polarization differences between the target with interference. Based on this model, the effect of relevant factors on radar target detection performance is described. Finally, the model is tested and verified by radar target detection experiments under the jamming conditions. The results of the paper will provide a reference to design the anti-jamming configuration for the polarimetric radar.

During radar operation, changes in the radar cross section (RCS) of a target have an adverse effect on the radar detection performance. In particular, the RCS of a target with high maneuvering characteristics can be changed rapidly. Therefore, a precise prediction of the RCS change is essential for satisfactory radar detection performance. In this study, a modeling and simulation (M&S) tool was developed for the estimation of radar detection performance. The M&S tool comprises a radar model, target model, and wave propagation model. In particular, pre-obtained RCS simulation data were utilized to consider the effect of the RCS change of the target. Using the developed tool, the RCS based on the relative attitude is extracted from the RCS simulation data and the RCS processed data using the moving average method. The M&S model is verified by comparing the two test results with the radar received signal power. Consequently, we verified that when using the RCS manufacturing data, the signal power is more similar to the test data than the other RCS simulation methods. It is expected that the radar performance on the high-maneuvering target can be estimated using the developed M&S tool.

Radar sensors are an attractive technology for different kinds of applications, especially, in the case of safety driving systems. This research deals with designing the target return signal detection algorithm for radar sensors using the generalized detector (GD) with adaptive detection threshold based on a sliding window technique to estimate the noise power. The general GD structure is introduced, and the proposed method to define the adaptive detection threshold is assumed to be employed by 24 GHz frequency modulation continuous wave (FMCW) radar sensor and the simulation results show the better detection performance in comparison with the cell averaging constant false alarm rate (CA-CFAR) detector one which is widely used by radar sensor systems for car applications.

Since fiber distributed vibration sensing (DVS) system based on phase-sensitive optical time domain reflectometer (Φ-OTDR) is extremely sensitive to the ambient environment, how to improve signal-to-noise ratio (SNR) and obtain vibration signal from background noise has become a key problem in the field. In this paper, firstly, based on the analysis of the wavelet high frequency component location algorithm and the separating averaging and differential algorithm, an improved separating averaging and differential algorithm is proposed by introducing an interval. Then cell average constant false alarm rate (CA-CFAR) detection algorithm is used to set the adaptive threshold. Finally, the continuous vibration section is found according to the influenced-spatial scale of the pipeline intrusion vibration, and the middle position of the section is judged to be the vibration position. The experimental results show that the positioning algorithm has less positioning error, shorter runtime and higher SNR. The SNR can be as high as 9.3483dB and the computation amount of the next identification module is reduced.

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

We propose an analytical framework based on stochastic geometry (SG) formulations to estimate a radar's detection performance under generalized discrete clutter conditions. We model the spatial distribution of discrete clutter scatterers as a homogeneous Poisson point process and the radar cross-section of each extended scatterer as a random variable of the Weibull distribution. Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section. We derive the optimum radar bandwidth for maximizing this metric under noisy and cluttered conditions. We also derive the peak transmitted power beyond which there will be no discernible improvement in radar detection performance due to clutter limited conditions. When both transmitted power and bandwidth are fixed, we show how the detection threshold can be optimized for best performance. We experimentally validate the SG results with a hybrid of Monte Carlo and full wave electromagnetic solver based simulations using finite difference time domain (FDTD) techniques.

Linearly combined order statistic (LCOS) constant false-alarm rate (CFAR) detectors are examined for efficient and robust threshold estimation applied to exponentially distributed background observations for improved detection. Two optimization philosophies have been employed to determine the weighting coefficients of the order statistics. The first method optimizes the coefficients to obtain efficient estimates of clutter referred to the censored maximum likelihood (CML) and best linear unbiased (BLU) CFAR detectors. The second optimization involves maximizing the probability of detection under Swerling II targets and is referred to as the most powerful linear (MPL) CFAR detector. The BLU-CFAR detector assumes no knowledge of the target distribution in contrast to the MPL-CFAR detector which requires partial knowledge of the target distribution. The design of these CFAR detectors and the probability of detection performance are mathematically analyzed for background observations having homogeneous and heterogeneous distributions wherein the trade-offs between robustness and detection performance are illustrated. >

In distributed microwave photonic (MWP) radar systems, the detection performance is highly sensitive to the quality of optical signals transmitted over the long fibers connecting the indoor and outdoor units. In this Letter, we propose an MWP radar system that, in addition to its standard range measurement capability, integrates optical frequency domain reflectometry (OFDR) for distributed vibration sensing along the optical fibers. A carrier-suppressed double-sideband (CS-DSB) optical signal is generated in the indoor unit, which serves as a light source and the local reference for the OFDR, as well as a transmitted signal for the radar transmitter. For radar detection, an echo received from an antenna is sent back to the indoor unit, where it is mixed with the local reference to perform de-chirping. For OFDR, a beat signal is produced by the coherent interference of the backscattered Rayleigh signal with the local reference, which realizes distributed optical fiber sensing. The dual functions of the system are evaluated by an experiment. For range measurement, a resolution of 4.8 cm at a transmitted bandwidth of 4 GHz is obtained. For distributed sensing, a reflection point detection with a resolution of 6.7 cm and a vibration measurement with a spatial resolution of 6 m at a vibration frequency of 4 kHz are demonstrated. The experiment also verified that utilizing OFDR to determine the vibration frequency, its impact on radar detection performance can be mitigated through digital signal processing.

High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

In this paper, we propose a parallel processing architecture, based on two TMS320C44 VME-bus DSP boards, for real time implementation of Cell Averaging and Clutter Map Constant False Alarm Rate (CFAR) detectors, and a data extractor based on a centroidal interpolation of the position of the detected target. The optimal processing speed has been achieved by fully exploiting the capacities of the dasiaC44 processor. The implemented system is well adapted for two dimension radars. The overall processing scheme interconnections and the real time implementation results are presented and discussed.

In this paper, we are interested in localizing acoustic sources such as gunfire of Automatic Pistol (AP) and Kalashnikov AK-47 via the Imote2 wireless sensor node platform, using an array of four synchronized microphones connected to ITS400 sensor board of Imote2. More particularly, this work concerns the implementation of a localization algorithm based on the TDOA (Time Difference of Arrival) between the acquired signals by the microphones. This work can be divided into two parts. The first part is dedicated to the realization of an Imote2-based platform, which allows the acquisition of acoustic signals. In the second part, we have evaluated the performance of relative TOA (Time of Arrival) estimation algorithms, where CA-CFAR (Cell Averaging Constant False Alarm Rate) algorithm gave the best results.

Periodically through the year, an Atlantic coast vessel traffic control radar, located near the entrance to the Delaware Bay, observes a reduction in detection range from 37 (20 nmi) to about 17 km (9 nmi). Sometimes ships can be seen visually from the radar tower before they can be observed on the radar screen. The reduction in the radar detection range usually lasts several hours and occurs often when fog is present. An investigation of this phenomenon, referred to as a radar deep fade (RDF), was undertaken to find out what causes the fade and to determine the best method of increasing the radar detection range when one occurs. The investigation indicated that these RDFs arose during the presence of a propagation condition known as subrefraction, which is an increase in the atmospheric index of refraction with altitude above the ocean surface at microwave frequencies. This increase in the index of refraction with altitude results in the effective radius of the Earth being much smaller than the usual 4/3 Earth radius that is observed for radar during standard atmospheric conditions. During subrefractive conditions, the effective Earth's radius can be between its true radius and one-half of its true radius. As a result, the radar horizon is shortened and the radar detection range is reduced. These RDFs will occur during propagation conditions known as sustained deep fades (SDFs), which arise when subrefraction lasts for more than two hours and causes one way losses exceeding 20 dB relative to free-space for line-of-sight (LOS) geometries. Subrefraction is caused by the movement of subtropical moist air over the cold ocean surface. The recommended solution for increasing the radar range in the presence of subrefractive conditions is to place the radar on a higher tower than the present 19-m-high tower, one that is perhaps 60-m high. This would increase the radar range by 50% when the Earth's effective radius is half of normal. This increase in radar height also usually increases the radar detection performance when skip zones associated with surface-based ducts occur or when evaporation ducts (EVDs) are present.