Detection of UAV Target Based on Continuous Radon Transform and Matched Filtering Process for Passive Bistatic Radar

Long-time integration technique is an effective way of improving target detection performance for unmanned aerial vehicle(UAV)in the passive bistatic radar(PBR),while range migration(RM)and Doppler frequency migration(DFM)may have a major effect due to the target maneuverability.This paper proposed an innovative long-time coherent integration approach,regarded as Continuous Radon-matched filtering process(CRMFP),for low-observable UAV target in passive bistatic radar.It not only mitigates the RM by collaborative research in range and velocity dimensions but also compensates the DFM and ensures the coherent integration through the matched filtering process(MFP).Numerical and real-life data following detailed analysis verify that the proposed method can overcome the Doppler mismatch influence and acquire comparable detection performance.

Improving Satellite-Retrieved Cloud Base Height with Ground-Based Cloud Radar Measurements

Cloud base height(CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a lookup table(LUT) of effective cloud water content(ECWC), representing the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness(CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparative analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar(MMCR) measurements, and results show that the mean bias(correlation coefficient) is0.18±1.79 km(0.73), which is lower(higher) than 0.23±2.11 km(0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar(i.e., Cloud Sat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing networks, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.

Coarse-fine joint target parameter estimation method based on AN-RSC in OFDM passive radar

In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

Ambiguity function analysis and side peaks suppression of Link16 signal based passive radar

Link16 data link is the communication standard of the joint tactical information distribution system(JTIDS)used by the U.S.military and North Atlantic Treaty Organization,which is applied as the opportunistic illuminator for passive radar in this paper.The time-domain expression of the Link16 signal is established,and its ambiguity function expression is derived.The timedelay dimension and Doppler dimension side peaks of which lead to the appearance of the false target during target detection.To solve the problem,the time-delay dimension and Doppler dimension side peaks suppression methods are proposed.For the problem that the conventional mismatched filter(MMF)cannot suppress the time-delay dimension side peaks,a neighborhood MMF(NMMF)is proposed.Experimental results demonstrate the effectiveness of the proposed methods.

A NOVEL ALGORITHM FOR SIDE PEAKS SUPPRESSION OF AMBIGUITY FUNCTION FOR PASSIVE RADAR BASED ON CHINESE DTTB SIGNAL

The ambiguity function of Chinese standard Digital Television Terrestrial Broadcasting (DTTB) signals for passive radar contains one main peak and many side peaks. The side peaks may cause the false alarms. The relative positions and the reasons for the side peaks are analyzed and a new algorithm for side peaks suppression is proposed in this paper. The algorithm, in consideration of the characteristics of the structure of the frame, can eliminate the side peaks completely in the valid Doppler observation interval by setting the reference signals to zero at equal intervals. Both the simulative and experimental results show that this algorithm can improve the performance of target detection of the passive radar based on DTTB signal.

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Global Navigation Satellite System(GNSS)-based passive radar(GBPR)has been widely used in remote sensing applications.However,for moving target detection(MTD),the quadratic phase error(QPE)introduced by the non-cooperative target motion is usually difficult to be compensated,as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective.Consequently,the moving target in GBPR image is usually defocused,which aggravates the difficulty of target detection even further.In this paper,a spawning particle filter(SPF)is proposed for defocused MTD.Firstly,the measurement model and the likelihood ratio function(LRF)of the defocused point-like target image are deduced.Then,a spawning particle set is generated for subsequent target detection,with reference to traditional particles in particle filter(PF)as their parent.After that,based on the PF estimator,the SPF algorithm and its sequential Monte Carlo(SMC)implementation are proposed with a novel amplitude estimation method to decrease the target state dimension.Finally,the effectiveness of the proposed SPF is demonstrated by numerical simulations and pre-liminary experimental results,showing that the target range and Doppler can be estimated accurately.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

Channel estimation in integrated radar and communication systems with power amplifier distortion

To reduce the negative impact of the power amplifier(PA)nonlinear distortion caused by the orthogonal frequency division multiplexing(OFDM)waveform with high peak-to-average power ratio(PAPR)in integrated radar and communication(RadCom)systems is studied,the channel estimation in passive sensing scenarios.Adaptive channel estimation methods are proposed based on different pilot patterns,considering nonlinear distortion and channel sparsity.The proposed methods achieve sparse channel results by manipulating the least squares(LS)frequency-domain channel estimation results to preserve the most significant taps.The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion.Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns.In addition,the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

Two-stage DOA estimation method for passive radar based on sparse representation

In a GPS illuminator based passive radar system,estimation of direction of arriving(DOA) of multiple targets is a difficult problem due to strong interference.A two-stage method combining extensive cancellation algorithm(ECA) and sparse representation is proposed.In the first stage,ECA algorithm is used to eliminate the direct-path and multi-path interference.In the second stage,sparse representation of improved weight constraints based on L1 norm is adopted to estimate DOA and suppress the interference.Simulation results show that the proposed method can effectively estimate DOA in low computation complexity without estimating the disturbance parameter.

Target detection using CDMA based passive bistatic radar

Recently, the code division multiple access （CDMA） waveform exists in the large area across the world. However, when using the CDMA system as the illuminator of opportunity for the passive bistatic radar （PBR）, there exists interference not only from the base station used as the illuminator of opportunity but also from other base stations with the same frequency. And be cause in the CDMA system, the signal transmitted by each base station is different, using the direct signal of one base station can not cancel the interference from other base stations. A CDMA based PBR using an element linear array antenna as both the reference antenna and surveillance antenna is introduced. To deal with the interference in this PBR system, an adaptive temporal cancellation algorithm is used to remove the interference from the base station used as the illuminator of opportunity firstly. And then a robust adaptive beamformer is used to suppress the interference from other base stations. Finally, the preliminary experiment re sults demonstrate the feasibility of using CDMA signals as a radar waveform.

New method for passive radar seeker to antagonize non-coherent radar decoy

Using super resolution direction of arrival（DOA） estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker（PRS） to antagonize non-coherent radar decoy.Considering the power and correlation property between radar and non-coherent decoy,an improved subspace DOA estimation method based on traditional subspace algorithm is proposed.Because this new method uses the invariance property of noise subspace,compared with traditional MUSIC algorithm,it shows not only better resolution in condition of closely spaced sources,but also superior performance in case of different power or partially correlated sources.Using this new method,PRS can distinguish radar and non-coherent decoy with good performance.Both the simulation result and the experimental data confirm the performance of the method.

Three-dimensional Fusion of Spaceborne and Ground Radar Reflectivity Data Using a Neural Network–Based Approach

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Moving target localization for multistatic passive radar using delay, Doppler and Doppler rate measurements

Time delay and Doppler shift between the echo signal and the reference signal are two most commonly used measurements in target localization for the passive radar. Doppler rate, which can be obtained from the extended cross ambiguity function, offers an opportunity to further enhance the localization accuracy. This paper considers using the measurement Doppler rate in addition to measurements of time delay and Doppler shift to locate a moving target. A closed-form solution is developed to accurately and efficiently estimate the target position and velocity.The proposed solution establishes a pseudolinear set of equations by introducing some additional variables, imposes weighted least squares formulation to yield a rough estimate, and utilizes the function relation among the target location parameters and additional variables to improve the estimation accuracy. Theoretical covariance and Cramer-Rao lower bound(CRLB) are derived and compared, analytically indicating that the proposed solution attains the CRLB. Numerical simulations corroborate this analysis and demonstrate that the proposed solution outperforms existing methods.

Target detection method in passive bistatic radar

Owing to the advantages in detecting the low altitude and stealth target,passive bistatic radar(PBR)has received much attention in surveillance purposes.Due to the uncontrollable characteristic of the transmitted signal,a high level range or Doppler sidelobes may exist in the ambiguity function which will degrade the target detection performance.Mismatched filtering is a common method to deal with the ambiguity sidelobe problem.However,when mismatched filtering is applied,sidelobes cannot be eliminated completely.The residual sidelobes will cause false-alarm when the constant false alarm ratio(CFAR)is applied.To deal with this problem,a new target detection method based on preprocessing is proposed.In this new method,the ambiguity range and Doppler sidelobes are recognized and eliminated by the preprocessing method according to the prior information.CFAR is also employed to obtain the information of the target echo.Simulation results and results on real data illustrate the effectiveness of the proposed method.

Study on moving target detection to passive radar based on FM broadcast transmitter

Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference （DPI） suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation（FM） broadcast transmitter is analyzed in this article; Secondly, a space-time-frequency domain synthetic solution to this problem is introduced： Adaptive nulling array processing is considered in the space domain, DPI cancellation based on adaptive fractional delay interpolation （AFDI） technique is used in planned time domain, and long-time coherent integration is utilized in the frequency domain; Finaily, an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator, Simulation results by real collected data show that the proposed method has a better performance of moving target detection.

Data fusion of target characteristic in multistatic passive radar

Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.

Observability and estimability of passive radar with unknown illuminator states using different observations

Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.

RESEARCH ON PERFORMANCE INFLUENCE OF DIRECT-PATH SIGNAL FOR DVB-S BASED PASSIVE RADAR

Passive radar detects moving targets by Cross Ambiguity Function (CAF), which is based on the cross correlation process of the direct-path signal in reference channel and echo signal in receive channel. Thus, the performance of direct-path signal is important to system performance for this type of radar. While the Signal to Noise Ratio (SNR) of direct-path signal is low, it will deteriorate the detection performance. In this paper, how SNR of direct-path signal induces degradation on the SNR of CAF, and how the integration gain affects by integration time are analyzed, both with theoretical analysis and numerical simulation, which are valuable for the R&D of passive radar.

DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar

This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.