FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

Range Ambiguity Elimination in a Short-Range FMCW Radar System

Modified implementation architecture for sinusoidal frequency modulation is introduced to extract the range information from the received radar echo. Range ambiguity problem arises because the range is calculated from the estimated phase of the received signal which is wrapped into （0, 2π]. By integrating Doppler frequency shifts, the variation of range can be estimated and used as an auxiliary information to help eliminating the corresponding range ambiguity. The performance of the new technique is evaluated by simulations. The results show that this technique is robust to sever phase noise and can be used effectively for ambiguity elimination of the modified sinusoidal frequency modulated continuous wave radar.

Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.

Parameters of Exterior Ballistic Feature Points Extraction in Radar Measurement Data by EMD

The problem of measuring exterior ballistic feature points is always difficult to solve and it is essentiale on exterior ballistic measurement.By analysis of radar reflection characteristics and non-stationary echo signals of exterior ballistic feature points,the echo data of exterior ballistic feature points is measured by using the continuous wave radar.The parameters of feature points are extracted by the empirical mode decomposition method(EMD)of Hilbert-Huang transform(HHT)spectrum analysis technique.The radar echo signal model and EMD extraction model are established to analyze the exterior ballistic mutation point detection and EMD extraction method of aliasing echo signal.Typical feature point parameters of exterior ballistic in rocket flight tests are carried out and the effectiveness of the method is verified.A new method of measuring the parameters of exterior ballistic feature point is therefore presented.

High-Precision Vital Signs Detection Method Based on Spectrum Refinement and Extended DCMA

In this paper,the spectral estimation algorithm is extended to the detection of human vi-tal signs by mm-wave frequency modulated continuous wave(FMCW)radar,and a comprehensive algorithm based on spectrum refinement and the extended differentiate and cross multiply al-gorithm(DCMA)has been proposed.Firstly,the improved DFT algorithm is used to accurately obtain the distance window of human body.Secondly,phase ambiguity in phase extraction is avoided based on extended DCMA algorithm.Then,the spectrum range of refinement is determ-ined according to the peak position of the spectrum,and the respiratory and heartbeat frequency information is obtained by using chirp z-transform(CZT)algorithm to perform local spectrum re-finement.For verification,this paper has simulated the radar echo signal modulated by the simu-lated cardiopulmonary signal according to the proposed algorithm.By recovering the simulated car-diopulmonary signal,the high-precision respiratory and heartbeat frequency have been obtained.The results show that the proposed algorithm can effectively restore human breathing and heart-beat signals,and the relative error of frequency estimation is basically kept below 1.5%.