Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

LPI radar signal detection based on the combination of FFT and segmented autocorrelation plus PAHT

This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.

Research on LPI radar signal detection and parameter estimation technology

Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.

Resource-saving scheduling scheme for centralized target tracking in multiple radar system under automatic blanket jamming

This paper investigates the problem of Joint Radar Node Selection and Power Allocation(JRNSPA)in the Multiple Radar System(MRS)in the blanket jamming environment.Each radar node independently tracks moving target and subsequently transmits the raw observation data to the fusion center,which formulates a centralized tracking network structure.In order to establish a practical blanket jamming environment,we suppose that each target carries the self-defense jammer which automatically implements blanket jamming to the radar nodes that exceed the preset interception probability.Subsequently,the Predicted Conditional Cramer-Rao Lower Bound(PC-CRLB)is derived and utilized as the tracking accuracy criterion.Aimed at ensuring both the tracking performance and the Low Probability of Intercept(LPI)performance,the resource-saving scheduling model is formulated to minimize the transmit power consumption while meeting the requirements of tracking accuracy.Finally,the Modified Zoutendijk Method Of Feasible Directions(MZMFD)-based two-stage solution technique is adopted to solve the formulated non-convex optimization model.Simulation results show the effectiveness of the proposed JRNSPA scheme.

LPI radar signal detection based on radial integration of Choi-Williams time-frequency image

This paper mainly revolves the time-frequency image of low probability of intercept(LPI) radar signals and carries out research work on image features selection and extraction and recognition. Since Choi-Williams distribution(CWD) uses the exponential kernel of bilinear generalized class of time-frequency distribution, it has an excellent time-frequency aggregation. And it is suitable for detecting LPI radar signals in a low signal-to-noise ratio(SNR) condition. A radial integration method based on the integral rotating factor is proposed to detect LPI radar signals when the signals' time-frequency image is obtained. First, the digital image processing method is used to preprocess the LPI radar signals' time-frequency images after CWD transformation; then, the radial integration method based on the integral rotating factor is used to detect LPI radar signals in the binary images. The analytic results of real data show that the method has a good performance on detecting LPI radar signals in a low SNR condition. Additionally,the method is simple and takes less logic resources and has the potential of real-time detection of LPI radar signals.