L. Anjaneyulu

Identification of LPI Radar signals by higher order spectra and neural network techniques

LPI Radars use continuous wave, wide bandwidth low power signals of the order of a few watts making its detection difficult. The important advantage of LPI radar is to go undetected, while maintaining a strong battlefield awareness. Common spectral analysis and conventional methods fail to detect emissions of LPI radars and even normal radars in noisy environments. This leads to use higher order spectral analysis (HOSA) techniques enabling us to extract much more information from the same intercept and hence facilitating detection. This paper reports the results of HOSA techniques (bi-spectrum, bi-coherence and tri-spectrum) and artificial neural networks (ANNs), applied to LPI radar signals. Bi-phase barker coded signals of different lengths, P1, P2, P3 and P4 Polyphase coded signals and Frank signal are analyzed using HOSA techniques to produce 2-D signatures of these signals. An artificial neural network (ANN) is trained on these signatures so that it will be able to detect and identify the LPI radar signal whose type is unknown when received. The results obtained clearly indicate the promising capability of these techniques to identify the type of LPI signal even with SNRs as low as -3 dB.

Identification of LPI radar signals by higher order spectra and neural network techniques

LPI Radars use continuous wave, wide bandwidth low power signals of the order of a few watts making its detection difficult. The important advantage of LPI radar is to go undetected, while maintaining a strong battlefield awareness. Common spectral analysis and conventional methods fail to detect emissions of LPI radars and even normal radars in noisy environments. This leads to use higher order spectral analysis (HOSA) techniques enabling us to extract much more information from the same intercept and hence facilitating detection. This paper reports the results of HOSA techniques (bi-spectrum, bi-coherence and tri-spectrum) and artificial neural networks (ANNs), applied to LPI radar signals. Bi-phase barker coded signals of different lengths, P1, P2, P3 and P4 Polyphase coded signals and Frank signal are analyzed using HOSA techniques to produce 2-D signatures of these signals. An artificial neural network (ANN) is trained on these signatures so that it will be able to detect and identify the LPI radar signal whose type is unknown when received. The results obtained clearly indicate the promising capability of these techniques to identify the type of LPI signal even with SNRs as low as -3 dB.

Radar emitter classification using self-organising Neural Network models

This paper presents a radar emitter identification and classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitterpsilas parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately identify and classify the emitter types.

A compact reconfigurable antenna with frequency, polarization and pattern diversity

This paper presents a novel, compact and simple reconfigurable antenna using coplanar waveguide feed which has frequency, polarization and pattern diversities. The basic antenna consists of four rectangular patches of dimensions 5 mm × 4 mm. Three switches are used to connect these four patches and with different switching modes, the desired reconfigurability is achieved. The operating frequency/band can be selected as 5.15 GHz (with 33.6% of impedance bandwidth) or 5.55 GHz (with 55% of impedance bandwidth) in two different operating modes. Reconfiguration in polarization takes place from linear polarization to right hand circular polarization and left hand circular polarization, and antenna is capable of rotating its radiation beam by ±180°. The measured results are in good agreement with simulated ones. The antenna exhibits good return loss characteristics, acceptable gain and VSWR.

A CPW-fed reconfigurable patch antenna with circular polarization diversity

This paper presents a simple and compact coplanar waveguide (CPW)-fed circular-shaped reconfigurable patch antenna with a switchable circular polarization (CP) sense. The circular patch is cut at the ends vertically and switches are introduced to connect the patch ends. By controlling the ON/OFF status of the two switches, the polarization of the antenna can be switched between two states: left-hand circular polarization and right-hand circular polarization. The patch is designed on a very thin RT Duroid substrate of dielectric constant (e r ) of 2.2 and thickness of 0.254 mm. The overall antenna dimensions are 35 × 30 mm. The antenna is designed and simulated using finite-element method -based EM simulator, HFSS. For each switching condition the return loss curve, radiation pattern are obtained. Axial ratio curves for polarization diversity cases are also plotted. Parametric studies have been made in order to get optimized values for certain antenna dimensions such as thickness, CPW ground to feed gap, etc.

AN AVERAGE VELOCITY DISPLAY CIRCUIT FOR KA-BAND CW DOPPLER RADAR

This paper describes the use of doppler speedometers using radar are finding potential applications in automotive and railway collision avoidance systems. Focus is on a gated digital display circuit to indicate the relative velocity of a moving vehicle. This paper presents the design philosophy and the hardware description of the average velocity display circuit along with the test results.