In this work, a frustum-shaped conformal antenna array for Direct Air-to-Ground Communication (DA2GC) is designed and studied. The requirement calls for an omnidirectional radiation pattern, however the simple monopole or dipole is constrained only to the azimuth plane/yaw and might have deep null in elevation plane due to the aircraft body effect. This work proposes an eight elements conformal array operating at C band frequency covering 4.4 to 5.0 GHz, whose substrate has the shape of a frustum, which provides a solution that meets all the requirements. This design achieves less than 5 dB gain variation across azimuthal coverage and ensures sufficient gain at shallow elevation angles. A passive conformal array on a frustum fed with a microstrip-based $1: 8$ power divider design with results are presented first. Later, an active conformal array with switchable beam is explained. Further, a comprehensive analysis of implementation brought out, indicating that the proposed will be better switchable sectoral coverage is acceptable. This analysis also indicates that the active array configuration requires reduced power due to the increased gain of the array and as it covers only a sector at a given time.

In this paper, a 2 inverted U-slot integrated rectangular patch antenna array is proposed that can be used for S band applications i.e., radar applications, communication and wireless networks. In the proposed antenna design 2 inverted U slots are introduced in the patch to improve bandwidth providing a bandwidth of 150 MHz and gain of 4.08 dBi resonating at 3.3 GHz. Furthermore, 4 X 4 antenna array is fabricated on 1.6 mm thick FR4 substrate with dielectric constant 4.4 providing a gain of 14.2 dBi. Ansys HFSS is used for antenna simulation and a prototype is fabricated and tested.

A novel photonic sub-Nyquist based wideband frequency measurement system based on a pulsed laser, a set of interleavers and a binary deduction algorithm is proposed and verified with simulations.

Data fusion technique combines data from two similar sensors placed in different location in order to reduce the error in filtered state estimate. In this paper, state vector fusion (SVF) and measurement fusion (MF) are used to fuse the bearing measurement and also to fuse the derived heading measurement. The derived heading from bearing measurement increases the accuracy of target state estimate. Here, the Lagrange three point difference (LTPD) method has been proposed to derive heading from the set of bearing measurements. Two sensors with single target scenario are considered and the heading parameters are derived for each sensor. The bearing and derived heading measurements from two different sensors are fused using SVF or MF and then nonlinear Extended Kalman filter (EKF) is used to obtain the optimized state estimate. Simulations have been carried out in order to compare the SVF and MF fusion techniques for the bearing measurements as well as the derived heading parameters using existing centered difference (CD) and proposed LTPD.

In general, bearing measurements combined with the derived heading increases the accuracy of target state estimate in bearing only tracking (BOT). Here, Lagrange five point difference (LFPD) method is proposed for deriving the heading measurements. This method is compared with Lagrange three point difference (LTPD) and centered difference (CD) method by considering the scenario of two sensors tracking a single target. The bearing and derived heading measurements from two sensors are fused using measurement fusion (MF) technique. The nonlinear Extended Kalman filter (EKF) is implemented using fused measurement to obtain the optimized target state estimate. Performance analyses are made between LFPD compared with LTPD and CD by considering without and with fusion technique. Simulation results indicate MF-LFPD performs comparatively better.

This paper discusses the application of Lagrange interpolation and cubic spline interpolation to predict acceleration/velocity and to adapt better window lengths for any range of target acceleration. The estimated velocity/acceleration is then smoothed using Kalman and adaptive Kalman filters. Simulation results show that in a 'high-level noise' scenario, the interpolated adaptive filter gives a more accurate estimation than the existing method of using a rectangular window function. Track initialization error minimized with spline interpolation was comparable to that minimized with Lagrange interpolation.

Relative velocity obtained through estimated Doppler frequency is often prone to error because of high noise and the target maneuverings. Better performance is achieved by applying adaptive length window functions [1] to the target echo signal. The optimum length is primarily dependent on target dynamics, expressed in terms of its acceleration and signal to noise ratio of the obtained echo. Papic et al [1] gave lookup table to select the window length for different signal-to-noise ratios (SNR) at three fixed accelerations. Other than these target accelerations, approximating the window length leads to error in the velocity estimation to low SNR. In this paper we have used Lagranges interpolation technique to get better window length adaptation for any range of target acceleration. The estimated velocity is then smoothened using Kalman filter and adaptive Kalman filter. Simulated results show that in high noise level scenario, our interpolated adaptive filter gives better estimation in comparison to the existing methods[1].

Estimating the position of a moving target in the polar frame of reference has been a major problem in the conventional tracking systems. The commonly used sensor equipment provides the position of target in polar coordinates i.e. in range and azimuth (or bearing) angle with respect to the sensor location. The use of simple Kalman filter increases the error in this case. For more accurate tracking, the Converted Measurement Kalman filter (CMKF) is used which can account for the inaccuracies in tracking using polar coordinates. Simulations were performed tracking the target with CMKF. It does not proffer well in missed detection and false alarm scenarios. So the tracking was improved by associating Global nearest Neighbour algorithm (GNN) algorithm with the CMKF. Later the simulations depict the inconsistency of GNN based CMKF in a dense missed detection and false alarm scenarios. So an improved algorithm in track estimation is used to solve the dense missed detections or continuous false alarm problem. Thus, through simulation, it was realized that GNN based improved CMKF is able to give a better tracking. Finally Wiener filtering is used to smoothen the tracking.

Data fusion techniques combine data from multiple sensors, and related information from associated databases, to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. This paper presents the fusion using the generalized quasilinearization technique to obtain a monotone sequence of iterates, converging uniformly for the tracks, obtained after association. Likelihood ratio based cost for association with kinematic information [1] is used for track-to-track association (T2TA). These associated tracks and the fused track are smoothened using Kalman filter (KF). Simulated results through MATLAB are compared with the state vector fusion technique. The main advantage of the proposed method is that fusion follows the actual track where as conventional method results are based on the Kalman estimates.

This paper analyses the velocity estimation of a target, from the Doppler filter using 1) Kalman filter 2) Adaptive Kalman filter 3) Kalman filter with state vector fusion 4) Adaptive Kalman filter with state vector fusion 5) State vector fused adaptive Kalman filter.Simulation through MATLAB gave good response for 4 th and 5 th algorithms under low signal to noise ratio. 2 nd and 3 rd algorithms gave better results in intensive maneuvers.But 1 st algorithm even though it is low cost and faster, fails due to the delay in response.