Vishal Cholapadi Ravindra

Projectile Identification and Impact Point Prediction

This paper presents a multiple model procedure to estimate the state of a ballistic object in the atmosphere and identify it using radar measurements for the purpose of impact point prediction (IPP). A key aspect of the projectile identification is the identification of the mode of stabilization used, i.e., fin stabilization or spin stabilization. Measurements are taken during the first part of its trajectory up to apogee, and the final state estimate obtained by the multiple model estimator is then predicted to its impact point on Earth. For each model a different extended Kalman filter (EKF) is used for state estimation, and the model likelihoods are then used to identify the projectile. It is shown from simulations carried out on three fin-stabilized projectile trajectories (mortars of different caliber) and a spin-stabilized (howitzer) projectile trajectory that the projectile can be identified with a high probability and also that the impact point is predicted to a high degree of accuracy and with a consistent covariance. It is also shown that accurate modeling of the gyroscopic effect caused by the spinning of the howitzer projectiles is critical for IPP accuracy in the case of spin-stabilized projectiles. The key in the design of the multiple model filter (MMF) is the choice of the models, which based on the characteristics of the different projectile trajectories, have different state dimensions. A choice has to be made between too few state components, which leads to poor accuracy/consistency, and too many state components, in which case the accuracy and discrimination ability suffers because of too much uncertainty in the model.

Impact point prediction and projectile identification

This paper presents a multiple model procedure to estimate the state of a ballistic object in the atmosphere and identify it using radar measurements. The measurements are taken during the first part of its trajectory and the final state estimate is then predicted to its impact point on earth. This paper uses, for each model, a different extended Kalman filter for state estimation and then uses the model likelihoods to identify the projectile. Simulations are carried out on three mortar trajectories using 7-state models. It is shown from simulations carried out on several ballistic trajectories that the impact point is predicted to a high degree of accuracy and with a consistent covariance and that the projectile can be identified with a high probability.

Aim Identification of Missiles with a Minimal Parameter Set

Considered here is the problem of using passive (line-of-sight angle) observations of a surface-to-air or an air-to-air missile (pursuer) from an aircraft (evader), to infer whether the missile is or is not aimed at the aircraft. The observations are assumed to be made only on an initial portion of the pursuers trajectory. The approach is to model the trajectory of the missile with a number of kinematic and guidance parameters, estimate them, and use statistical tools to infer whether the missile is guided toward the aircraft or not. A mathematical model is presented for a missile under pure proportional navigation (PPN) with a changing velocity (direction change as well as speed change), to intercept a nonmaneuvering aircraft. A maximum likelihood (ML) estimator is used for estimating the missiles motion parameters and a goodness-of-fit test is formulated to test if the aircraft is the aim or not. Using measurement data from several realistic missiles aimed at an aircraft, it is shown that the proposed method can solve this problem successfully. The key to the solution, in addition to the missile model parametrization, is the use of a reliable global optimization algorithm such as a genetic algorithm (GA) for the MLE. The estimation/decision algorithm presented here can be used for an aircraft to decide, in a timely manner, whether appropriate countermeasures are necessary.

Rapid aim identification for surface to air missiles with a hierarchical search

This work deals with the following question: using passive (line-of-sight angle) observations of a multistage surface to air missile from an aircraft, how can one infer that the missile is or is not aimed at the aircraft. The observations are assumed to be made only on the initial portion of the missiles trajectory. The approach is to model the trajectory of the missile with a number of kinematic and guidance parameters, estimate them and use statistical tools to infer whether the missile is guided toward the aircraft or not. A mathematical model is presented for a missile under pure proportional navigation with a changing velocity (direction change as well as speed change), to intercept a nonmaneuvering aircraft. A maximum likelihood estimator (MLE) is used for estimating the missiles motion parameters and a goodness-of-fit test is formulated to test if the aircraft is the aim or not. Using measurement data from several realistic missiles - single stage as well as multistage - aimed at an aircraft, it is shown that the proposed method can solve this problem successfully. The key to the solution, in addition to the missile model parametrization, is the use of a reliable global optimization algorithm with a hierarchical search technique for the MLE. The estimation/decision algorithm presented here can be used for an aircraft to decide, in a timely manner, whether appropriate countermeasures are necessary.

Removing ping timing ambiguity via data association

This paper considers the use of a multiple ping active sonar approach in order to track multiple targets. In most underwater target tracking applications that rely on active sonar observations, the sonar sends out a single ping at each scan interval and receives returns that might either be from target(s) or due to clutter. We propose to use a multi-ping paradigm, with the idea that the better detectability of more pings can, assuming a high clutter density, lead to better localization of targets. However, this introduces a timing ambiguity in the ping returns adding a new level of complexity in the data association. In this paper we propose a multi-ping data association (MPDA) algorithm as a solution. MPDA formulates the assignment problem as a linear program, which could then be solved using a primal-dual interior point approach. A comparison is made between three different scenarios: (a) the sonar sends out a single ping in each scan interval; there is no timing ambiguity (which return is due to which ping), (b) the sonar sends out multiple pings within a scan interval and the timing ambiguity is avoided by using orthogonal waveforms for each ping, (c) when the sonar sends out multiple pings, each ping being an identical waveform, within each scan, leading to a timing ambiguity. A well known multiple target tracking technique such as the JPDA is used in cases (a) and (b), while the MPDA algorithm is used to solve the assignment problem in case (c), and is shown to perform better than case (a) in high clutter densities. Case (b), the unrealizable bogey, performs best.

Aim Identification with a Minimal Parameter Set

This paper considers the problem of using passive (line-of-sight angle) observations of a surface to air or an air to air missile (pursuer) from an aircraft (evader), to infer whether the missile is or is not aimed at the aircraft. The observations are assumed to be made only on an initial portion of the pursuers trajectory. The approach is to model the trajectory of the missile with a number of kinematic and guidance parameters, estimate them and use statistical tools to infer whether the missile is guided toward the aircraft. A mathematical model is presented for a missile under pure proportional navigation with a changing velocity (direction change as well as speed change), to intercept a nonma-neuvering aircraft. A maximum likelihood estimator (MLE) is used for estimating the missiles motion parameters and a goodness-of-fit test is formulated to test if the aircraft is the aim or not. Using measurement data from several realistic missiles aimed at an aircraft, it is shown that the proposed method can solve this problem successfully. The key to the solution, in addition to the missile model parametrization, is the use of a reliable global optimization algorithm for the MLE. The estimation/decision algorithm presented here can be used for an aircraft to decide, in a timely manner, whether appropriate countermeasures are necessary.

Feature-aided localization and tracking of ground vehicles using passive acoustic sensor networks

Tracking of a moving ground target using acoustic signals obtained from a passive sensor network is a difficult problem as the signals are contaminated by wind noise and are hampered by road conditions, terrain and multipath, etc., and are not deterministic. Multiple target tracking becomes even more challenging, especially when some of the vehicles are light (wheeled) and some are heavy (e.g., tracked vehicles like tanks). In such cases the stronger acoustic signals from the heavy vehicles can mask those from the light vehicles, leading to poor detection of such targets. Acoustic passive sensor arrays obtain direction of arrival (DoA) angle estimates of such emitters from the received signals. The full position estimates of targets, obtained following the association of the DoA angle estimates from least three sensor arrays, are used for target tracking. However, because of the particular challenges encountered in multiple ground vehicle tracking, this association is not always reliable and thus, target tracking using such full position measurements only is difficult and it can lead to lost tracks. In this paper we propose a new feature-aided tracking (FAT) algorithm to augment the existing target tracking algorithms which use only kinematic measurements, in order to improve the tracking performance. We present a novel DoA detection technique followed by frequency domain feature extraction from real data. The techniques are developed based on real data sets and tested on real data based on a field experiment.

Communication Channel Equalization-Pattern Recognition or Neural Networks?

The communication channel equalization is a difficult problem, especially when the channel is nonlinear and complex. Numerous algorithms are presented in the neural networks literature to solve this problem. In this paper, a comparison is made among the latest neural network techniques (Complex Minimal Resource Allocation Networks (CMRAN) [1]), a classical communication technique (Viterbi algorithm), and two pattern recognition techniques (Support Vector Machine (SVM), Learning Vector Quantization (LVQ)) to solve this problem. The simulation results show that Viterbi (MLSE decoding technique), and SVM methods outperform the CMRAN method.