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In multi-sensor spatial registration process, the ill-posedness of the information matrix is the main factor which affects the quality of the registration result. This paper focuses on this problem and makes some progress. For simplicity and without loss of generality, we firstly revisit the least square registration algorithm and analyze the ill-posedness of the information matrix and its reasons. To solve the ill-posed problem, a novel matrix singular value modification algorithm, i.e., the Combination Singular Value Decomposition (CSVD) algorithm is proposed by using the trapezoidal distribution characteristic of singular values of Fisher information matrix. The main contribution of the proposed CSVD algorithm is that it can fully preserve the determinate part of information matrix, while effectively suppressing the uncertain part. Finally, simulation results of two different scenarios show that, the proposed CSVD method can significantly improve the stability and accuracy of the sensor spatial bias estimation.

An important prerequisite for successful multi-radar integration is that the data from the reporting radars are transformed to a common reference frame which is free of systematic or registration errors. According to the ship-borne radar data processing, the types of bias are divided into four main categories: radar measurement biases, ship-position biases, attitude biases, and baseline transform biases. In this chapter, we present an algorithm which uses detecting data for estimation of equivalent biases. Our approach is unique for two reasons. First, we explicitly avoid the individual biases and instead use equivalent biases modeling the four main class biases, which leads to a highly nonlinear bias model that contains 12 unknown parameters. Then, we use the singular value decomposition (SVD) within least-squares estimator to automatically handle the issue of parameter observability. Finally, according to two different simulation scenes, we demonstrate that our algorithm can improve track accuracy, especially for ship-borne radar.

An algorithm is presented on-line absolutely registration two 3D radars using targets that are tracked by both radars. The algorithm estimates and removes radar biases and radar frame orientation errors, For illustrative purposes, the algorithm uses the difference in the position of a target, as reported by the two radars, to compute a set of bias parameters. These bias parameters are then applied to align both radar's track positional data. The registration algorithm is applicable to those situations where there are no radar location errors, the magnitude of the bias errors is small, and these errors do not change with time or very slowly with time.

An algorithm is presented to absolutely registration two 3-D sensors using targets that are tracked by both sensors. The algorithm estimates and removes sensor biases and sensor frame orientation errors, For illustrative purposes, the algorithm uses the difference in the position of a target, as reported by the two sensors, to compute a set of bias parameters. These bias parameters are then applied to align both sensorpsilas track positional data. The registration algorithm is applicable to those situations where there are no sensor location errors, the magnitude of the bias errors is small, and these errors do not change with time or very slowly with time.

An adaptive limited memory approach (ALMA) is proposed to deal with the data initial association problem in tracking-while-scanning systems in high clutter environments. By using measurements in the window limited memory and exploiting the target detection probability and receiver noise sampling information, the initial track number is estimated. It in turn can be adopted to adjust the initial correlation region and the normalized residual square threshold adaptively; many false initial trajectories are therefore restrained. Computer simulation demonstrates that the real time data initial association problem can be settled by a combination of radar's dynamic characteristics and limited window measurements.

A novel approach is proposed, which is able to overcome several shortcomings existing in the typical distributed multi-sensor track association (MSTA) model-the sequential minimum normalized distance nearest neighbor (SMNDNN) correlation and the sequential minimum mean square error (SMMSE) fusion. By considering the mutual dependency in the track estimation errors between different sensors and using the window cumulative normalized distance (WCND) technique, this phase optimization algorithm can guarantee stability in dealing with track association, especially in a dense track environment. The experiments demonstrate that our model can efficiently resolve the MSTA, the ad hoc dense track association problem.