A new algorithm for general asynchronous sensor bias estimation in multisensor-multitarget systems

Abstract

Errors due to sensor bias are often present in sensor data and can reduce the tracking accuracy and stability of multi-sensor systems. The other practical problem is that the target data reported by the sensors are usually not time-coincident or synchronous due to the different data. This paper deals with these problems and presents a new algorithm for estimation of both constant and dynamic biases in asynchronous multisensor systems. We use the measurements from asynchronous sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. This algorithm is a Kalman filter based technique to estimate both the range and offset biases and is implemented recursively which is computationally efficient and provided real time estimation of asynchronous sensor bias. The Simulation results show the Cramer-Rao Lower Bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient.

Track fusion in the presence of sensor biases

“…In this work, it is assumed that local tracks from different sensors are synchronous. In many practical applications, sensor reports are usually not time‐coincident because of different data rates of local sensors.Hence, it is meaningful to implement bias estimation and track fusion to the case of asynchronous sensors [20, 21]. The key point is, based on asynchronous local tracks, to establish the pseudo‐measurement equation of the bias vector (including the asynchronous pseudo‐measurement, the asynchronous pseudo‐measurement matrix, the asynchronous bias vector and pseudo‐measurement noise), and evaluate the statistical property of the pseudo‐measurement noise.…”

Generic multisensor multitarget bias estimation architecture

“…It can be accomplished by extrapolation of pairs of measurements to a common time reference and posterior differentiation (see, for instance, [2,6]), by the procedure described in this paper or by other techniques (e.g. such as definition of pseudo measures from combinations of measures [4,5,10] or parallel target state and bias estimation [3,7,9]). In general, independently of the method applied, an observation of the bias terms is obtained from each measure or group of measures coming from the involved sensors.…”

“…[2][3][4][5][6][7][8][9][10][11][12]). The basic idea is estimating every bias terms in the measurements potentially causing consistency mismatch, and removing them from raw measures, providing the tracking filters with bias-corrected (mostly unbiased) measures.…”

Asynchronous multi-sensor bias estimation with sensor location uncertainty

“…In 2005, X. Lin extended his work to the asynchronous sensors and considered the uncertainty of the sensor position by the assumption that n-1 measurement come from the first sensor and the last one comes from the second sensor [7]. Based on his work, in 2006, A. Rafti [8] proposed a new algorithm with the assumption that the present measurements must be fused with the last previous measurements from both sensors.…”