In this article, we consider indoor positioning of a shopping cart in a store with a hybrid approach combining ultra wideband (UWB) and a people dead reckoning (PDR) system. While UWB can provide a very accurate positioning estimate in ideal circumstances, its accuracy reduces in non-line-of-sight situations and the update rate decreases when the number of users increases. To solve these issues, each shopping cart is not only equipped with a UWB tag, but also with an inertial measurement unit (IMU) sensor to determine the step length and heading of the user moving the shopping cart, in order to track the shopping cart in between two UWB measurements. As the IMU is not attached to the body, the measured acceleration will be different than in other works considering PDR systems. In this article, we therefore first extract a model for the acceleration, and use the resulting model in the PDR system, where we look for the best acceleration component to track the cart. To combine the PDR and UWB information, we consider two approaches, i.e. Kalman and particle filtering, and compare both approaches. Moreover, we investigate the effect of the presence of map information of the store on the trajectory information. Our experiments show that the average positioning error using UWB only equals 62.6 cm, while the Kalman and particle filter result in an accuracy of respectively 34.1 cm and 41.3 cm, and when using map information in combination with particle filtering, the accuracy improves to 28.0 cm.